CLIENT PROJECT REPORT CPR4037 - TfL Consultations · 2017-11-15 · CLIENT PROJECT REPORT CPR4037 A Direct Vision Standard for HGVs Casualty Impact Analysis for Proposed Implementation

CLIENT PROJECT REPORT CPR4037

A Direct Vision Standard for HGVs Casualty Impact Analysis for Proposed Implementation in London

Iain Knight, Alex Livadeas, Alix Edwards, Rahul Khatry, Martin Dodd, Phil Martin

Version 4.0 CPR4037

Report details

Report prepared for: Transport for London (TfL)

Project/customer reference: tfl_scp_001653_co036

Copyright: © TRL Limited

Report date: 04/10/2017

Report status/version: Version 4.0

Quality approval:

Courtney Newbould

(Project Manager)

Phil Martin

(Technical Reviewer)

Disclaimer

This report has been produced by TRL Limited (TRL) under a contract with Transport for London (TfL). Any views expressed in this report are not necessarily those of Transport for London (TfL).

The information contained herein is the property of TRL Limited and does not necessarily reflect the views or policies of the customer for whom this report was prepared. Whilst every effort has been made to ensure that the matter presented in this report is relevant, accurate and up-to-date, TRL Limited cannot accept any liability for any error or omission, or reliance on part or all of the content in another context.

When purchased in hard copy, this publication is printed on paper that is FSC (Forest Stewardship Council) and TCF (Totally Chlorine Free) registered.

Contents amendment record

This report has been amended and issued as follows:

Version Date Description Editor Technical Reviewer

0.1 July 2017 Initial draft

1.0 25/08/2017 First draft for methodologies A Edwards Phil Martin

2.0 05/09/2017 Draft final report for TfL review I Knight Phil Martin

3.0 15/09/2017 Update in response to feedback & Final internal review

I Knight, A Livadeas

Matt Seidl

4.0 04/10/2017 Update in response to final feedback I Knight Phil Martin

Document last saved on: 04/10/2017 12:32

Document last saved by: Phil Martin

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Executive Summary

The Mayor of London is adopting a ‘Vision Zero’ approach to road danger reduction based on the view that no loss of life is inevitable or acceptable. Heavy goods vehicles (HGVs) are disproportionately involved in collisions involving vulnerable road users (VRUs) in London and blind spots have been identified as a significant contributory factor in certain types of those collisions. For this reason, Transport for London (TfL) have developed a direct vision standard (referred to simply as ‘The Standard’) to measure the quality of vision from HGVs and London’s Mayor proposed using it to ban or restrict those HGVs with the poorest vision (rated 0-star) by 2020 and to permit only vehicles with a ‘good’ level of direct vision (defined as 3-star or above) by 2024. This report describes work commissioned by TfL to quantify the casualties affected by the measure as part of their integrated impact assessment of the proposed policy. Research involving Loughborough Design School (LDS) and Jacobs also contributed to the Integrated Impact Analysis IIA and considerable collaboration between all parties has been required.

The main elements of work have been to:

Converting ratings from ‘The Standard’, provided by LDS, for individual HGV makes and models to estimates of numbers of those vehicles in different data sets.

Testing the likely effect of different options, for example, different geographies, times of day, or complementary measures.

Combining data from Jacobs with ratings data from ‘The Standard’ for vehicles to forecast how the composition of the HGV parc would change in response to different policy options.

Identifying evidence of the likely effectiveness of direct vision in preventing casualties.

Forecasting the combined effects of each policy option over a period of time to quantify the casualty impacts of the measure.

The main findings were that:

1) Data from collisions involving HGVs and VRUs where direct vision might be relevant (i.e. excluding rear) show that blind spots are most commonly a factor when the vehicle moves off from rest and the collision is at the front or when the HGV turns left and collides with a VRU at the nearside.

2) This type of collision produced between 4 and 6 fatalities per year on average (2011 to 2015). This was a small proportion of all collisions in London but a large proportion of all GB cases involving the same manoeuvre. Similar problems exist across the EU.

3) Limitations in the data severely restrict the ability to produce a definitive estimate of the effects, and the consequent uncertainty produces a wide range of predicted effects. In particular, a lack of information on cab height and poor completion of make and model information for HGVs in registration data limit the ability to accurately map ratings from ‘The Standard’ to vehicle population in traffic or collisions. Substantial discrepancies in description of HGV type between STATS19 collision data and Driver and Vehicle

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Licensing Agency (DVLA) registration data undermine confidence in the accuracy of at least one of those sources of vehicle category information.

4) Vehicles that have been rated in accordance with ‘The Standard’ represent around 75% of new HGV > 12t sales in GB. Assuming these were representative of all HGVs > 12t, 0-star vehicles represented between 27% and 83% of the London parc, those less than 3-star between 76% and 96%. Parallel IIA activities at an early stage highlighted that the costs for an ‘outright restriction’ would likely be orders of magnitude higher than benefits. This led TfL to explore alternative approaches, resulting in the following options for casualty analysis:

a) Encouraging voluntary uptake of improved solutions (‘do minimum’),

b) An ‘outright restriction’ as originally planned.

c) Introducing a new ‘HGV safe system scheme’ that required vehicles without excellent direct vision to demonstrate that they had sufficient alternative measures in place to compensate for deficiencies in direct vision..

5) The ‘do minimum’ scenario where TfL use ‘The Standard’ in voluntary best practice schemes (e.g. FORS, CLOCS) and in commercial contracts, was considered the baseline against which other options were measured (doing nothing was not considered an option by TfL). This alone was predicted to have significant benefits compared to doing nothing, potentially preventing a total of 2 to 9 fatalities and 1 to 9 serious casualties by 2030 at a casualty prevention value of between approximately £5m and £27m.

6) The two policy options were measured against the ‘do minimum’ option and their benefits are, therefore, in addition to those of the ‘do minimum’ option.

a) An ‘outright restriction’ would likely prevent a total of 4 to 18 fatalities and 2 to 16 serious casualties by 2030, at a value of £11m to £48m

b) An ‘HGV safe system scheme’ was estimated to prevent 2 to 15 fatalities and 1 to 15 serious casualties by 2030, at a value of £6m to £41m.

7) The benefits of an outright restriction come early 2020/24 but do not improve over time, in line with an assumed compliance culture. The safe system approach had less effect early in the period where some of the most promising alternative technologies are relatively immature but improves over time as technology improves and a best practice culture takes hold, such that the annual benefit in 2030 is predicted to exceed that of the outright restriction.

8) These benefits were based on the most robust methodology available. A narrower range of results was also considered, based on only a small sample of vehicles measured by LDS where data was available on the height at which each given vehicle was most often sold. For comparison, this suggested a monetised benefit for the outright restriction option of £18m to £43m and for the safe systems scheme of £6m to £40m.

9) The benefits of either policy option above, calculated by either methodology, are additive to the benefits of ‘doing minimum’. Doing minimum will result in casualty reductions compared with a continuation of the casualty trends of the past, which more closely represent a ‘do nothing’ scenario.

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Contents

1 Introduction 1

2 Quantifying the problem 3

2.1 VRU casualties within the wider context of London’s road safety 3

2.2 Analysis of VRU fatalities involving goods vehicles 5

3 Identifying the target population 7

3.1 Identification by HGV size, manoeuvre and impact point 7

3.2 Identification by blind spot 9

3.3 Identification by body type/configuration 10

3.4 Consideration of under-reporting 12

3.5 Conclusions and quantification 13

4 Comparing London with other regions 15

4.1 What to compare 15

4.2 Comparisons 16

5 Applying vision ratings to the HGV parc 21

5.1 Methods 21

5.2 GB market share 27

5.3 London market share (vehicles) 28

6 Collision risks by geography 30

6.1 Methods 30

6.2 Results 31

7 Collision risks by time of day 41

7.1 HGV-pedestrian collisions 41

7.2 HGV-pedal cyclist collisions 42

7.3 Analysis 43

8 Definition of detailed policy options for assessment 45

9 Valuing the prevention of casualties 46

9.1 Casualty valuation 46

9.2 Congestion valuation 46

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9.3 Emissions valuation 49

10 Effectiveness of solutions 53

10.1 Retrospective statistical approach 53

10.2 Predictive engineering approach 56

10.3 Modelling Effectiveness 60

11 Forecasting the future casualty effects of the policy options 63

11.1 Overview of the model 63

11.2 Baseline scenario “Do Nothing” 63

11.3 Baseline scenario 2 “Do Minimum” 67

11.4 Policy Option 1 “Outright restriction” 68

11.5 Policy Option 5 “HGV safe system scheme” 70

11.6 Overall results 70

12 Un-monetised risks 74

12.1 Consideration of uncertain collision types 74

12.2 Consideration of growth in cycling and walking 74

12.3 The assumption that ‘the market will provide’ 75

12.4 The effect of TfL’s policies on other cities/regions. 75

12.5 Safety dis-benefits in inter-urban highway traffic 76

13 Conclusions 77

14 References 79

Appendix A Selected STATS19 field definitions 81

Appendix B Comparing London with the UK 82

Appendix C Data on HGV manoeuvre, impact point and blind spots 86

Appendix D Location of injuries – pedestrians and pedal cyclists 91

Appendix E Time of injuries – pedestrians and pedal cyclists 99

Appendix F Calculation of collision values - example 103

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1 Introduction

The Mayor of London is adopting a ‘Vision Zero’ approach to road danger reduction based on the view that no loss of life is inevitable or acceptable. Previous research had identified that heavy goods vehicles (HGVs) were substantially over-involved in fatal collisions with vulnerable road users (VRUs) and that in certain close proximity manoeuvres, blind spots appeared to be a contributory factor with a large proportion of the vehicles being of ‘off road’ specification and relatively high from the ground (Delmonte et al., 2013). Studies of the field of view through the windscreen of HGVs, direct vision, suggested that the direct vision was poorer from this type of vehicle (Summerskill et al., 2015). A formal method of assessing and rating the quality of direct vision from HGVs was developed (Robinson et al., 2016): The direct vision standard (hereafter referred to simply as ‘The Standard’). In response to these findings, London’s Mayor proposed1 using the standard to ban or restrict those HGVs with the poorest vision (rated zero star) by 2020 and to permit only vehicles with a ‘good’ level of direct vision (defined as 3-star or above) by 2024.

Transport for London (TfL) has therefore undertaken to produce an integrated impact assessment of the proposed policy. TRL were commissioned to undertake an expert analysis of the casualty impact of the proposed policy to inform the wider impact assessment undertaken by TfL and which was contributed to by other research and consultancy organisations. Loughborough Design School (LDS) have been responsible for measuring the field of view from a large sample of vehicles representing the majority of those new vehicles in excess of 12 tonnes sold in the UK and refining the standard (and particularly its rating boundaries) in light of the findings. Jacobs have been responsible for assessing the business impacts on industry. The research undertaken by TRL has, therefore, involved considerable interaction with the work undertaken in parallel by these other organisations.

The main elements of work have been to:

Establish methods of converting ratings from ‘The Standard’, provided by LDS for individual HGV makes and models, to estimates of the proportion of the London HGV parc that fall into each vision category and would, therefore, be affected by the proposal

Seek information to quantify the scope of likely effects that could be achieved with a range of different policy options relating, for example, to different geographies or times for a ban as well as considering alternative or complementary measures.

Combine data from Jacobs with vision ratings from ‘The Standard’ to forecast how the composition of the HGV parc would change in response to different policy options.

Identify evidence of the likely effectiveness of direct vision in preventing casualties.

1 https://www.london.gov.uk/press-releases/mayoral/new-measures-to-rid-london-of-dangerous-lorries

https://www.london.gov.uk/press-releases/mayoral/new-measures-to-rid-london-of-dangerous-lorries

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Forecast the combined effects of each policy option over a period of time to quantify the casualty impacts of the measure.

The work has involved analysis of a wide range of data sources relating to vehicle population, exposure to risk and collisions and the development of a bespoke spreadsheet model that calculates the effects of implementing the various policy options. This is the final report that describes all analyses in full.

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2 Quantifying the problem

The standard is intended to reduce the risk of VRUs being killed or injured in collisions where they were moving in close proximity to goods vehicles carrying out low-speed, forward manoeuvres. The generally accepted definition of a VRU is adopted in this report (i.e. pedestrians, pedal cyclist and motorcyclists) and the risk to these three groups has been a focus of efforts to improve road safety in London for some time. The particular risk to VRUs is recognised, for example, in TfL’s 2013 road casualty reduction plan (TfL, 2013).

Goods vehicles are categorised according to their maximum gross vehicle weight2 (MGW) and for the purposes of this report they have been defined as3:

Light goods vehicle (LGV) – less than 3.5 tonnes MGW

Medium goods vehicle (MGV) – 3.5-7.5 tonnes MGW

Heavy goods vehicle (HGV) – over 7.5 tonnes MGW

Guidance on variants (e.g. axle configurations) is given in the DVSA document ‘Lorry types and weights guide’4. The analysis here is confined to MGVs and HGVs, and where HGVs alone are considered, this is stated.

Analysis of collisions and casualties in London between 2011 and 2015 has been conducted using the STATS19 dataset. In addition to the publicly available STATS19 data5, TRL has access to the ‘enhanced’ dataset, which links to DVLA vehicle information via registration numbers. This has made details held by the DVLA available to be cross-referenced and used for detailed categorisation of vehicles (e.g. make and model) and allowed for the analysis and predictions of the effects of the standard to take place.

2.1 VRU casualties within the wider context of London’s road safety

During the period 2011-2015, between 129 and 159 people were killed on London’s roads each year6, with the number of fatalities staying fairly constant from 2012. This is illustrated in Figure 2-1:

2 In STATS19 this is referred to as gross vehicle weight (GVW).

3 Note that many different categorisation systems exist, for example, in type approval goods vehicles are

classified as light below 3.5 tonnes and heavy above 3.5 tonnes and there is an additional sub-division at 12

tonnes. For driver licensing purposes, any goods vehicle in excess of 3.5 tonnes is referred to as a Large Goods

Vehicle or LGV. Many regulations refer to maximum authorised mass or maximum permitted mass. The

categories chosen here are based on the definitions used in the STATS19 database,

4 https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/211948/simplified-guide-

to-lorry-types-and-weights.pdf

5 https://data.gov.uk/dataset/road-accidents-safety-data

6 https://www.gov.uk/government/statistical-data-sets/ras30-reported-casualties-in-road-accidents#table-

ras30043

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Figure 2-1: London road user fatalities 2011-2015. Source: Analysis of STATS19 database.

The significance of HGVs in these figures is shown in Table 2-1, in which it can be seen that the proportion of fatalities is high, particularly for pedestrians:

Fatal injury Serious injury Slight injury Total injuries % fatalities

Pedestrian 35 57 127 219 16.0

Pedal cyclist 18 40 210 268 6.7

Motorcyclist 4 27 148 179 2.2

Table 2-1: London VRU fatalities 2011-2015 involving HGVs. Source: Analysis of STATS19 database.

Analysis of Department for Transport (DfT) data in Table RAS300437 shows that the corresponding VRU fatality proportions for all types of vehicle injuries (i.e. not just from HGVs) is 1.3, 0.3 and 0.6% respectively for pedestrians, pedal cyclists and motorcyclists. So, for example, a pedestrian injured in a collision with an HGV is more than 12 times more likely to be killed than in a collision with another type of vehicle.

As shown in, Figure 2-2, the proportion of VRUs in these fatality numbers has remained at around 80%, with pedestrians making up around half of the total.

7 https://www.gov.uk/government/statistical-data-sets/ras30-reported-casualties-in-road-accidents#table-

ras30043

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Figure 2-2: VRU fatalities as a proportion of all London fatalities. Source: Analysis of STATS19 database.

2.2 Analysis of VRU fatalities involving goods vehicles

Between 2011 and 2015 there were a total of 80 VRU fatalities in collisions in London where goods vehicles were involved. These are detailed Figure 2-3 below. This data suggests an upward trend in overall fatalities, driven by an increase in the numbers of pedestrian casualties. However, correlation is very low, suggesting that the trend explains only a small percentage of the variation seen, such that it is difficult to conclude that this is not merely a function of random chance. For example, the pattern could also represent random fluctuation around a constant average.

Figure 2-3: Trends in VRU fatalities involving goods vehicles in London 2011-2015. Source: Analysis of STATS19 database.

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Although the overall trend is for the number of pedal cyclist and motorcyclist casualties to remain constant, there are significant differences from year to year. Pedal cyclist fatalities vary between a minimum of 2 and a maximum of 7 per annum; the corresponding figures for motorcyclists are 1 and 2, therefore a single additional fatality may double the casualty rate. As absolute values are small, statistical analysis and prediction should be considered with this in mind.

Counts carried out by the Department for Transport (DfT)8 between 2011 and 2015 show that heavy goods vehicles made up an average of 4% of London motor vehicle traffic between 2011 and 2015. Pedal and motorcyclists each accounted for less than 3% of motor vehicle traffic (although pedal cycles are not counted as such, the figure given here is a comparison with numbers of motor vehicles). When considering the proportion of fatalities involving goods vehicles, there is again a lot of variation, some of which is due to the small numbers involved.

The average and median proportion of all VRU fatalities in London caused by goods vehicles are 19% and 15% respectively (Figure 2-4). This is an over-representation relative to the volume of traffic (4% of London traffic involves goods vehicles in excess of 3.5 tonnes). The difference is more pronounced for pedal cyclists, where an average of 39% of fatalities involved goods vehicles. Despite the variation and the relatively great significance of a small change in the number of fatalities, the effect of goods vehicles on VRUs in London does appear to be disproportionately large when compared to their (and VRU) numbers on the roads of the capital, particularly for pedal cyclists and pedestrians.

Figure 2-4: Proportion of each type of VRU fatality in London that involved goods vehicles. Source: Analysis of STATS19 database.

8 https://www.dft.gov.uk/traffic-counts/area.php?region=London

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3 Identifying the target population

3.1 Identification by HGV size, manoeuvre and impact point

The majority of literature regarding manoeuvring of HGVs and collisions with VRUs where blind spots are thought to be a contributory factor, has identified a few key manoeuvres (see for example, Schreck and Seiniger (2014) and Robinson et al. (2016)). Most commonly a left turn (right turn in mainland Europe) is identified, but also reversing and moving off from rest. Reversing has not been considered to be within the scope of direct vision and has not, therefore, been considered in detail here.

London collision data from STATS19 has been examined to identify the frequency with which different VRUs have been injured in collisions with HGVs engaged in different manoeuvres and with different impact points. Table 3-1 below shows the involvement of different HGV sizes in fatal collisions.

Table 3-1: The frequency of relevant fatalities by size of goods vehicle

All goods vehicles (>3.5t)

HGV >7.5t

All pedestrians 49 35

Pedestrians – HGV moving off + front impact 15 12

All pedal cyclists 24 18

Pedal cyclists – HGV turning left + nearside impact 13 9

All motorcyclists 7 4

Motorcyclists – HGV moving off + front impact 2 0

Motorcyclists – HGV turning left + nearside impact 1 0

It can be seen that most fatalities involve HGVs in excess of 7.5t. This has been observed in previous research where the traffic volumes of different goods vehicle sizes was also considered and further highlighted the over involvement of larger vehicles (Robinson et al., 2016). This led to the proposal that ‘The Standard’ should only to larger vehicles. The mass threshold actually chosen by TfL was 12 tonnes to coincide with EU type approval definitions of larger HGVs (category N3). STATS19 does not divide vehicles at 12 tonnes such that there is a slight mis-match between the casualty numbers assessed here and the actual policy. However, only a relatively small proportion of GB HGVs will have a maximum mass between 7.5 and 12 tonnes, so this difference is likely to be very small.

The subsequently more detailed analyses of impact point and manoeuvre focussed on the goods vehicles in excess of 7.5 tonnes. An example of the manoeuvre breakdowns for fatally injured pedestrians and pedal cyclists is shown in Table 3-2 and Table 3-3 below, and full details including motorcyclists and other injury severities can be found in Appendix C.

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Table 3-2: Pedestrian fatalities by HGV manoeuvre and impact point, London 2011-2015

Pedestrian fatal injury incidence 1st point of impact

Vehicle Manoeuvre None Front Back Offside Nearside

Reversing 0 0 0 0 1

Slowing or stopping 0 2 0 0 0

Moving off 0 12 1 0 4

Turning left 0 1 0 0 2

Overtaking static vehicle - offside 0 1 0 0 0

Going ahead other 1 6 0 1 3

Total 1 22 1 1 10

It can be seen that pedestrians killed by HGVs moving off from rest and where the impact point is at the front of the vehicle is the largest group of pedestrian fatalities. It should be noted that earlier data analysed by Robinson et al. (2016) for the years 2005-14 found that “moving off, hit at front” and “going ahead other, hit at front” were equal at a total of 21 fatalities each. This suggests the frequency of the going ahead other collisions have reduced substantially in more recent years while the moving off collisions have stayed more constant, though the low number each year makes a trend very difficult to observe.

Table 3-3: Pedal cycle fatalities by HGV manoeuvre and impact point, London 2011-2015

Cyclist fatal injury incidence 1st point of impact




Turning right 0 0 0 1 0

Going ahead left-hand bend 0 0 0 0 2

Going ahead right-hand bend 0 1 0 0 0


Total 0 1 0 1 16

It can be seen that for pedal cyclists the biggest group of fatalities occur when the cyclists hits the nearside of an HGV turning left. Overall, the vast majority occur at the nearside, some of the additional ones occurring when moving off from rest or going ahead, either in a straight line or around a left hand bend. Only one fatality occurred at the offside, though in

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a small sample, this represents 6% of the fatalities and 11% of those that occurred when an HGV was turning.

Table F3 in Appendix C (from which Tables 3-2 and 3-3 are extracted), shows that a much lower figure of 3 motorcyclist fatalities occur, and that none of them are coded with the ‘moving off / front’ or ‘turning left / nearside’ manoeuvres.

Additionally, the STATS19 analysis also shows that of the 80 VRU fatalities from collisions involving goods vehicles (>3.5t), only 1 involved a left-hand drive HGV. Although the overall exposure of left-hand drive vehicles in London is not known, this does not suggest that they represent a major part of the VRU collision problem.

3.2 Identification by blind spot

Most collision analyses use impact point and manoeuvre as a proxy for blind spot collisions, because no further data is available. However, STATS19 does contain information on factors that contribute to the cause of the collision and one of those is recorded as a vehicle blind spot. Thus, this should be a more accurate quantification of the problem. In the most recent analysis supporting development of the direct vision standard, Loughborough Design school analysed VRU casualties from collisions where the HGV involved was coded with blind spot as a contributory factor (Summerskill et al., 2017), without relying on the vehicle manoeuvre, provided the vehicle was not hit in rear which is not amenable to direct vision improvement.

However, the contributory factors system in STATS19 relies on the reporting police officer completing the form within a few days of the collision. The reporting officer is not usually technically trained in collision investigation and the record is made before any in-depth collision investigation has been undertaken. With relatively technical contributory factors, such as assessing whether a VRU should have been visible either through a glazed area or mirror in a highly dynamic manoeuvre is quite difficult and there is considerable potential for an officer untrained in these analyses to judge incorrectly. Thus, this method could be potentially either under or over-report the problem or perhaps even be misleading as to the type of manoeuvre it might have occurred in. The above notwithstanding, Table 3-4 below compares the numbers of collisions where blind spot was coded.

Table 3-4: VRU fatalities from collisions involving HGVs where blind spot was coded as a contributory factor

All contributory factors Blind spot only Proportion (%)

Pedestrian 35 17 49

Pedal cyclist 18 8 40

Motorcyclist 3 2 67

Further analysis in Table 3-5 shows that most, but not all, “left turn, hit at nearside” and “moving off, hit at front” scenarios are associated with collisions where blind spot was cited as a contributing factor

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Table 3-5: VRU fatalities from collisions involving the nearside of a left turning or front of a moving off HGV, by presence of blind spot as a contributory factor.

All contributory factors Blind spot only

Pedestrian 12 10

Pedal cyclist 9 7

It can be seen that restricting the manoeuvres to those where engineering analysis and in-depth studies have suggested blind spots are a problem (Robinson et al., 2016) does not much change the number of pedal cycle fatalities, 8 blind spot collisions when all manoeuvres are considered (Table 3-4), 7 when blind spot is coded and manoeuvres are restricted to moving off, hit at front and turning left, hit at nearside (Table 3-5). When only the manoeuvre is considered with all contributory factors there are 9 fatalities. This shows that for pedal cycles the difference between identification of target collisions by manoeuvre and by blind spot contributory factor is small; they identify almost exactly the same collisions.

However, the same comparisons for pedestrians show a total of 17 blind spot fatalities reduce to only 10 when restricted to the identified manoeuvres. The other 7 fatalities occur as follows:

Reversing / nearside : 1

Moving off / nearside: 4

Turning left / nearside: 1

Turning left / front: 1

Reversing is out of scope of direct vision, turning left and being hit at the nearside can be captured within the target population by not restricting each manoeuvre specifically to one type of VRU. However, the remaining two mechanisms are somewhat ambiguous. It is possible that they are examples of interpretation in the police coding of collisions, for example, the impact in all cases is at the nearside front corner and in the left turn they have called this front and in the moving off collisions they have called it nearside. However, particularly for the moving off collisions, they could genuinely be other types of collision that are not in scope of direct vision. For example, HGVs do suffer a range of unusual incidents where, for example, a driver gets into a parked vehicle and moves off from rest not realising that somebody is, for example, working underneath the vehicle, sleeping under the vehicle or working on the load of the vehicle. If they were run over at, or fell at, the nearside this could be a perfectly valid code combination.

3.3 Identification by body type/configuration

Previous research has shown that certain types of HGV appear to be over-represented in collisions between pedal cycles and HGVs turning left (Delmonte et al., 2013). The possibility of dividing target populations by specific body types and configurations was therefore also investigated. However, analysis of the enhanced STATS19 data for this report has shown

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that a significant amount of miscoding of vehicle make, model and body type is present. For example, Figure 3-1 shows deficiencies in coding make and model of goods vehicles in the entire 2011-2015 data sample used.

Figure 3-1: STATS19 errors in coding make/model

Whilst only a small proportion of vehicles were coded entirely correctly (16%), only 3% were coded entirely incorrectly, with the remainder being either not coded at all or having only partial information about the make and model. An example of such incorrect coding is shown in Table 3-6, where a selection of records is shown which are coded as goods vehicles (type 20 or 21) but with makes and models which show motorcycles or passenger cars.

Table 3-6: Examples of obvious miscoding in the enhanced STATS19 data

Vehicle type

Towing and Articulation

Make Model GVW Body type

Wheelplan

20 0 HONDA CBR 125 RW-7 0 18 A

21 0 HONDA FJS 600 0 18 A

20 0 HONDA JAZZ HS IMA CVT 1258 14 C

20 0 HONDA XR 125 L - 4 0 18 A

20 0 HYUNDAI I10 ACTIVE AUTO 1015 14 C

20 0 HYUNDAI I20 COMFORT 1045 14 C

It is important to note that the vehicle type (20 or 21) is recorded by the police officer attending the collision and the make, model, GVW and wheelplan all come from the DVLA database that is linked in to form the enhanced data. It is impossible to identify whether the error comes from the police officer miscoding the vehicle type or whether the vehicle type is reliable and the error comes perhaps from writing down the registration number incorrectly,

26%

3%

55%

16% No make/model

Wrong make/model

Incompletemake/model

Correct make/model

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or it being wrongly transcribed during data entry, such that the collision links to the wrong DVLA vehicle record.

Analysing the body type of vehicles involved in collisions where coding was considered to be of sufficient quality, shows that “Tippers” were most commonly involved in collisions where pedal cycles were killed during left turns and “Tractor units” were most commonly involved in collisions where pedestrians were killed when moving off from rest.

Figure 3-2: Turning left and moving off collisions by HGV body type involved.

However, further analysis shows that, of 490 goods vehicles in the entire London STATS19 sample that were coded as ‘tractor’ (based on DVLA records), 307 were at the same time coded as ’no tow or articulation’ (based on police officer interpretation at the scene). Except in the small proportion of cases where a tractor unit was not towing a semi-trailer, this is an inconsistent result and significantly undermines confidence in any conclusions drawn from the body type data and assessment of whether the vehicle is towing or articulated.

It should also be noted that the assignment of vision ratings in accordance with ‘The Standard’ to collision data records in STATS19 was achieved via this same linked DVLA data and body type fields. This substantial discrepancy in the coding of the same vehicle in different sources therefore also substantially undermines confidence in the accuracy of the vision ratings assigned to vehicles in collisions.

3.4 Consideration of under-reporting

Internal, unpublished research by TfL has identified further miscoding that can be considered as under-reporting. TfL staff have explained to TRL that by reviewing information from a number of sources (e.g. press reports, correspondence with witnesses and truck companies involved) where they were made aware of high profile collisions and comparing these with STATS19, they identified a number of instances where an HGV was involved but

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not coded as an HGV (typically a construction-bodied HGV coded as an ‘other vehicle’). A two-year sample of these data, supplied by TfL to TRL, was analysed to compare the number of HGVs and other vehicles involved in collisions with the relevant conflicts and this work has resulted in the derivation of correction factors of +21% for pedestrian and +30% for pedal cyclist collisions that would allow this under-reporting to be accounted for.

However, it is not known to what extent the TfL analysis managed to exclude any cases where a vehicle that was not an HGV was mistakenly coded as an HGV. It does, therefore, remain possible that this is an overestimate or that the uplift factors generated from a small sample of collisions over two years is fully representative of the average pattern over a longer period.

In addition to this, it is widely recognised that STATS19 suffers a more general element of under-reporting. This is thought to be minimal for fatal collisions, but much more prevalent in low severity collisions that may not initially have been reported to the police or where the police may not have attended the collision. An uplift value of 2% was therefore applied to correct for the underreporting of road collisions. This value was based on recommendations from the European HEATCO project (IER, 2006), with the uplift value associated with fatal casualties applied only. This was due to the authors’ view that the majority of HGV collisions resulting in an injury to a VRU would be attended by the police and emergency services due to the perceived severity of VRU collisions with HGVs. Uplift values for HGV collisions involving seriously and slightly injured pedestrians, cyclists and motorcyclists was therefore assumed to be equivalent to that for fatal collisions (i.e. 2%).

3.5 Conclusions and quantification

It can be seen that there is substantial uncertainty inherent in the quantification of the most relevant population of collisions that might be avoided by improved direct vision. This is particularly true when considering analyses by body type and for this reason, separating populations by body type was excluded from consideration. For the remainder of factors it was considered that the most appropriate approach was to create a range reflecting the extent of uncertainty. So, the target population was defined as all pedestrians, pedal cyclists or motorcyclists injured in collision with an HGV >7.5t with the following additional criteria:

Upper estimate

o HGV turning left, impact point at nearside

o HGV moving off from rest, impact point at front

o Misreporting uplift of 21% for pedestrians and 30% for pedal cyclist and motorcycle casualties

o Under-reporting uplift of 2% for all severities

Lower estimate

o HGV turning left, impact point at nearside, and blind spot contributory factor

o HGV moving off from rest, impact point at front, and blind spot contributory factor

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These criteria result in the following baseline casualty numbers for London, noting that where the uplift factors resulted in fractions of casualties, source numbers at full precision were used in all calculations but in presentation all numbers have been rounded to the nearest whole number.

Table 3-7: Baseline target population numbers relating to VRU casualties from collisions involving HGVs in direct vision relevant low-speed manoeuvres within London

Acc_Year Upper Lower

Fatal Serious Slight Fatal Serious Slight

2011 9 9 26 7 3 8

2012 4 3 26 3 1 12

2013 5 12 33 3 5 7

2014 4 1 23 1 0 8

2015 8 12 33 4 6 9

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4 Comparing London with other regions

4.1 What to compare

London has been assessed against other parts of the United Kingdom in terms of road casualties in order to determine whether the capital is unusual in this respect. A number of metrics were considered so that valid, meaningful, comparisons could be made, the starting point being the locations where TfL operates. These are the 32 London boroughs, the City of Westminster, and Heathrow Airport, an area corresponding to that of the Greater London Authority.

Research into this subject shows that there are numerous ways to define cities and administrative districts and where their boundaries lie (e.g. by population, population density, geographical area, building density, etc.) – see Appendix B for details. The decision here was ultimately dictated by the need to use reliable data provided by official bodies, such as the DfT or the Office for National Statistics (ONS), and therefore the comparisons system was aligned with that used by those organisations producing the statistical data.

4.1.1 Comparison options

Of the measures considered, EU metropolitan areas appear to be the least mixed in terms of transport policies, though there is some cross-over (e.g. in the ‘West Midlands urban area’). However, when considering the STATS19 areas that are coded within these conurbations, there are discrepancies of around 10% in geography. It is not possible therefore to compare data between these areas and those defined by STATS19.

Consequently, the system that has been used to draw comparisons between London and other parts of the UK is the metropolitan county definition. It should be noted that these are referred to as FMCs in UK government data, but that the areas covered are the same. The metropolitan counties and their sub-divisions are shown in Table 4-1, below.

Table 4-1: Definition of metropolitan counties

Metropolitan county Districts

Greater Manchester

City of Manchester, City of Salford, Bolton, Bury, Oldham, Rochdale, Stockport, Tameside, Trafford, Wigan

Merseyside

City of Liverpool, Knowsley, St Helens, Sefton, Wirral

South Yorkshire

City of Sheffield, Barnsley, Doncaster, Rotherham

Tyne and Wear

City of Newcastle upon Tyne, City of Sunderland, Gateshead, South Tyneside, North Tyneside

West Midlands

City of Birmingham, City of Coventry, City of Wolverhampton, Dudley, Sandwell, Solihull, Walsall

West Yorkshire

City of Leeds, City of Bradford, City of Wakefield, Calderdale, Kirklees

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4.2 Comparisons

4.2.1 United Kingdom comparison

Traffic data was obtained from DfT tables TRA02026 (goods vehicles and motorcycles) and TRA0413 (pedal cycles). Although TfL have supplied pedestrian traffic data (and additional pedal cycle data), no other metropolitan area was able to do so. Each local authority was contacted individually, but no meaningful pedestrian data was available.

Traffic data obtained was combined with DfT casualty data9 to calculate average casualty rates over the period 2006-2015, and these are illustrated in Figure 4-1 to Figure 4-4, below.

Figure 4-1: Rate of pedal cyclist casualties from collisions involving HGVs - London vs GB regions, 2006-2015

It can be seen that in terms of pedal cyclist casualty rates per billion cyclist kilometres, London is clearly a much higher risk than for GB as a whole and when compared to other cities, with only Manchester coming close to a comparable level of risk. The reasons for this are not known, but could potentially include differences in the infrastructure, in the level of HGV or VRU activity in the regions, the type of HGVs required to service different cities needs and/or the attitudes and behaviours of the road users involved.

This is not the case with respect to motorcyclists, where it is Manchester that has the stand-out high level of risk and London is more comparable to other cities and GB as a whole. Again, the reasons for this are unknown.

9 https://data.gov.uk/dataset/road-accidents-safety-data

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Figure 4-2: Motorcyclist HGV casualty rates - London vs GB regions 2006-2015

Figure 4-3: Pedal cyclist MGV casualty rates - London vs GB regions 2006-2015

Figure 4-4: Motorcyclist MGV casualty rates - London vs GB regions 2006-2016

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Again, some caution is required when viewing this information due to the statistical significance of the data. Casualty numbers are small and a single event can have a proportionately large effect. Traffic data is also published in billions of kilometres to an accuracy of one decimal place. Each of the main individual comparator cities used above have much lower levels of motorcycle traffic than London, which is expressed in the data either as 0.0 or 0.1 billion km/year. Thus, year on year variation in traffic for these cities becomes a binary 0 or 1 such that the rounding error could almost double the real traffic or reduce it to nothing. Even for London, which sees motorcycle traffic of between 0.6 and 0.9 billion km/year, the rounding to one decimal place gives a coarse estimate. The rounded data could change from, for example, 0.7 to 0.8 billion km, representing a change of 14% but the real change behind this could be from 0.7499 to 0.7501, an increase of just 0.03%. In other words, the rounding errors inherent in the data can be substantial.

4.2.2 European comparison

Across the EU, road fatalities fell between 2010 and 2015 (Adminaite et al., 2016) (Figure 4-5). The percentage change from 2011 is shown compared to that of London:

Figure 4-5: Change in total traffic fatalities relative to 2011 for London and EU. Source: Derived from (Adminaite et al., 2016) & analysis of STATS19 data

In Europe, HGV-related fatalities have been consistently falling since 200510, with a 14% drop between 2011 and 2014 (the latest year for which data is available), as illustrated in Figure 4-6. When compared to London, it can be seen that, although the overall trend may be similar, there are significant changes between years that suggests that London has not consistently enjoyed the same improvements. However, as the absolute numbers for London are small, increases of one or two fatalities (as occurred in 2012 and 2013) produce proportionally large changes so the ‘spike’ in this data can to some extent be regarded as

10 https://ec.europa.eu/transport/road_safety/sites/roadsafety/files/pdf/statistics/dacota/bfs2016_hgvs.pdf

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signal ‘noise’. Identification of any trend is, therefore, very difficult. The 5-year period presented suggests random fluctuation around a relatively constant average but must be treated extremely cautiously.

Figure 4-6: Change in total fatalities from collisions involving HGVs in London and EU, relative to a 2011 baseline. Source: STATS19 (2011-15) and (Adminaite et al., 2016) (2011-

2014)

The 2017 Volvo Trucks Safety Report reviewed a range of sources of collision data including CARE data relating to Europe, member states’ national data and in-depth samples including their own collision investigations (Volvo, 2017). Volvo found that around 20% of collisions involving VRUs injured in a collision with an HGV, involved the truck making a right turn (equivalent to left in the UK). The same report found that across the EU, around 5% of VRUs were killed or seriously injured in collisions involving the truck moving off from rest. This provides evidence that the problems seen in London do exist elsewhere, but it suggests that there is much more of a bias in the EU toward the nearside turn manoeuvre rather than the moving off manoeuvre.

Details of the right (left in UK) turn manoeuvre were examined by Schreck and Seiniger (2014), confirming that these problems existed in other EU cities. However, it also highlighted differences in the detailed collision mechanisms compared with collisions in London (Robinson et al., 2016). Robinson et al. (2016) found that fatal left turn collisions in London typically involved a rigid HGV stopping at lights before the left turn manoeuvre. The lateral separation in each case was not documented numerically but was typically expected to be small given the geometry of the roads on which the collisions occurred. By contrast, Schreck and Seiniger (2014) found that the collisions typically involved articulated vehicles that did not stop at traffic lights in the run up to the collision and that could be separated laterally from the pedal cycle before the collision by up to 4.5m.

One possible explanation of the differences relates to the difference in infrastructure design in different cities. London’s streets are often narrow and, where cycle lanes are present, they are often narrow strips at the side of the lane separated only by road markings. Schreck and Seiniger’s (2014) study used in-depth data from Berlin where it was stated that fully

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separated cycle lanes were quite common. Such lanes place the cyclist much further (laterally) from a truck on the main carriageway, but still need to cross side roads where the truck might turn.

Figure 4-7: Examples of cycle lane layouts, left in London and right in Berlin (Schreck and Seiniger, 2014)

One clear message from this is that although infrastructures may differ the same basic problem with turning HGVs and cyclists still occurs, even if the detailed mechanisms of the collision may differ. However, it is not known which situation, if any, is more representative of the EU as a whole and, therefore, which set of circumstances would best inform a pan-EU approach.

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5 Applying vision ratings to the HGV parc

5.1 Methods

5.1.1 Overview

The aim of the analyses described in this section was to assess how many vehicles entering London each year would fall into each of the standard star rating categories, in order to:

Inform the analysis of cost undertaken outside of this contract as part of TfL’s business impact assessment.

Quantify the exposure to risk and provide a mechanism by which the safety effect of future policy options can be easily modelled.

Where possible, quantify the number of collisions that might have involved different levels of direct vision performance.

This analysis proved very problematic for the following reasons:

Vision rating information, in accordance with ‘The Standard’ was supplied by make and model. In DVLA registration data for HGVs the model field is rarely properly completed such that no direct link can be reliably made.

Automatic Number Plate Recognition (ANPR) data on vehicles in London and STATS19 information on collisions rely on DVLA data to identify vehicle characteristics and so suffer the same limitation.

Alternative exposure data from the Continuing Survey of Road Goods Transport (CSRGT) does not contain information on make and model at all.

Society of Motor manufacturers and Traders (SMMT) data does contain reliable information on make and model. It relates to GB as a whole and shows that each model of HGV comes in a very wide range of different model variants, mass capacities and body types, often more than 100 permutations per model.

Loughborough Design School (LDS) supplied two to four measured ratings per model in most cases, one relating to the highest mounting height the model was sold at and one the lowest and in a few cases relating to the height at which the highest volume of trucks was sold. None of the population data available contained any information relating to cab mounting height and discussion with manufacturers revealed that there was no relationship between model variant code and cab mounting height.

For many models, the difference in direct vision performance associated with the full range of cab mounting heights was large. The most extreme example was that one model of HGV was 0-star at its highest but 3-star at its lowest. Several models ranged between 0- and 2-star.

As a consequence of these problems, estimates of the size of the fleet and exposure to risk by vision rating had to be made in wide ranges for GB and extended to London specific data sources using a proxy for make and model. When considering ranges, an ‘extreme’ range was defined where it was assumed that at one extreme all registered vehicles of a given

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make and model were sold at the lowest mounting height and, at the other extreme, all were sold at their highest mounting height. This method guarantees that the truth will lie between the two boundaries, but provides a wide range of results that is not very informative.

The small sample of vehicles for which information on the ‘most sold’ height was available, was used to estimate a ‘most sold’ height for N3 and N3G variants of models for which the information was not directly available. This was based on an assumption that the position of the most sold height within the range, relative to highest and lowest, was the same for the vehicles where it was not known as it was, on average, where it was known. This produces a narrower range that is more informative but is based only on a small sample and a simplistic estimating method, so there is a higher chance that the truth will lie outside of the quoted range.

To infer results for London, where make and model is essentially unknown, from those for GB, body type (e.g. tipper, tractor, box, etc.) was used as a proxy. The distribution (percentage) of vision rating for each body type was calculated based on the GB data and assumed to be the same in the London specific data (ANPR). A similar approach was used for the alternative London data from the CSRGT but body type was also not available so a combination of rigid/artic, and axle configuration was used. In both cases, the intention was to define proxies that best allowed the type of vehicles that had been identified in previous collision analyses, e.g. (Robinson et al., 2016), to be clearly identified.

More details of the data sources and the way they were combined are provided below.

5.1.2 Rating vehicles in accordance with ‘The Standard’

Data on measurements of the field of view of the current N3 HGV parc, and how this had been categorised into a 5-star rating, were provided by LDS. The final version of this data is illustrated in Figure 5-1, below, and numeric data was provided in relation to exact measured volumes of visible space and the threshold values for each rating.

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Figure 5-1: Illustration of baseline direct vision performance of Euro 6, category N2 HGVs. Source: Loughborough Design School.

While each manufacturer typically only has a small number of standardised cab designs, those cabs can be mounted at a variety of different heights from the ground. These different heights are chosen depending on the application (e.g. off-road use expected or not) and also end customer choices about tyre sizes, suspension types, etc. The height at which the cab is mounted can have a substantial influence on the final view achieved by each vehicle. For that reason, LDS quantified the direct vision performance of each model assessed at a range of different heights. As a minimum, the lowest and highest mounting height that each model could be sold in were assessed, but, in a few cases, interim assessments were made at the height which the manufacturer stated was most commonly sold either overall, or when separated for the on-road (N3) and off-road (N3G) specifications.

5.1.3 ANPR and DVLA data

The term ‘parc’11 refers to the total number of vehicles collectively. No data set currently exists that defines a ‘London’ parc of HGVs and by its very nature that parc can vary on a daily basis. Vehicles must be registered to an address and it is possible to identify those registered in London, but vehicles in London may be used anywhere and vehicles registered outside London may enter London. So, registration in London is a poor proxy, and can be complicated further by the fact that a number of large fleets will be headquartered in London, and will register all their vehicles there, but will base them at regional depots around the country.

11 HGV ‘parc’ is the term used throughout this document to define the total number of vehicles collectively.

The term ‘fleet’ referring to a company-owned fleet.

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Thus, the closest proxy is data that is collected by the network of Automatic Number Plate Recognition (ANPR) Cameras in London. These cameras record individual vehicles that are seen by this network of cameras on a daily basis and links to registration data to identify them and filter out repeat visits by the same vehicle. The camera network is not exhaustive and the density of cameras is greater in central London than it is in Greater London. This study used data covering Greater London. It is theoretically possible for a vehicle to enter Greater London without being registered by a camera, though it is considered likely that the number of unique vehicles missed over the course of a calendar year would be very low. It is considered the closest available proxy for a ‘London Parc’ and has, therefore, been used as the benchmark data set for this analysis. The data itself was provided by TfL.

The ANPR data relies on a link to the national DVLA registration database to identify vehicles. The national DVLA data is populated by companies that sell new vehicles and register them for the first time. For passenger cars, this is relatively straightforward. The selling organisation is almost always an officially franchised dealer associated with the manufacturer, the make and model sold are very easily defined, recognisable and important to the purchaser such that it is both easy and important to get it right. For HGVs, many will be registered by the franchised dealer, but many may also be sold unregistered as chassis-cabs to body builders that then sell it to the end customer and are therefore responsible for registration. While the make and basic model (e.g. Mercedes Actros) are relatively simple, each model can be sold in a huge variety of different variants with many different variant codes. The net result of this additional complexity combined with the fact that there are no penalties for not completing the model field, is that the DVLA data contains information of the vehicle model only relatively rarely. It is, therefore, not possible to accurately link the LDS vision data directly to the ANPR London data at a vehicle model level, only at the level of vehicle manufacturer, which analysis shows to be a poor proxy for the real results.

5.1.4 Linking to SMMT data to identify make and model

The Society of Motor Manufacturers and Traders (SMMT) produce a database of the UK vehicle parc. This is compiled by merging data from the DVLA registration database with data obtained direct from UK offices of their member vehicle manufacturers. This data set still contains some anomalous entries, but for the vast majority of cases the make and model fields are properly completed. Thus, it is possible to link the LDS data on direct vision and the SMMT registration data for the UK, based on make and model.

The SMMT data contains records relating to a very large range of variants for each make and model that has been assessed. An extract is shown below in Figure 5-2 as an example and in fact in 2015 there were 133 different variants of Volvo FM where at least one new vehicle was registered in 2015. Many more were recorded if vehicles that were no longer sold in 2015 were considered.

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Figure 5-2: Example extract illustrating the extent of different permutations of vehicle type associated with one model of HGV. Source: SMMT

5.1.5 Linking HGV models to vision ratings

Accurately assessing the number of vehicles in each vehicle class from the SMMT data would require accurate mapping of the small number of direct vision ratings supplied by LDS (2 for Volvo FM) to the large number of different variants recorded in registration data (133 for Volvo FM). However, a range of problems exist with this approach. For example, the Volvo FE has an option to fit a low-entry cab, which is of obvious interest in consideration of direct vision and LDS have produced a separate direct vision rating for this option. However, the presence of the low entry cab is not indicated by any information recorded as part of the model variant in the SMMT data. Thus, any of the many entries for Volvo FE may or may not include a low-entry cab, but there is no way of knowing which. More generally, none of the information recorded in the SMMT data directly relates to vehicle height and so the difference in direct vision performance at different heights cannot be resolved directly via the available data.

Solutions to this problem were sought in discussion with a selection of manufacturers. Each explained that the reason for varying heights was complex and not directly related to the choice of model variant designation as used for registration purposes. It was also considered that the same complexities limited the ability to simply correlate with a broad parameter such as gross vehicle weight (GVW) or body type. That is, there are no hard and fast rules that say a tipper will end up being taller than a box body from the same model and no guarantee that the 32-tonne variant will be taller than the 26-tonne variant.

At least one manufacturer records data internally that could allow this problem to be resolved, such that their sales data could be divided by height and thus by a more accurately mapped vision rating. However, at least one other stated that such linking would be technically impossible because even their own internal systems did not record the detail required. It has not proved possible within the timeframe for this research to obtain data

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from manufacturers that would allow greater resolution of this important fundamental base element of the impact assessment.

The best case/worst case height method described to get around this problem is guaranteed to produce a correct result; the true answer will lie between the extremes. However, it may not prove very informative given the wide range in heights between the two extremes. In the absence of additional data, it is possible to consider alternative methods of estimating where in the range between lowest and highest height assumptions, the most likely heights will lie. However, these will inevitably provide at least some risk of producing an incorrect answer, where the truth actually lies outside of a calculated range.

LDS have measured three vehicle models where the manufacturer confidentially provided them with information as to which height was sold in largest volumes (i.e. the most sold height). In two cases, this was separated by whether or not the vehicles were sold as an off-road vehicle, designated by the sub-category ‘G’ during type approval (N3 or N3G).

The ‘most sold’ method took the measured visible volume associated with each vehicle at its lowest height and designated it as 100%, and the volume visible at the highest height as 0%. Using linear interpolation, the volume visible at the ‘most sold’ height for N3 and N3G vehicles was also converted to a percentage between 0 and 100. It was then assumed that whatever the highest and lowest height of all other vehicles (where ‘most sold’ height was unknown), the most sold height would be at the same percentage point in the range as the average for the 3 where it was known. The resulting estimated ‘most sold’ volumes were converted to star ratings using the same rating boundaries. The narrowed range was produced by assuming that all registrations of a given model were at the N3 most sold height (lower) or the N3G most sold height (upper).

5.1.6 Grouping by body type

At this point, it is possible to calculate effects on GB as a whole, but still not for London where make and model fields are not widely available. In general, previous research (Delmonte et al., 2013) (Summerskill et al., 2015) has suggested that vehicles from the construction sector and/or those with type approval status as an ‘off road’ HGV (N3G) are over-involved in collisions. The type of body typically used by construction HGVs is generally considered to be different to the distribution and long-haul sectors. For example, a cement mixer is almost exclusively a construction vehicle and a tipper is predominantly a construction or agricultural sector vehicle. Thus, the SMMT data was grouped by the body type field. For each body type group, the number of registered vehicles associated with each category of vision rating defined in ‘The Standard’ was summed and then expressed as a percentage distribution, such that, for example, box vans were 9% 0-star, 11% 1-star and 38% 2-star with a lowest height assumption, and tippers were 10% 0-star, 5% 1-star and 47% 2-star.

5.1.7 Applying vision ratings to the ANPR data

London may well have different commercial needs to other regions in GB that result in different freight transport patterns. For example, the concentration of major construction activity might be higher, while the number of national distribution centres may be lower

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than other regions. Although this hasn’t been investigated in detail by this project, it is therefore considered that London may see a slightly different mix of HGV body types (e.g. tipper, box, etc) than GB as a whole. However, it was also assumed that there was no reason why the mix of makes and models used by the operator of a given body type of vehicle (e.g. tipper) would be different if that operator was deploying their vehicles in London or anywhere else in UK.

Given these assumptions, then the distribution of vision ratings for each body type will also be assumed to be the same for London’s ANPR data as was identified nationally in the SMMT data. Thus, the total number of vehicles recorded for a given body type was multiplied by the relevant distribution of vision scores for that body type (derived from the SMMT data). This produced an estimate of the number of vehicles at each vision level defined by ‘The Standard’.

5.1.8 Linking to other datasets

The same process, using the proportion of vehicles of each body type that fall into each vision category as defined using the SMMT data, was repeated to allow vision ratings to be linked to the enhanced STATS19 collision data set.

A similar process was undertaken to link the vision ratings to data obtained from the Continuing Survey of Road Goods Transport (CSRGT). The CSRGT contains data relating to origin and destination of freight journeys by different types of HGV, which allows analysis by more detailed geographies. Unfortunately, the data does not contain body type or make and model. So, in this case three surrogate groups of vehicles were defined:

4-axle rigids, relatively likely to be construction vehicles (tippers, cement mixers, etc.) such as those that have been observed to be disproportionately represented in collision statistics for cyclists and left turning HGVs.

Other rigid vehicles.

Articulated vehicles including drawbar combinations.

5.2 GB market share

The results of the analysis described above produce the following estimate of market share by their vision rating, as defined by ‘The Standard’. It can be seen in Figure 5-3 that this results in a very wide range of estimates that in 2015 0-star rated vehicles represented between 34% and 89% of vehicles first registered in 2015, depending on the height of the vehicles.

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Lowest cab height

Highest cab height

Figure 5-3: GB market share by vision rating based on lowest and highest cab heights for vehicles first registered in 2015. Source: TRL assessment based on LDS rating data and

SMMT registration data

The estimate in Figure 5-4 was based on a limited sample of only 3 models where greater manufacturer data was available. Using this small sample narrows the expected range considerably such that zero-star vehicles represent between 67% and 81% of the same parc. This also suggests that most vehicles are sold at a height higher than the mid-point of the full range available.

Most sold cab height lower

Most sold cab height upper

Figure 5-4: GB market share by vision rating based on most sold height lowest and upper ranges for vehicles first registered in 2015 (note limited sample size). Source: TRL

assessment based on LDS rating data and SMMT registration data

5.3 London market share (vehicles)

The results for London based on the ANPR data and the GB distribution of vision rating by body type are shown below in Figure 5-5 and Figure 5-6. These indicate similarly that there is a wide range of estimated 0-star vehicles depending on their height, but that it narrows considerably when using the more detailed manufacturer data.

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Lowest cab height

Highest cab height

Figure 5-5: London market share by vision rating based on lowest and highest cab heights. Source: TRL calculation based on LDS rating data and TfL ANPR data

Most sold cab height lower

Most sold cab height upper

Figure 5-6: London Market share by vision rating based on most sold height lowest and upper ranges for vehicles first registered in 2015 (note limited sample size). Source: TRL

assessment based on LDS rating data and TfL ANPR data

The evidence presented in section 4.2.1 suggested London has a disproportionate problem with HGV-VRU collisions in manoeuvres where poor vision is likely to be a contributory factor. Comparing the results above, relating to the vision performance of the London vehicle parc, to the equivalents for GB offers no evidence to explain this observation. The direct vision performance of the London parc is on most measures no worse than the GB parc and is estimated to contain a smaller proportion of 0-star vehicles than GB as a whole.

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6 Collision risks by geography

6.1 Methods

6.1.1 Collisions

The relevant collision groups were identified in the national collision database, STATS19. This database contains the geographic Cartesian coordinates (Eastings and Northings) relating to the specific location of each collision recorded. These were converted to latitude and longitude values, which were then exported into Keyhole Markup Language (KML) files. The KML files were in turn imported into Google maps created for the purpose of illustrating the locations graphically12.

6.1.2 Exposure to risk

Although imperfect, the most commonly used measure of the exposure to risk is the distance travelled by the specific types of vehicles involved. This is an improvement over the number of vehicles operating in an area because one type of vehicle may travel much further on average than another, possibly explaining differences in collision numbers that may not have been apparent on a risk per vehicle basis. However, it still leaves the possibility that some vehicle types may spend a greater proportion of their distance travelled on safer roads (from a VRU perspective), such as motorways. Data at this level of detail is not widely available.

The DfT collects data on the activity of GB-registered HGVs (vehicles weighing >3.5 tonnes) operating in the UK through the Continuing Survey of Road Goods Transport (CSRGT). Headline results from this survey are published as national statistics (see (DfT, 2017) for example). The survey is usually based upon a sample of about 230 vehicles per week. The operator of the HGV is asked to provide details of all domestic trips undertaken by that vehicle during a one-week period. The survey data is then grossed up to population figures through grossing factors calculated using population data for HGVs, for each quarter, from DVLA licensing records.

The DfT kindly supplied detailed data to this project covering the period 2011–2015 for vehicles where the origin and/or destination point was located within the London NUTS1 area (UKI)13. In addition to the origin and destination, it includes information about the axle

12 An interactive version of all these maps can be found online at the web addresses below:

Fatal injury collisions: https://drive.google.com/open?id=1pPFXFAZf5XboEaSta0JKhGerF1w&usp=sharing

Serious injury collisions: https://drive.google.com/open?id=1n836ie8vNZ78GeUwek4W_gkjxOw&usp=sharing

Slight injury collisions: https://drive.google.com/open?id=1H7FB2AM4-KfAKOmVso-xICf84iE&usp=sharing

13 The NUTS classification (nomenclature of territorial units for statistics) is a hierarchical system for dividing up

the economic territory of the EU. Regions are defined at several different levels: NUTS 1 relates to major socio-

economic regions, NUTS 2 are basic regions for the application of regional policies and NUTS 3 are small

regions for specific diagnoses. NUTS 3 areas broadly coincide with London boroughs.

https://drive.google.com/open?id=1pPFXFAZf5XboEaSta0JKhGerF1w&usp=sharing

https://drive.google.com/open?id=1n836ie8vNZ78GeUwek4W_gkjxOw&usp=sharing

https://drive.google.com/open?id=1H7FB2AM4-KfAKOmVso-xICf84iE&usp=sharing

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configuration (e.g. 4-axle rigid, 3+2 artic), gross vehicle weight, and the year in which the vehicle was surveyed.

The precise postcode of the origin and destination points was not made available because it was considered to represent personal data because it may have become possible to identify specific individual vehicles or operators even from the anonymised data held.

In order to assess potential traffic routings, and therefore distances travelled in different regions of London, each origin and destination listed was assigned a random location on the road network within the NUTS3 area specified within the data. This means that the location of an origin or destination was randomly assigned within the right London Borough. Routing algorithms based on Edsger Dijkstra's algorithm for solving shortest path problems were used to plot the route between origin and destination. The mileage travelled on different road classes was then recorded. The routes remain a coarse approximation because the actual origin and destination points for each journey remain unknown and, as such, the results have not been presented at road class level, only as generic heat maps indicating the geographical areas where traffic activity was high. Even within this, the error inherent in the method may mask differences in exposure to risk between different vehicle types.

For each journey in the dataset, the calculated distance represented the distance travelled by one vehicle of this configuration. So, the values were then grossed up to population figures using the gross mileage data recorded within CSRGT.

For this project, only road sections within the London NUTS1 area (UKI) were considered. If part of a journey included roads outside of the UKI area then those non-UKI roads were excluded from the analysis.

Initially, the results of the routing analysis were then separated by vehicle type (e.g. 4-axle rigid, other rigid, artic) as a proxy for direct vision performance. However, once information was available from LDS on the actual vision ratings of different vehicles then analyses were repeated to quantify the traffic undertaken by vehicles at different vision ratings. The process of linking this data was as described in Section 5.1.

6.2 Results

6.2.1 Collisions

Collisions involving HGVs > 7.5tonnes and VRUs have been grouped by the type of VRU injured, the injury severity and considering all crashes within the Greater London Authority (GLA) or just those that occurred within the central London Congestion Charge Zone (CCZ). The results are shown in Table 6-1, below.

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Table 6-1: Collisions involving HGVs > 7.5t by VRU type injured, injury severity and collision location. Source: STATS19 analysis

All GLA CCZ

Number %

Fatal

Pedestrians 35 1 3%

Pedal cyclists 19 3 16%

Motorcyclists 4 0 0%

Serious

Pedestrians 54 6 11%



Slight

Pedestrians 119 16 13%



It can be seen that any policy option considering only the congestion charge zone would limit its scope to only a relatively small proportion of the Greater London total. Additional analysis considering only those collisions specifically relevant to direct vision (e.g. moving off or turning left manoeuvres) did not reveal substantial differences in that conclusion.


A basic analysis of the traffic patterns in the London data was conducted to provide background context to understand the exposure to risk in general terms before linking exactly to the vision rating as defined by ‘The Standard’. For example, an analysis of distance travelled by road class and vehicle type showed a broadly similar pattern for each vehicle type.

Table 6-2: Distribution of HGV traffic (vehicle kms) in London by road class and vehicle type. Source: analysis of estimated routes based on CSRF+GT origin and destination data

Proportion of vehicle kms by road type classification

A B M All

4-axle rigid 86% 11% 3% 100%

Other rigid 86% 12% 3% 100%

Articulated (including drawbar)

82% 8% 11% 100%

This may be a consequence of the randomised start and finish points, because in reality it may be that operators choose vehicles based on the local access available to start and finish

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points, but these are not accurately represented by the technique used here. For example, a consolidation centre on the edge of London may be situated very near a motorway and be a destination mainly for articulated vehicles. However, the randomised process for positioning each destination may have placed this centre the other side of the borough to a motorway such that it appears that more distance is travelled on minor roads than is really the case. The opposite could also be true for destinations away from major roads that are served by smaller vehicles: if the random process places them on a major road the distance on minor roads would be reduced.

Differences between vehicle types have also been looked for in terms of journey origins and destinations, at the source level of NUTS3. It can be seen that in general the origin of trips is relatively evenly distributed (note that destination patterns are very similar because most trips will subsequently involve a mirror image return journey).

Figure 6-1: Distribution of HGV trip origins by London borough. Source: Analysis of CSRGT data

In general, the most popular origins are Barking & Dagenham and Havering (15%), Enfield (12%), Bexley and Greenwich (9%) and Ealing (8%).

Figure 6-2 shows that when only 4-axle rigids were considered, they were less likely to come from Merton and Wandsworth (1% instead of 4%) and more likely to come from Hounslow (9% instead of 4 %).

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Figure 6-2: Distribution of 4-axle rigid HGV trip origins by London borough. Source: Analysis of CSRGT data

Similarly, Figure 6-3 shows that different patterns were also evident when only articulated vehicles were considered. Merton showed a slight increase (to 5% from 4%) as did Enfield (to 15% from 12%), whereas Camden showed a drop (from 4% to 1%).

Figure 6-3: Distribution of articulated (including drawbar) HGV trip origins by London borough. Source: Analysis of CSRGT data

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6.2.3 Analysis and conclusions

A comprehensive range of ‘heat maps’ were created to assess whether the patterns of cycle and pedestrian collisions match with the levels of exposure of either VRUs or HGVs in those areas. The VRU data was provided by TfL. The traffic heat maps are defined such that the areas with the highest levels of traffic in any given chart are indicated as bright yellow, those with the lowest are shown in purple. Collisions have been mapped as dots indicating the exact location of the collision; blue dots represent pedestrians, green represents pedal cyclists and orange represents motorcyclists.

The heat maps14 of pedestrian traffic illustrate a greater concentration of traffic in the City and Westminster boroughs of central London with a reducing concentration towards the outer London boroughs. As shown in Figure 6-4, the pedestrian fatalities also show some tendency to be more central, but perhaps without the strong peak in the city area that would be suggested from the pedestrian traffic.

Pedestrian to HGV collisions Pedestrian kilometres

Figure 6-4: Locations of HGV > 7.5t to pedestrian fatalities and pedestrian traffic heat map. Source: STATS19 collision data (2011-15) and pedestrian walking survey data provided by

TfL

Pedal cycle traffic shows a similar pattern although the peak levels are slightly less centralised than for the pedestrian traffic. Conversely, the pedal cycle fatalities appear to be more centralised than the pedestrian fatalities, though most remain just outside the congestion charge zone. Thus, the location of pedal cycle fatalities does seem to broadly correlate with peak levels of pedal cycle traffic.

14 Note that the heat maps are generated in relative terms. That is, the area with the highest traffic volume will

always be coded the bright yellow, the lowest the darkest purple. The colour is not linked to an absolute value

of traffic. Thus, the heat maps cannot be compared to each other in absolute terms to say, for example, that

there was more pedestrian traffic in Wandsworth.

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Pedal cycle to HGV collisions Pedal cycle traffic (kilometres)

Figure 6-5: Locations of HGV > 7.5t to pedal cycle fatalities and pedal cycle traffic. Source: STATS19 collision data (2011-15) and pedal cycle survey data provided by TfL

There is at least some, and possibly quite a strong, relationship between VRU traffic and fatalities. Figure 6-6 to Figure 6-8, below, examine the relationship between different types of HGV traffic and VRU fatalities.

Figure 6-6: Location of fatal VRU-HGV collisions and articulated HGV traffic (2011-15). Source: STATS19 collision data and traffic data from the CSRGT

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Figure 6-7: Location of fatal VRU-HGV collisions and 4-axle rigid HGV traffic (2011-15). Source: STATS19 collision data and traffic data from the CSRGT

Figure 6-8: Location of fatal VRU-HGV collisions and other rigid HGV traffic (2011-15). Source: STATS19 collision data and traffic data from the CSRGT

It can be seen that there is much less correlation generally between the areas of peak HGV traffic and the VRU collisions. The traffic patterns for articulated vehicles and 4-axle rigid HGVs are quite different with peak articulated vehicle use in north London and peak 4-axle rigid use in pockets to the west and east of the city. It can be seen that smaller rigid vehicles seem to work in areas similar to both articulated and larger rigid HGVs, as they have high

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levels of traffic in both the areas that the other groups of vehicles do, plus in the centre of the city. Thus, based on this data alone, it would be expected that smaller rigid vehicles would be those most commonly involved in collisions with VRUs.

An important conclusion of this finding is that if it is genuinely the larger vehicles that present the highest risk to VRUs, as suggested by previous collision analyses (Robinson et al., 2016), then even low levels of that type of traffic in areas with high levels of VRU traffic can result in the observed numbers of collisions. There is, therefore, a risk that if VRU traffic increases substantially in the future in those areas where the vehicle kilometres travelled by the larger vehicles is already high then, in the absence of any measures to control the risk and assuming use of the same road types, the number of VRU casualties would be expected to rise substantially.

A further breakdown of the activity of different vehicle types by their vision rating revealed little difference. That is, with the exception of 5-star vehicles which from 2011-15 would have been mainly used as refuse collection vehicles, the pattern of use did not vary for different levels of vision. This likely reflects the fact that size and body type will determine the jobs that vehicles do and thus where and how often they are used. However, these are not the only determinants of direct vision performance, make and model also played a strong role and many of the makes and models were sold into all sectors at all relevant size points such that the correlation between type of vehicle and vidion performance is not as strong as might have been expected.

Given the stronger relationship between VRU activity and collisions, the fact that peak VRU activity is relatively centralised in London and that much of the peak use of larger vehicles is outside of the central area, there is scope for optimising the geography of any policy relating to the use of vehicles that lies somewhere in between the boundaries of the congestion charge zone and the GLA. Although this has not been defined in detail, a boundary formed by the north and south circular roads would approximate such a zone as illustrated in Figure 6-9, below.

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Figure 6-9: Illustrating the proportion of HGV-VRU fatalities that occur within a boundary formed by north and south circular roads.

It can be seen that a north/south circular boundary would capture almost all of the pedal cycle fatalities (green dots) and a large proportion of the pedestrian ones (blue dots). Figure 6-10 shows that while a large proportion of ‘other rigid’ traffic would be within the zone, there would still be substantial areas of peak traffic outside of it, and therefore, not subject to it. For articulated vehicles and rigid vehicles, a greater proportion of the peak traffic would be outside of the zone.

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Figure 6-10: Illustrating the proportion of HGV traffic that occurs within a boundary formed by north and south circular roads.

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7 Collision risks by time of day

7.1 HGV-pedestrian collisions

The time of day at which collisions between HGVs and pedestrians occurred have been analysed. All manoeuvres and all collision severity levels have been considered. The results for fatally injured pedestrians are shown below in Figure 7-1 where the HGV was moving off from rest, and Figure 7-2 for collisions involving any HGV manoeuvre. Results for other collision severities can be seen in Appendix E.

Figure 7-1: Pedestrians fatally injured in London collisions involving HGVs moving off from rest, by time of day (2011-2015). Source: STATS19

It can be seen that the vast majority of pedestrian fatalities occur during daylight hours, with this pattern also seen for both serious and slight injuries.

Figure 7-2: Pedestrians fatally injured in London collisions involving HGVs (all manoeuvres), by time of day (2011-2015). Source: STATS19

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7.2 HGV-pedal cyclist collisions

Similar data has been produced for pedal cyclists with results for fatalities presented below and for other collision severities in Appendix E. Overall, pedal cyclists appear slightly more prone than pedestrians to collisions with HGVs at night and also show a greater likelihood of suffering a fatal injury during rush hour traffic. Again, this relationship is also seen for both serious and slight injuries.

Figure 7-3: Time of pedal cyclist fatal injury collisions – turning left. Source: STATS19

Figure 7-4: Time of pedal cyclist fatal injury collisions – all manoeuvres. Source: STATS19

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7.3 Analysis

Comparing the data shown in Figure 7-3 and Figure 7-4 to a limited sample of national pedal cycle traffic data15 reveals a similar pattern to the fatal collision data, which indicates a possible link to weekday traffic volumes, assuming London cycling follows national patterns in relation to time of day (Figure 7-5). Although the conclusions that can be drawn from this are limited due to the comparability of the data (i.e. London vs UK data, no HGV traffic included, and for one year only), the similarity in the patterns of pedal cycle injuries of all severities is striking.

Figure 7-5: Pedal cycle traffic distribution on all GB roads 2016

It is more difficult to draw any conclusions from the distributions of pedestrian casualties as occurrences are spread much more evenly through the hours of the day. Pedestrian daily traffic flow data is not widely available, but data collected in January 2015 and published by the City of London Corporation16 gives timed, weekday traffic flows at a limited number of locations around the ‘Square Mile’ (Figure 7-6). This suggests strong rush hour peaks for pedestrian traffic that is perhaps less evident in the number of collisions at different times.

15 https://www.gov.uk/government/statistical-data-sets/tra04-pedal-cycle-traffic

16 https://www.cityoflondon.gov.uk/services/transport-and-streets/traffic-

management/Documents/Bank%20junction%20pedestrian%20movement%20analysis.pdf

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Figure 7-6: City of London timed pedestrian traffic flow

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8 Definition of detailed policy options for assessment

The research described in this report was undertaken in close collaboration with the sponsor, TfL, and with organisations they had separately contracted to measure more vehicles, LDS, and to undertake a business impact assessment, Jacobs. Thus, emerging findings were exchanged at regular intervals and simplistic analyses were undertaken at an early stage to assess the order of magnitude of effects.

Any scheme that outright bans a large number of vehicles relative quickly will result in a high compliance cost. TfL have considered a range of options that could mitigate that cost to business. The initial findings from their research show that for compliance costs to be materially lower, there needs to be an option for retro-fitting vehicles in a way that reduces other aspects of road risk to compensate for poor direct vision.

These findings led TfL to the development of the following options:

Option 0 – Do Minimum: The direct vision standard does now already exist and TfL will not withdraw it. Therefore, the minimum action that will occur is that it will be used within existing schemes to promote best practice, for example FORS17, and within TfL’s own contracts for freight services. This is expected to promote a significant degree of voluntary uptake of vehicles with better vision over time

Option 1 – Outright restriction: The outright restriction represents the basic consideration of banning all vehicles with a 0-star rating from 2020 and all vehicles with less than a 3-star rating by 2024.

Option 2 – Phased outright restriction: As option 1 but until 2024 the ban would apply only to newly registered vehicles.

Option 3 – Outright restriction with transitional mitigation: As option 1 but until 2024 some 0-star rated vehicles may be issued a permit if they can demonstrate the use of alternative safety measures to mitigate the risk.

Option 4 – N3G ban: Prohibit all N3G category vehicles from 2020.

Option 5 – HGV safe system scheme: All N3 HGVs to apply for permit to prove a safe system standard is met. All vehicle rated 1-star or better in accordance with ‘The Standard’ automatically given permit (from 2020, 3-star from 2024). Permits only issued to vehicles 0-star (from 2020, less than 3-star from 2024) where they can demonstrate they are “above par” on a number of key safety aspects.

In options 1, 3 and 5, all N3 vehicles would need to apply for a permit and only those found to comply with the requirements would be granted one.

TfL asked TRL to focus the analysis of casualty impacts on options 0, 1 and 5.

17 https://www.fors-online.org.uk/cms/news/transport-londons-direct-vision-standard-consultation-closes-18-

april-2017/

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9 Valuing the prevention of casualties

9.1 Casualty valuation

The UK DfT routinely assigns an economic value to the prevention of casualties and, as such, historical data is already readily available (Department for Transport, 2016). In order to generate future values a calculation was made based on the available data on data in DfT statistical table RAS60003, “Total value of prevention of reported accidents by severity and cost element”, which breaks down costs by collision severity into the following categories:

Lost output

Medical and ambulance

Human costs

Police costs

Insurance and admin

Damage to property

This calculation essentially broke down the components in the historical data to identify the number of collisions that the values were based on and in the analyses of future values the number of collisions was replaced with a forecast.

9.2 Congestion valuation

STATS19 collision data was merged with traffic data from TfL’s Automatic Traffic Counter networks. The Automatic Traffic Counters report flow and average speed for each hour. Where a collision had occurred within 500 metres of a traffic counter the data was analysed to see how much the speed in the area dropped compared to the same hour the previous week.

It was found that generally only traffic counters on the same road as the collision saw a drop in speed (parallel roads did not generally see a significant speed drop). As a result, only detectors on the same road as the collision were considered for further analysis.

It was also found that only detectors within around 300 metres of a collision saw a significant drop in speed. Therefore, it was assumed that the range of a collision’s impact is around 300 metres.

The hours containing and following each collision were looked at to see how long each road was disrupted for. An average vehicle delay was calculated from this data by calculating the time taken to travel 300 metres during the collision (based on the measured speed) and the time taken in the same hour the previous week. The total delay was calculated by multiplying this by the number of vehicles which passed the detectors. The total delay was turned into a monetary value using the values of time for cars from WebTAG18.

18 https://www.gov.uk/guidance/transport-analysis-guidance-webtag

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The sample of incidents with available data was small:

2 fatal incidents

0 serious incidents

19 slight incidents

The fatal incidents generated up to 70 vehicle hours of delay and the slight incidents between zero and 150 vehicle hours of delay. Since the figures are not significantly different within this small data set, data has not been reported separately for different incident types.

A number of assumptions were made in this methodology:

That all collisions cause congestion for 300 metres

That the speed of vehicles remains constant throughout the 300 metres travelled

No congestion is caused on the surrounding road network

The number of impacted vehicles is the number which pass the detector

The set of incidents analysed are representative of all incidents – it is likely that the incidents are biased towards those on main roads since these are more likely to have detectors

A number of improvements could be made to the methodology given additional data sources:

Floating vehicle data from GPS units could be used to understand the full impact of an collision on journey times on the surrounding road network

Additional fixed detectors would allow more granular counts of the number of impacted vehicles

A larger volume of collision data would give more confidence in results

The collisions that were analysed gave the following results as in Table 9-1. Overall the average delay caused by each collision was 22 hours. Multiplying this by the value of time for cars from WebTAG gives the following results for each year as in Table 9-2.

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Table 9-1: Congestion delay hours per collision

Time Severity Road class Road Type Total delay (h)

16:47 Fatal 3 6 0

07:58 Fatal 3 6 70

09:32 Slight 3 3 45

14:30 Slight 3 3 6

06:45 Slight 3 1 11

07:50 Slight 3 6 6

10:10 Slight 3 3 0

11:30 Slight 3 6 14

07:05 Slight 3 6 10

16:51 Slight 3 3 1

09:27 Slight 3 3 4

08:00 Slight 3 6 3

07:17 Slight 3 3 49

14:26 Slight 3 6 35

17:30 Slight 3 6 0

11:48 Slight 5 2 19

Table 9-2: Congestion cost per collision forecast

Year Cost per collision of congestion

Year Cost per collision of congestion

2012 £272 2021 £306

2013 £276 2022 £311

2014 £282 2023 £316

2015 £286 2024 £321

2016 £289 2025 £327

2017 £292 2026 £333

2018 £295 2027 £339

2019 £298 2028 £345

2020 £302 2029 £351

2030 £358

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9.3 Emissions valuation

A road traffic collision is likely to lead to an increase in emissions due to:

Congestion: The collision will lead to congestion and start/stop traffic. This will increase the emissions relative to normal traffic conditions

Diverting: Drivers might choose to avoid the area or be directed by the police around the area (in a case of the police closing the road). This will lead to an increase in the distance covered, resulting in higher emissions than normal.

In addition, there may also be other vehicles required to attend the incident – police, ambulance, fire, vehicle recovery, etc. However, their impact will be small in comparison to the congestion and diversion effects, so these will not be included in the analysis.

To analyse the effect, the London collision traffic data (as in section 9.2) was used. The emissions were determined for the incident period and the standard period.

Defra’s Emission Factors Toolkit (EFT)19 was used to calculate the emissions from the traffic. The input to this is the vehicle flows and speeds. Normally, AADT (annual average daily traffic) are used, but in this instance the hourly flows and speeds, collated over all the lanes in the affected direction, are used. The input also requires the light-duty:heavy-duty vehicle split, which is not available from the collision traffic database. From a brief review of classified traffic count data, a default value of 5% heavy-duty vehicles was used for the local fleet. It was also assumed that only light duty vehicles would divert around the incident.

The additional emissions of NOx, PM and CO2 were determined as follows:

1) The hourly flows following the incident in both directions were compared to the standard flows from unaffected days, to determine the direction affected and to decide on the time period to analyse.

2) Congestion:

a) The emissions with the collision were calculated using the EFT based on the flows & speeds from the collision day; over all the hours the incident appears to affect flow.

b) The emissions without the collision were calculated using the EFT based on the flows & speeds from non-collision days; over the same hours as the incident.

c) The total emissions from (2) were subtracted from (1) to give the change in emissions in g/km.

d) The length of road disruption was not known from the traffic data. However, a value of 300 m was used for the analysis, to be consistent with the congestion valuation described earlier. This was then used to determine to total change in emissions in g or kg.

19 Details of the toolkit can be found at https://laqm.defra.gov.uk/review-and-assessment/tools/emissions-

factors-toolkit.html

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4) Diversion:

a) This was determined when there appears to be a reduction in the flows at the incident.

b) The reduction in flow was assumed to divert around the incident.

c) As the distance was not be known and will vary depending on the location, an assumed value for the diversion distance of 1 km was used (i.e. the vehicle is driven 1 km around the incident instead of 300 m through it).

d) The emissions were calculated using the EFT using typical non-collision speeds.

e) It was assumed that only light-duty vehicles divert around the incident (buses are unlikely to divert from their route unless the road is closed), so the diversions emission calculations are based on 100% light-duty vehicles.

The two sources of additional emissions, congestion and diversion, were then combined to give the total increase in emissions in g or kg.

The composition of the local fleet plays an important role in the resulting emissions. As the years progress, old dirty vehicles get scrapped and newer clean vehicles are introduced into the fleet. The EFT includes the year, which defines the fleet composition to use for the calculations. As the year will affect the overall emissions, the emissions were calculated for a base year (2015) plus future years: 2020 and 2025, and then interpolations taken accordingly for the annualisation of the model period to 2030.

This method does have some inherent assumptions and limitations as follows:

1. Traffic count is typically on one side of the collision. The collision will mainly affect traffic speeds heading towards the collision, not away from it. Therefore the extra emissions for each collision might need to be multiplied by 2, 3 or 4 depending on how many queues head towards it (only the 2nd incident has traffic data from more than one site).

2. Only traffic flows and speeds at the traffic counts are available; no information on stop/start traffic, queue length, etc., which would also affect emissions.

3. The incident might reduce emissions at the site, because there are fewer vehicles getting through. It is assumed that all of the short-fall take a detour, though they might cancel their journey (e.g. stay home that day).

4. It is assumed that the incident affects 300 m of traffic congestion (this matches the approach in the congestion valuations).

5. It is assumed that diverting traffic covers 1 km.

To give a value to the additional emission that occur because of the incident, costs as detailed in the following sub-sections have been used.

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9.3.1 PM & NOx

Defra’s damage costs have been used, as listed in “Air quality economic analysis. Damage costs by location and source”20. The document is undated but was last accessed on 4 August 2017. The PM costs are listed in Table 9-3 and the NOx costs are in Table 9-4. The NOx table shown is for when the PM costs are also being valued (as in this case). Different damage costs are also available when only the NOx costs are being valued. The costs are provided for different area types, with central, low central and high central values. Only the cost for central, inner and outer London are shown here.

Table 9-3: PM costs by location and source

2015 prices

£ per tonne of emission change Central

Estimate Low Central

Estimate High Central

Estimate

Transport central London £265,637 £207,981 £301,859

Transport inner London £273,193 £213,898 £310,447

Transport outer London £178,447 £139,717 £202,781

Table 9-4: NOx costs by location and source (where PM is also valued)

2015 prices

£ per tonne of emission change Central

Estimate Low Central

Estimate High Central

Estimate

Transport central London £96,171 £38,468 £153,874

Transport inner London £98,907 £39,563 £158,251

Transport outer London £64,605 £25,842 £103,368

These values were inflated by 2.5% compounded to give costs for future years.

9.3.2 CO2

For CO2, DECC’s “Updated short-term traded carbon values used for modelling purposes 2015”21 has been used. This list the low central and high values per tonne of CO2 equivalent, as shown in Table 9-5.

20 https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/460398/air-quality-

econanalysis-damagecost.pdf

21 https://www.gov.uk/government/publications/updated-short-term-values-used-for-modelling-purposes-

2015

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Table 9-5: DECC’s updated traded carbon values for modelling purposes, £/tCO2e in real 2015 terms

Year Low Central High

2015

£5.94 £20.79

2016

£5.91 £23.40

2017

£5.89 £26.41

2018

£6.12 £29.86

2019

£6.35 £34.04

2020

£6.59 £39.03

2021

£6.84 £45.16

2022

£9.38 £51.99

2023

£12.68 £58.98

2024 £3.06 £17.14 £67.35

2025 £7.63 £22.56 £73.72

2026 £11.54 £29.02 £80.48

2027 £17.12 £31.15 £87.64

2028 £23.81 £34.47 £93.82

2029 £32.83 £40.58 £101.81

2030 £38.29 £47.10 £108.74

2031 £38.29 £47.10 £108.74

2032 £38.29 £47.10 £108.74

2033 £38.29 £47.10 £108.74

2034 £38.29 £47.10 £108.74

2035 £38.29 £47.10 £108.74

The emissions values for NOx (g), PM10 (g), and CO2 (g) were calculated separately for each incident, for the years 2015, 2020 and 2025. The emissions reduced over time, due to the evolution of the fleet. For example, new, clean vehicles are introduced and the older, dirty vehicles get scrapped. The default fleet from Defra’s Emissions Factors Toolkit (EFT) has been used for each of these years.

The damage costs were then calculated for each of these three years for NOx, PM and CO2, which were then added together to give the total cost. For the subsequent cost evaluation, the average of the central and inner London costs have been used (there is not a lot of difference between the two). These have then been interpolated and extrapolated to give costs for each year ranging from 2011 to 2030.

Damage costs also include SOx and NH3 however these data are unavailable. These emissions would be tiny and the damage costs/tonne for SOx & NH3 are about 2 orders of magnitude lower than for NOx & PM (about £2k/t compare to £60-275k/t). So it can be assumed that the costs would be negligible.

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10 Effectiveness of solutions

10.1 Retrospective statistical approach

10.1.1 Direct vision

Vehicles are already in circulation with a broad range of direct vision performance from the best to the worst. As described in section 5, the vision ratings by make and model, provided by Loughborough Design School, were linked to ANPR data showing the number of different vehicle types that entered London in 2016 and to the sub-sample of London STATS19 data that is ‘enhanced’ with a link to DVLA information on body type. It was therefore possible to calculate a real world collision rate for different vision levels, as defined by ‘The Standard’ in terms of the number of collisions per 100,000 vehicles of a given level that enter London. The results are shown in Figure 10-1 overleaf.

Based on the body of previous research relating to collisions involving blind spots and the ability of direct vision to improve outcomes, it would be expected that the average number of relevant collisions per vehicle would be less for vehicles with better direct vision than for vehicles with worse direct vision. Figure 10-1 shows that this is not what was observed from the data for either of the main target manoeuvres or given any of the four different ways of linking vision rating to collision and exposure data or at any collision severity. In general, 5-star vehicles had a lower collision rate than 0-star vehicles but the intermediate ratings available (where both casualties and vehicles exist at that level22) are generally much higher than either 0- or 5-star vehicles.

Taken at face value, this would imply that improved direct vision was not effective in reducing the risk of close proximity manoeuvre collisions with VRU. That is, it could be taken as evidence of an absence of the expected effect. However, the analysis is fundamentally limited by the fact that both the collision and exposure data do not contain accurate information on vehicles model, and even if there were, there is no way to tell what height the cab was at for the specific vehicle recorded entering London or involved in a collision. Thus, there is no way to be sure the correct vision rating is applied to any individual vehicle which leaves very significant scope for error in both collision and exposure data. This is particularly true in the case of the collision data. For the exposure data, the assumptions and estimations used are applied across a large population of more than 200k vehicles and therefore errors in individual cases would be expected to even out over a large population. However, the collision numbers are measured in tens. Thus, the assumption that the distribution of vision rating by body type that applies across the whole GB HGV population

22 A casualty rate of zero is indicated either where no casualties have occurred involving a collision at that

vision rating level and/or where no vehicles exist at that level. It should be noted that section 5.2 showed that

no vehicles were rated 3- or 4-star at their highest height, based on either methodology and, in the most sold

methodology, no vehicles were rated 4-star at their lowest height. Thus, most cases where there is a blank

space represent situations where the method says there are no vehicles rather than that there are vehicles in

circulation but no collisions.

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of around 0.5 million applies in the tens of collision cases recorded is extremely vulnerable to error.

In addition to this, there are a range of more standard confounding factors such as whether different vehicle configurations, body types or vision ratings get used differently on different roads, by older or younger drivers, in higher risk areas or in different commercial situations affecting risk (e.g. driver paid by load quantity delivered rather than per hour). None of these could be accounted for in the analysis. It is, therefore, considered more likely that this result represents a continued absence of evidence of the effect of direct vision on VRU collision risk, rather than evidence that such an effect does not exist.

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Figure 10-1: VRU casualty rate per 100k vehicles entering London by manoeuvre, method of linking vision ratings to vehicle population data, vision rating and injury severity.

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10.1.2 Blind spot safety (detection and warning) systems

The option for a safety permit based on a safe system of work is not yet defined in detail. One likely component of it may be the use of active safety detection and warning systems. A test method has been developed to assess the performance of such systems and systems are in use commercially in the market. In theory, a retrospective statistical analysis of effectiveness would also be possible for these systems.

However, at this time, very few systems have been performance tested against the new standard such that performance of systems is unknown. There is also no way currently to identify from standard sources of collision data (e.g. STATS19) or exposure data (e.g. ANPR) whether or not any specific vehicle is equipped with any such device and, if so, what type of device it was equipped with. The absence of this data makes a retrospective statistical analysis of the effectiveness of such systems impossible at this time.

10.2 Predictive engineering approach

The main solution considered in this context is, of course, direct vision. All standards of direct vision (0- to 5-star) do potentially exist in the vehicle parc today. The option for a safe system of work allows the possibility of a range of different solutions intended to prevent the same casualties. The details of what exactly will form the safe system under the permit, should it be taken further forward, have yet to be defined. In the short term, therefore, it was assumed that the rating scheme defined by Knight et al. (n.d.) for blind spot safety systems, such as advanced field of view aids and proximity and collision warnings would be used.

TfL provided information understood to be derived from the freight operator recognition scheme (FORS) that approximately 30% of HGVs in London would currently be equipped with such systems. Evidence suggested that most of the existing systems would score relatively low on the assessment scale (defined as A to F to avoid confusion with the direct vision star rating) and that the highest ratings would not be achievable by any current system on the market (Knight et al., n.d.).

Thus, the collision rate currently experienced and measured using past data, is the product of a blend of real vehicles at all levels of direct vision performance, mostly not equipped with additional blind spot safety features and, where blind spot safety systems are fitted, mostly with relatively low performance levels.

10.2.1 Effectiveness of direct vision

There is an absence of convincing evidence that a statistical relationship either does, or does not, exist between the direct vision performance of a vehicle and the involvement per unit of exposure of that type of vehicle in collisions between HGVs undertaking low speed manoeuvres and VRUs in close proximity to it. Given this absence, a predictive method of quantifying the potential effectiveness of direct vision was considered.

Relatively few studies contain empirical data quantifying the risks associated with different levels of direct vision but a recent study examined the relationship using a driving simulator. In a study conducted by Arup and Leeds ITS, 30 drivers were tasked with avoiding a cyclist or

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pedestrian while driving a traditional and low-entry cab (LEC) HGV in a simulated urban environment (Arup, 2017). Three main scenarios were assessed:

1. A cyclist travelling along the nearside of a stationary HGV

2. A cyclist travelling along the nearside of a moving HGV

3. A pedestrian walking in front of a stationary HGV

Two rounds of simulations were carried out, one to simulate normal driving and one with an

added cognitive load for the drivers. In Scenario 1 improved direct vision corresponded to a

100% reduction of the number of drivers who collided with at least 1 cyclist but when a

cognitive task was introduced there was a 44% increase in this figure. In Scenario 2

improved direct vision resulted in a 20% reduction in a normal driving task but had no effect

in a cognitive driving task. Finally, in Scenario 3, improved direct vision had the largest

reduction in collisions with VRUs, with a reduction of 88% in a normal driving task and 77%

in a cognitive driving task when compared to a traditional cab.

Figure 10-2: Number of drivers who experienced a collision with at least one VRU (Adapted from (Milner and Western-Williams, 2016)).

The simulation results suggest that in the case of cyclists, improved direct vision is unlikely to substantially reduce the number of collisions for the trialled scenarios (0-44% increased during cognitive tasks, 20-100% reduction during normal driving tasks). Furthermore, survey responses from HGV drivers conducted within the same project demonstrated that there was no evidence to suggest that an additional nearside passenger door window improves the safety of VRUs in close proximity to a HGV. The HGV drivers raised several issues with the additional windows including giving the VRU a false sense of security and that the cyclist can only be identified whilst they are adjacent to the window, by which point the cyclist is already in the danger area.

Rigid HGVs are comparable in size, mass and manoeuvrability to buses. However, buses do have considerably better direct vision than HGVs, typically with a much lower driving position and a relatively low edge to the front windscreen. View to the nearside can be more obscured by structures associated with assault screens, A-pillars, mirror clusters and pillars forming part of the passenger door among other things. Edwards et al. (2017)

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comprehensively reviewed collisions involving buses in London. In a sample of 48 fatal collisions occurring over a period of several years, by far the dominant collision type was pedestrians crossing the road and being hit by the front of a bus moving along normally in traffic. 4 involved a bus moving off from rest while a pedestrian was crossing in front of them. None involved a pedal cyclist injured during a left turn. This implies, that the improved direct vision of buses has contributed to a reduced problem with these manoeuvres but does not prove a causal link.

10.2.2 Effectiveness of other technical solutions

Direct vision is not the only safety measure capable of reducing the frequency of close proximity manoeuvring collisions between HGVs and VRUs. TfL already encourage a variety of other actions, including fitment of blind spot cameras, sensors to detect VRUs in blind spots and warn the driver, left turn warnings aimed at alerting VRUs, driver training, assessments of work related road risk, etc.

Knight et al. (n.d.) described research sponsored by TfL that created a rating scheme to assess the casualty reduction potential of ‘blind spot safety systems’ such as advanced vision aids and proximity and collisions warning. This was created to work alongside action on direct vision and to inform the choices of vehicle operators choosing to fit such systems in line with FORS requirements. Four categories of system were defined as shown in Figure 10-3, below.

Figure 10-3: Category definitions of systems in scope of the assessments defined by (Knight et al., n.d.)

Field of View Aid

• Technically, any system that helps enable a VRU in close proximity to be seen

• However, direct vision and blind spot mirrors excluded because dealt with elsewhere

Proximity warning

• System that uses sensors to detect the presence of a VRU close to the vehicle and warns the driver

• Warning sounds whenever VRU is present irrespective of whether vehicles are on a collision path

Collision warning

• System that uses sensors to assess the trajectories and speeds of both vehicles and warns when it calculates a collision is imminent

Motion Inhibit

• A system that prevents a vehicle moving off from rest when sensors detect a vulnerable road user in close proximity to the front of the vehicle

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The same research defined a range of test scenarios related both to the effectiveness of systems in collision mechanisms similar to those defined as the target population in the direct vision assessment, but also considering false positives and the quality of the human machine interface. The different assessments are illustrated in Figure 10-1, below.

Table 10-1 Test scenarios for each blind spot safety application

A scoring scheme was developed that accounted for all of these different variables to produce a total rating. However, only two different systems were used to develop the tests and the rating boundaries were therefore assessed based on theoretical systems and published information available about the characteristics of some systems on the market. Thus, 14 systems were considered in an exercise to derive boundaries and weightings considering the following variables:

Whether the test result related to physical performance or the quality of HMI

Performance in ‘true positive’ tests and false positive tests

What type of collision (moving off or turning left) the test related to

The system of weighting allowed each individual test score to be combined into an overall percentage score (max 100%). These percentage scores can be translated to any category system desired. In the original research, a 0- to 5-star approach was used in order to be consistent with direct vision assessments (Knight et al., n.d.). However, for the purposes of this research, reported in subsequent sections, 0-5 was replaced by A-F to avoid confusion with the direct vision rating.

The majority of the systems assessed (some real, but mostly based on specification) were found to score at the low end of the performance scale proposed. This is because the test and specification information suggested that current aftermarket systems often only work in

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one of the two key manoeuvres, and they tend to detect the presence of anything regardless of the level of risk (e.g. roadside railings). This lack of discrimination between objects tends to mean either that the warning is frequently activated in non-safety critical situations or that the sensitivity of the system is reduced so that it only detects things within about 60 cm of the vehicle. This reduces the frequency of activation in non-critical situations but also limits the effectiveness of the system in critical situations. The performance of the Human Machine Interface (HMI) is also often variable.

Overall, such systems would be likely either to have a limited effect due to their operating limits, or due to drivers disengaging with the system because they are annoyed or misunderstand the warnings. On the other side, highly effective systems should be possible that work in both scenarios, use a mix of proximity warning, genuine collision warning and potentially brake intervention (moving off scenario only). These would be expected to follow best practice HMI such that proximity warnings provide low annoyance “information” signals only, collision warnings would be urgent and more intrusive (but also more rare) and, ultimately, should the driver not take the right action, the vehicle could intervene, whilst still being over-rideable by drivers in the case of a false positive. No systems with all of these characteristics are yet thought to be in the market, though some that are moving in that direction are.

Automated braking systems active in the left turn situation are not yet available to purchase on finished vehicles but are being marketed by tier one suppliers to the commercial vehicle manufacturing industry23. These have excellent potential benefits, but are, as yet, untested such that the real effectiveness remains unknown. They have been excluded from the scope of the procedure developed by Knight et al. (n.d.) at the current time but could be added relatively easily when they become available to operators purchasing new vehicles.

10.3 Modelling Effectiveness

The model is based on the principle of associating different levels of relative risk to different levels of direct vision performance (0-5) and different levels of blind spot safety system performance (not fitted & A to F). The model calculates for past years as well as future years. Thus, an important element of self-verification of the model is also included. When the relative risk of each performance level is multiplied by the number of vehicles at that level, then it produces an estimated number of casualties. When considering past years (2011-17) then the number of vehicles in each policy option is no different to those in the ‘do nothing’ scenario. Thus, the number of casualties calculated should be the same as the actual number of casualties observed in past data in the ‘do nothing’ scenario.

In the ‘effectiveness’ calculation, the value of 1 is equivalent to the past observed casualty rate. However, the need to calculate the actual casualty numbers for past years means that the level of each system closest to 1 varies depending on whether the vehicle parc is defined based on the extreme highest and lowest values or the narrowed range defined by the limited data on ‘most sold’ heights. The value of relative risk associated with vehicles rated

23 http://www.wabco-auto.com/media/media-center/press-releases/press-releases-single-view/news-

article/wabco-unveils-breakthrough-technology-to-help-protect-pedestrians-and-cyclists-in-city-traffic-new/

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by ‘The Standard’ at 0-star was entered manually and assumed to be the same for a vehicle not fitted with blind spot safety systems. For direct vision in ‘moving off from rest’ scenarios, the relative risk of a 5-star vehicle was related to a 0-star vehicle based on the findings of (Arup, 2017), as described in section 10.2.1 (77% to 88% reduction in risk). Intermediate levels were estimated based on linear interpolation between the two.

Robinson et al. (2016) found that only between around 30% and 50% of cyclists killed in collisions with left turning HGVs would be in a position to be visible in direct vision at the critical moment that the driver needed to see them in order to avoid a collision. Thus, the effectiveness of direct vision estimated for moving off from rest was factored by this proportion at each level, in order to account for the reduction in the scope of benefits. While for direct vision the proportion could have been applied more simply to the ‘do nothing’ casualty populations, this would have prevented assessment of other measures that could potentially influence all left turn collisions.

The interpretation of the study by Arup suggested the effectiveness of direct vision in left turn collisions would be between 0% and 20% (Arup, 2017). This implies that there are more factors influencing the difference in effectiveness between the moving off from rest manoeuvre and the left turn manoeuvre than just the number of collisions involving a dynamic manoeuvre with a high speed approach from the rear compared with one where the cyclist is around the front of the cab for the whole lead up to the collision. It is considered that this is likely to be related to the higher driver workload in the more complex left turn manoeuvre. Simply applying a range of 0% to 20%, in line with (Arup, 2017), to the left turn collisions resulted in small differences when compared to predicted values for past years. Thus, an arbitrary effectiveness reduction factor to account for additional complexity was introduced. The value of this factor was iteratively tuned until it calculated correctly the observed data for 2011-15, within a small tolerance. This resulted in a final estimate that a 5-star vehicle in direct vision would have a risk of left turn VRU fatalities per vehicle between 19% and 22% lower than for a 0-star vehicle. The risks for serious and slight casualties were similar but varied slightly to account better for variations in the baseline numbers and to ensure that past data was back-calculated correctly.

The effectiveness of blind spot safety systems (detection and warning) was considered on a similar basis, but lesser evidence of effectiveness was available.

When moving off from rest is considered, the draft proposals would require a motion inhibit system (Knight et al., n.d.). IIHS research on passenger cars shows that AEB that does not rely on driver response and will intervene for the driver, is more effective than forward collision warning alone for example (IIHS, 2012). Direct vision was considered highly effective for this situation but still relied on the driver behaving correctly in all situations. Assuming the same intervention vs warning premium applied, it was assumed that the blind spot safety system at category F would be even more effective than direct vision (85% to 95%).

However, Knight et al. (n.d.) found that the systems they tested had either a very low detection range or would detect a range of non-VRU objects at the side of the road such that the warning would be very frequent. There was clear evidence of relatively poor HMI, not in compliance with ISO standards intended to promote good practice and in one instance this had culminated in deliberate sabotaging of the device fitted to one vehicle

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hired in from a commercial supplier (as FORS compliant). Thus, it was considered that a Category A system would offer only small benefits (5% to 10%) despite the relatively simple collision situation.

When left turns are considered it was assumed that the same left turn manoeuvre complexity factor would reduce the effectiveness compared to moving off from rest. However, in this instance all left turn to cyclist collisions would be in scope, whether occurring dynamically as cyclists approached fast from the rear or when both parties moved off simultaneously. Thus, the reduction to account for the different collision types in direct vision was removed. This resulted in a category F system having an effectiveness of 38% to 46% but a category A system just 2% to 5%. Intervening systems were estimated using linear interpolation between the best and worst systems.

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11 Forecasting the future casualty effects of the policy options

11.1 Overview of the model

The target populations of collisions that may be influenced by the proposed policies are those where a pedestrian or cyclist is injured in collision with the front of an HGV that is moving off from rest or the side of an HGV that is turning, and have been defined in detail. Data relating to the number and severity of such casualties has been derived from STATS19 records over several years. The model therefore extends this work and forecasts the expected number of casualties over the evaluation period, on the basis of no new policies being implemented (do nothing).

Vehicles at all levels of direct vision performance already exist. The effectiveness of direct vision in improving outcomes has been assessed by considering the risk posed by different vehicle types with different vision capabilities in terms of the number of relevant casualties per unit of exposure (per registered vehicle or per vehicle km). This has been combined with experimental research considering the benefits of direct vision and the benefits of other technologies capable of influencing the same collision types. In this way the effect of direct vision can be directly linked to the exposure (registered vehicles entering London, or vehicle km in London) such that the casualty effect is directly proportional to the changes brought about in the vehicle fleet by each policy option. This change in the fleet is a common element directly linked to both casualty reduction and business impact.

The raw data inputs used in this casualty analysis have covered the period 2011 to 2015. Therefore, the forecast period runs from 2016 to 2030 inclusive. 2016 is a forecast year because, whilst it has chronologically passed, the casualty data for 2016 have not yet been released by the DfT. The period of forecast is only up to 2030 because extending the model any further would be technically unrealistic.

This model is representing today’s HGV parc and making forecasts based on direct vision and blind spot technologies. However, it is not attempting to model changes in powertrain toward electric vehicles, nor changes in security, connectivity or automation. An automated truck would be fitted with a range of sensors and systems that can interpret the environment and any potential collision partners, and apply automated responses to avoid the collisions. They may include an improved direct vision facility for the drivers, but the drivers would likely not be required to drive for large sections, if not all, of the journey. Automation therefore might drastically change the collision frequency and types, and this currently model is not attempting to model those changes. By 2030 some automated trucks might be in the vehicle parc, however only in small numbers, which is why the model can extend as far as 2030. After that point, it would require a much more extensive model to account for the changing technological and automation trends, and that is outside of the scope of this research.

11.2 Baseline scenario “Do Nothing”

A ‘do nothing’ Policy option is often included as the baseline for a regulatory impact assessment and represents a situation where no new policies are applied in future. Policies that have been implemented in the past but have not fully penetrated the market will

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continue to have an effect until market saturation. Thus, it is typically formed by projecting past trends forward at least in the short term and in the long term reverting to follow underlying drivers. TfL stated that they did not consider ‘doing nothing’ to be a viable policy option and considered that the minimum they will do is to implement the direct vision standard in best practice regimes and purchasing policies. This was considered the ‘do minimum’ option and as such, they did not wish a ‘do nothing’ option to be explicitly considered.

However, even the ‘do minimum’ option, represents a departure from the practice of the past and present and, therefore, from the empirical casualty and exposure data of the past. Thus a projection of past trends is not a valid estimate of the casualties expected in a ‘do minimum’ scenario. Estimating what the future vehicle parc and casualty record will be under the ‘do minimum’ scenario requires exactly the same type of data and calculations that is required for the main two policy options.

It is, therefore, an essential part of the modelling process to include a baseline ‘do nothing’ option where the future forecasts can be based on simple assumptions of continuing past trends. This projection allows the casualties under ‘do minimum’ to be quantified.


The main measure used within the model for exposure to risk is the number of unique vehicles recorded by ANPR cameras in the GLA area each year. TfL provided data for the most recent period available. Historic data was not available for trend analysis. Thus, in the do-nothing assumption it was assumed that there had been no change in the total number of vehicles or in their distribution by direct vision rating over the period of past years reviewed. It was also assumed that this trend would continue in future such that there was no temporal change.

11.2.2 Casualties

Casualty numbers for the target populations of casualties, as defined in section 3 were combined with DfT traffic forecasts (Department for Transport, 2016). These traffic forecasts are made using 5 scenarios, of which numbers 4 and 5 are proposed (for various reasons, detailed in the DfT guidance) to be the most significant predictors for the growth of freight traffic (LGV and HGV). These 2 scenarios are based on combinations of oil price and GDP, as illustrated in Table 11-1.

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Table 11-1: Traffic growth scenario factors. Source: (Department for Transport, 2016)

The forecasts for London, for both rigid and articulated HGVs, are shown in Table 11-2.

Table 11-2: London HGV traffic growth projections. Source: (Department for Transport, 2016)

Billion miles 2010 2015 2020 2025 2030 2035 2040

Scenario 4 Rigid 0.48 0.47 0.43 0.42 0.42 0.41 0.41

Artic 0.15 0.15 0.15 0.16 0.17 0.18 0.19

Scenario 5 Rigid 0.48 0.47 0.53 0.56 0.59 0.63 0.68

Artic 0.15 0.15 0.18 0.20 0.22 0.23 0.26

The target population figures were combined with the traffic figures to calculate a casualty rate per billion vehicle miles. This rate was averaged for 2011 to 2015 because there was considerable annual scatter in the results and no reliable trend to suggest risk was increasing or decreasing. Thus, the future projections assumed a casualty rate per billion vehicle miles equal to the average for 2011 to 2015 and multiplied that by the forecast number of vehicle miles (Department for Transport, 2016) to get an estimated absolute number of casualties.

Figure 11-1 shows the actual number of VRUs recorded within the target population for the years 2011 to 2015 and then shows the resulting forecast of VRU target population casualty numbers (absolute numbers not rates per km). Figure 11-2 and Figure 11-3 show the actual and forecast data for seriously and slightly injured VRUs, respectively.

It is important to note that the corridors of forecast casualty numbers is where it would be expected that the future number of casualties will lie, on average. Collisions are relatively rare and their occurrence is influenced by a large range of contributory factors. Thus, where collision numbers are low they tend to display a large element of random annual variation, as seen in the actual data. Thus, the actual number of casualties in any individual future year may well lie outside the corridor predicted but should not, in isolation, be considered

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evidence that the prediction was wrong, until sufficient time has passed to show that the average lies outside the corridor.

Figure 11-1: Actual and forecast numbers of fatally injured VRU in the population of collisions targeted by direct vision.

Figure 11-2: Actual and forecast numbers of seriously injured VRU in the population of collisions targeted by direct vision.

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Figure 11-3: Actual and forecast numbers of slightly injured VRU in the population of collisions targeted by direct vision.

11.3 Baseline scenario 2 “Do Minimum”

In this scenario, it was assumed that TfL would publish the direct vision standard and would incorporate it into the FORS and CLOCS voluntary initiatives and they would use it within their own commercial contracts for major construction works such that the industry was commercially encouraged to choose a vehicle with the best direct vision standard that was operationally capable of doing the job.

Thus, it was assumed that from 2018 operators would start to choose to upgrade the direct vision performance of their vehicles. It was assumed that the initiative would build momentum over time such that the proportion of people choosing to upgrade would increase each year. The proportion upgrading was considered to be highest for those operating the lowest vision standard vehicles. It was also considered that the biggest proportion of those upgrading would upgrade to the next vision standard up, a modest step, but that some would jump levels such that a small proportion would upgrade direct to a 5-star vehicle. Of course, all of the small proportion upgrading an existing 4-star vehicle would upgrade to a 5-star. The resulting distribution of vehicles depends of course, also on the technique used to map vision ratings to the current vehicle population, such that an extreme range and a range based on the more limited sample of ‘most sold’ data are shown in Figure 11-4, below.

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Figure 11-4: Projected vehicle numbers by vision rating: Do Minimum

In addition to this, it was also assumed that the blind spot safety systems test procedure developed by TfL would be used in a similar voluntary/best practice manner to produce increases in the fitment and quality of devices. Information provided by TfL suggested that currently fitment of devices was around 30%, so an initial range of 25% to 35% was modelled. In the do minimum scenario it was assumed this level of compliance would increase to around 40% linearly by 2030.

The quality of systems was assumed to be relatively low initially, with 80-90% falling into the lowest quality rating (A) and no systems available at levels D to F. Due to the general trend in technology with OEMs beginning to release proprietary systems and aftermarket suppliers responding to the introduction of the procedure, it was assumed that that would improve such that by 2030 only 15% to 50% would be in the lowest category (A) and that 37% to 76% would be in the D to F categories.

11.4 Policy Option 1 “Outright restriction”

The evidence around what the industry would do in response to the ban was provided by Jacobs after their consultations with industry. The basic message from this was that as soon as the ban was confirmed, any operator purchasing a new vehicle as part of the natural replacement cycle would buy a 3-star vehicle because the life of the vehicle would likely be such that it may still be in use at the time that 0- to 2-star vehicles were banned. Thus, a proportion equivalent to the new sales of vehicles each year was subtracted from the 0-star total and added to the 3-star total from 2018 to 2020.

What the industry would do in 2020 in response to the ban would depend on how many of their vehicles were affected. If it was a small proportion of their national fleet they would transfer compliant vehicles from outside London. If this was not possible they would replace

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vehicles early. However, if the proportion of their fleet affected was too large several of them might withdraw from the market.

For the purposes of the casualty impact assessment, it has been assumed that the economic principle that the market would supply the need is correct. That is, it has been assumed that any operators leaving the market would be replaced by other operators prepared to invest in the necessary vehicles such that there was no change to the total number of vehicles operating in London.

Thus, in 2020 it was assumed that all remaining 0-star vehicles would leave the London parc. A small proportion would be replaced with 1- and 2-star vehicles where, in the interim, those that were able to, transferred vehicles from other parts of the country. However, the majority of 0-star vehicles would simply be replaced by 3-star vehicles.

In 2024, it was assumed that all remaining 1- and 2-star vehicles would be replaced by 3-star vehicles. In the context of an outright restriction, it was considered that this might reinforce a ‘compliance culture’ where operator behaviour is dominated by compliance with legal requirements at minimum cost. Thus, it was assumed that no optional additional uptake of 4- or 5-star vehicles would occur. The net result of this action is illustrated in Figure 11-5 below.

Figure 11-5: Projected vehicle numbers by vision rating: Outright restriction

The policy introducing an outright restriction does not contain any requirements in relation to blind spot safety systems (detection and warning) or other possible elements of a safe system of work. However, it was assumed that it would not replace planned actions around using the rating scheme for detection and warning systems to encourage operators to choose more effective systems and for suppliers to design more effective systems. It was

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therefore assumed that fitment and quality of such systems would remain exactly as in the ‘do minimum’ option.

11.5 Policy Option 5 “HGV safe system scheme”

The basic premise of the HGV safe system scheme is that it is very much cheaper to install systems such as a blind spot detection and warning system than it is to replace a whole vehicle with one with better direct vision. However, it was identified in the do minimum scenario that there are operators who are beginning to apply risk management techniques to road transport that are similar to those applied on construction sites. The rationale for this is that they have recognised that if they suffered the number of fatalities on a construction site that they suffer as road deaths in their supply chain, they would risk site closure. If this can be reinforced with commercial incentives through contracts then this could lead to substantial ‘voluntary’ adoption of vehicles with better vision. It was therefore assumed that the voluntary upgrading of vehicles to those with better vision would be the same as in the “do minimum” scenario. All those that did not do this, or upgraded to less than 5-star vehicles would be forced by the HGV safe system scheme to comply with the safe system of work in relation to VRU close proximity collisions. For the purposes of this assessment this was assumed to mean fitting a blind spot safety system such that compliance increased from 30% to 100%.

The quality of blind spot system available was considered more likely to be linked to the availability of technology than to the take up rate within the market. This was, therefore, also assumed to remain as it was in the ‘do minimum’ scenario.

Depending on how the scheme is implemented, it is possible that participation could encourage best practice more than the measures outlined under the ”Do Minimum” scenario such that more operators voluntarily choose to adopt a 5-star vehicle in order to gain more of a commercial advantage with their clients than would be the case under do minimum. It is possible, that, if viewed as positive incentive, this policy option could encourage acceleration in the development of higher quality detection and warning systems and, in the future, possibly even full AEB systems intended for this type of collision. If so, the predictions from this model would prove conservative.

11.6 Overall results

In terms of interpretation of the model results, it is important to understand that if the “do minimum” scenario is considered to be the baseline option that will happen if one of the other new policy options is not selected, then the benefit of any other policy option is the difference in the number of casualties in that scenario, and do minimum.

The results derived are described as both ‘extremes’ (best and worst case) and ‘most sold’ heights. The difference between these two approaches has only a very small effect on the number of casualties. This is because whatever the actual mix of vehicles on the road, that mix produces the current casualty rate. The predictions of effectiveness by vision rating have to be adjusted to account for the differences in the base population. Effectively the future forecast is verified by ensuring when applied to previous years it comes up with the right answer, within a close tolerance. In addition to this, the revised vehicle population

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distribution by vision influences all policy options. One option is then subtracted from the other, so that most of the difference cancels out.

Thus, the extreme results for the policy options assessed are shown in Table 11-3 and Table 11-4, whilst the results for the ‘most sold’ approach (which are very similar) are summarised in Table 11-5.

It can be seen that over the assessment period, the total cumulative casualty prevention effects of the HGV safe system scheme approach are slightly less than those of the outright restriction. Comparing individual years of data shows that in the early years the advantage of the outright restriction approach is much greater than the safety system permit, the latter producing around half the benefit in 2020 compared with the former. This reflects the unavoidable nature of the legislative approach bringing the benefits of full compliance forward.

However, it can also be seen that in the later years, the safe system approach is actually predicted to offer slightly more benefit than the outright restriction. This reflects the fact that it increases fitment of technologies that can potentially mitigate a larger proportion of all the relevant crash types, principally left turns. It also reflects the assumption that it will help foster more of a best practice approach where operators will see commercial competitive incentives to exceed the minimum standard but that this behavioural change will take time to build momentum.

Table 11-3: The effect of policy option 1 ‘outright restriction’ relative to the ‘do minimum’ option, based on extreme best/worst case mapping of vision rating to population data

As can be seen, the low numbers mean that that calculations have had to deal in fractions of casualties prevented, which in reality is not possible. Headline results should therefore be quoted in terms of whole numbers over the cumulative period. For example, policy option 1 will prevent between 4 and 18 fatalities by 2030.

Upper Upper Upper Lower Lower Lower Crash prevention value

Acc_Year Fatal Serious Slight Fatal Serious Slight Upper Lower

2018 -0.1 -0.1 -0.5 0.0 0.0 0.0 303,355-£ 55,206-£

2019 -0.2 -0.2 -0.9 0.0 0.0 -0.1 592,175-£ 102,331-£

2020 -1.5 -1.2 -5.3 -0.3 -0.1 -0.4 3,649,423-£ 622,992-£

2021 -1.5 -1.3 -5.6 -0.3 -0.1 -0.4 3,906,831-£ 657,776-£

2022 -1.6 -1.4 -5.9 -0.3 -0.1 -0.4 4,144,417-£ 686,287-£

2023 -1.7 -1.4 -6.1 -0.3 -0.2 -0.4 4,350,278-£ 706,881-£

2024 -1.9 -1.6 -7.0 -0.6 -0.3 -0.8 5,053,059-£ 1,385,690-£

2025 -1.8 -1.6 -6.7 -0.5 -0.3 -0.8 4,937,225-£ 1,348,034-£

2026 -1.7 -1.5 -6.4 -0.5 -0.3 -0.7 4,761,254-£ 1,304,919-£

2027 -1.6 -1.4 -6.0 -0.5 -0.3 -0.7 4,531,033-£ 1,247,678-£

2028 -1.5 -1.3 -5.6 -0.4 -0.2 -0.6 4,245,276-£ 1,176,635-£

2029 -1.4 -1.2 -5.1 -0.4 -0.2 -0.6 3,900,182-£ 1,091,641-£

2030 -1.2 -1.1 -4.5 -0.4 -0.2 -0.5 3,496,094-£ 994,601-£

Cumulative -17.9 -15.2 -65.5 -4.5 -2.4 -6.5 47,870,602-£ 11,380,672-£

Permit Ban: London VRU casualties HGV both manoeuvres combined

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Table 11-4: The effect of policy option 5 ‘HGV safe system scheme’ relative to the ‘do minimum’ option, based on extreme best/worst case mapping of vision rating to

population data

Table 11-5: Summary of cumulative effect of policy options relative to the ‘do minimum’ option, based on most sold low/high mapping of vision rating to population data

It is worth noting that these benefits should not be considered the total benefit of the safety improvements relative to past data because past data was, effectively, from the “do nothing” scenario in relation to direct vision. Thus, the “do minimum” scenario does have a benefit in its own right. Thus, the total effect of improvement in direct vision and blind spot safety systems compared to doing nothing are as shown in Table 11-6 below, based on the extreme best/worst case approach.



2018 0.0 0.0 0.0 0.0 0.0 0.0 18,358.52£ 1,738.79£

2019 0.0 0.0 0.1 0.0 0.0 0.0 41,546.12£ 3,627.73£

2020 -0.7 -0.7 -2.9 -0.1 -0.1 -0.2 1,776,473.42-£ 239,609.74-£

2021 -0.8 -0.8 -3.3 -0.1 -0.1 -0.2 2,081,895.85-£ 249,987.90-£

2022 -0.9 -0.9 -3.8 -0.1 -0.1 -0.2 2,397,236.24-£ 275,256.22-£

2023 -1.0 -1.0 -4.3 -0.1 -0.1 -0.2 2,761,689.02-£ 322,964.83-£

2024 -1.2 -1.2 -4.9 -0.2 -0.1 -0.3 3,186,068.32-£ 421,490.22-£

2025 -1.3 -1.3 -5.4 -0.2 -0.1 -0.4 3,595,195.31-£ 514,978.64-£

2026 -1.5 -1.5 -6.0 -0.2 -0.2 -0.5 4,053,088.83-£ 608,796.25-£

2027 -1.6 -1.6 -6.6 -0.3 -0.2 -0.5 4,495,860.14-£ 725,273.79-£

2028 -1.7 -1.8 -7.2 -0.3 -0.2 -0.6 4,905,806.36-£ 845,943.03-£

2029 -1.8 -1.9 -7.6 -0.4 -0.2 -0.7 5,282,765.14-£ 990,772.13-£

2030 -1.9 -2.0 -8.0 -0.4 -0.3 -0.8 5,607,501.49-£ 1,129,830.13-£

Cumulative -14.4 -14.7 -59.9 -2.4 -1.6 -4.7 40,083,675-£ 6,319,536-£

Permit Safe System: London VRU casualties HGV both manoeuvres combined


Fatal Serious Slight Fatal Serious Slight Upper Lower

1-Outright Restriction -15.9 -13.4 -57.6 -7.2 -4.0 -10.8 42,640,198-£ 18,220,905-£

5-HGV Safe System Scheme -14.3 -14.6 -59.5 -2.5 -1.7 -4.8 39,974,537-£ 6,528,854-£

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Table 11-6: The effect of ‘do minimum’ relative to the ‘do nothing’ option, based on extreme best/worst case mapping of Vision rating to population data

Compared with past data, or more accurately the forward forecast based on doing nothing, the total benefit of policy options 1 and 5 can be added to the benefits of the do minimum option.

TfL’s guidance on assessing the impact of transport policies dictates that they should be based on the health and safety principle that risks should be reduced to a level as low as reasonably possible (the ALARP principle). They consider that this principle requires safety measures to be implemented unless the cost is disproportionately greater than the safety benefit obtained. Thus, they recommend assessing policies based on standard valuations of the prevention of casualties but if this fails to exceed a benefit to cost ratio in excess of 1 then they should test the case again using a casualty prevention value multiplied by 3. If this does have a benefit to cost ratio in excess of 1 then the case should be examined in detail to assess which valuation is most appropriate.

The revised valuations according to the ALARP multiplier of 3 are presented in Table 11-7, below, for each policy option.

Table 11-7: Summary of casualty prevention values for each option when using the ALARP multiplier of 3

Casualty prevention

value: Upper Casualty prevention

value: Lower

Relative to ‘Do Nothing’

‘Do Minimum’ -£ 78,822,744 -£ 16,868,059

Relative to ‘Do Minimum’

Outright restriction -£ 143,611,806 -£ 34,142,017

HGV safe system scheme -£ 120,251,026 -£ 18,958,609



2018 0.0 0.0 -0.1 0.0 0.0 0.0 100,116.60-£ 28,581.94-£

2019 -0.1 -0.1 -0.2 0.0 0.0 0.0 181,033.91-£ 40,450.99-£

2020 -0.1 -0.1 -0.4 0.0 0.0 0.0 328,489.58-£ 56,587.83-£

2021 -0.2 -0.2 -0.7 0.0 0.0 0.0 527,365.63-£ 78,962.94-£

2022 -0.3 -0.3 -1.1 0.0 0.0 -0.1 770,980.56-£ 117,345.68-£

2023 -0.4 -0.4 -1.6 -0.1 0.0 -0.1 1,084,986.53-£ 178,151.16-£

2024 -0.6 -0.5 -2.1 -0.1 -0.1 -0.2 1,485,472.40-£ 281,741.04-£

2025 -0.7 -0.7 -2.8 -0.2 -0.1 -0.3 1,947,156.10-£ 394,655.20-£

2026 -0.9 -0.9 -3.5 -0.2 -0.1 -0.3 2,511,689.63-£ 522,015.61-£

2027 -1.1 -1.1 -4.4 -0.3 -0.2 -0.4 3,157,250.73-£ 680,483.54-£

2028 -1.4 -1.3 -5.4 -0.3 -0.2 -0.5 3,883,067.37-£ 859,395.13-£

2029 -1.6 -1.6 -6.4 -0.4 -0.2 -0.7 4,697,823.76-£ 1,075,346.73-£

2030 -1.9 -1.8 -7.6 -0.5 -0.3 -0.8 5,598,815.16-£ 1,308,968.69-£

Cumulative -9.4 -8.9 -36.3 -2.1 -1.2 -3.5 26,274,248-£ 5,622,686-£

Do Minimum: London VRU casualties HGV both manoeuvres combined

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12 Un-monetised risks

Limitations in the available data make it difficult to quantify and monetise all factors that the direct vision standard might influence. Where such quantification has not been possible it has been considered an un-monetised risk that may be positively or negatively affected by the policy options. The influence of the risks identified have been assessed qualitatively in this section

12.1 Consideration of uncertain collision types

The analysis of collisions identified some collision types that were ambiguous. For example, 4 of 17 fatal pedestrian collisions where the colliding HGV was considered to have suffered a blind spot occurred when the HGV was moving off from rest but the collision was at the nearside. It is possible that these collisions were in fact the same type of crossing collisions as when the impact point was in front of the vehicle but slightly misinterpreted by the reporting police officer. If this is the case, the target population will be underestimated by up to around 25%.

It is equally possible that the blind spot contributory factor has been coded in error of that this describes different types of collision that would not be influenced by the direct vision standard. For example, experience in in-depth collisions in the HVCIS project (Knight et al., 2006) suggests that HGVs do suffer a range of unusual collisions. For example, a mechanic underneath the nearside wheel fixing something when the vehicle moves off from rest, or a person on the load bed securing the load that falls off to the nearside when the vehicle moves off from rest.

While the target population used may well be correct, these ambiguous collisions coded as ‘blind spot’ may mean that the baseline numbers and, therefore, the benefits are slightly underestimated.

12.2 Consideration of growth in cycling and walking

The study necessarily required the assumption of constant numbers in the HGV parc in London because of a lack of data. The analysis of geography also showed that there would be a strong link between VRU numbers and collision risk. However, there is potentially a strong interaction between the perception of VRU safety and the amount of VRU traffic, which in turn will influence the actual safety level and the perception of safety. Insufficient information exists to model this accurately at this time. For this reason, the model also assumed static VRU traffic.

TfL have forecast strong growth in pedal cycle traffic, though this may in part assume improved safety in a circular argument. Although in reality the relationship between traffic levels and the number of casualties is complex, it is usually assumed to be directly proportional at least over small changes in the level. For example, if in the year 2030 it was forecast that pedestrian and cycle traffic would be 30% higher than the average for 2011 to 2015, then the ‘do nothing’ forecast of casualties would be 30% higher for that year. This would also translate directly to a 30% increase in the forecast casualty reduction for that year.

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12.3 The assumption that ‘the market will provide’

The main analysis has been based on the economic principle that where a demand exists, the free market will respond to meet that demand. The final vision rating data from Loughborough Design School was based on the use of rating boundaries that classified the majority of N3 HGV models available as less than 3-stars in all their configurations. In policy option 1, where an outright restriction will be in place, the demand for compliant vehicles (level 1+ in 2020 and level 3+ in 2024) will be strong. With 6 years for the vehicle manufacturing industry to respond to the likely demand by increasing production capacity for models that can already comply, modifying existing models to become compliant or introducing new models, then there is a reasonable chance that the assumption will prove true.

However, in the do minimum scenario and the HGV safe system scheme, the forecast upgrading of vision performance is effectively voluntary. Although some of the voluntary upgrading is from a rating according to ‘The Standard’ of 0 to 1, 1 to 2, etc. where vehicle models are available if the right height specifications are chosen, upgrades to level 3+ based on the current portfolio of measured vehicles would suggest only three suitable models would be available. The demand in a voluntary situation would be lower volume and potentially more fragile than in a ban situation. There is, therefore, a risk that the assumption that ‘the market will provide’ will not hold true in this case. If this risk occurs, then it might mean that the benefits of the ‘do minimum’ scenario relative to doing nothing would be decreased. Assuming that the extra demand of the outright restriction would see the market responding, the benefits of the ban relative to ‘do minimum’ and the ‘HGV safe system scheme’ would be increased.

12.4 The effect of TfL’s policies on other cities/regions.

If TfL implements policy option 1, the analysis suggested that between 2020 and 2024 some operators that had a mixed nationwide fleet of vehicles at vision level 0, 1 and 2, would transfer compliant vehicles to London from other areas and non-compliant vehicles from London to other areas. This represents a safety benefit in London but a safety dis-benefit in the other areas.

Overall, it was estimated that this would be a relatively small proportion of vehicles (see section 11.4) and only for the time between 2020 and 2024. In addition to this, the magnitude of dis-benefit elsewhere will depend on where the compliant vehicle is taken from. If, for example, it was taken from an agricultural task in a rural area where it did not interact with many VRUs then it is likely the safety gain in London would exceed the safety loss in the other area. This may also hold true to a lesser extent even in other cities. This is because the evidence suggests the collision rate per HGV kilometre or per VRU kilometre is higher in London than in other comparable city regions.

In addition to the transferring of vehicles, operators that replace vehicles that they use in London would naturally replace them with vehicles with a vision rating compliant with the 2024 requirement (3+ star). Vehicles will be bought to a London specification if there is a reasonable prospect it will need to enter London on any kind of regular basis. However, this

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does not mean it will be exclusively used in London. When used in other areas where it interacts with VRUs, that region will see the associated safety benefit of London’s policy.

In addition to this, TfL’s development of the direct vision standard has already helped to initiate proposals to include minimum standard of direct vision within European type approval regulation. If these proposals are implemented, which looks likely at this stage, then TfL’s policy will have had benefits across Europe and in other markets that buy European specification vehicles.

12.5 Safety dis-benefits in inter-urban highway traffic

Although many features will influence direct vision performance, achieving a higher vision rating will tend to mean a lower mounting height for the vehicle cab, particularly in the short term between scheduled major re-designs of cab ranges. Jacobs reported that stakeholders cited concerns that a tall cab and a high vantage point increased safety outside of city driving because of the more commanding view that it enabled. This is consistent with survey responses reported by (Arup, 2017) where it was apparent that drivers felt safe in a high cab. Similar views have been expressed in the field of passenger cars when considering the views from SUVs compared to smaller cars.

While there is evidence that drivers have a perception of increased safety in a tall vehicle, there is little, if any, objective evidence of a relationship with casualty rates. In the absence of this evidence there are several factors that may need consideration:

Relative height: It is possible that the perceived advantage comes not from the absolute height of the vehicle but the height of the eye point relative to that of other road users. That is, it is being able to see over the top of other vehicles that drivers perceive to be of benefit. Thus, if a policy promotes lower vehicles more generally, it is possible that the effect on the ability to see over other vehicles will not be greatly affected. Even in an HGV rated by ‘The Standard’ as 5-star vision, the driver will be able to see over the top of passenger cars. There will be more models of HGV and bus that it cannot see over any more but those models will be discouraged by the standard and will therefore be fewer in number. If all HGVs were 5-star, there might be no disadvantage.

The effect on smaller vehicles: While a very tall vehicle may be perceived to be safer by its driver in highway driving because it enables a better view, that increase in height has the potential to further degrade the view of the smaller passenger cars around it. For example, HGVs can obscure roadside signs from the view of a passenger car overtaking in a second or third lane. A taller HGV would increase the distance at which the obstruction can occur. This could make sudden late reaction, for example, to motorway exits, more frequent with a consequent increase in collision risk.

In summary, there is a risk that high vision vehicles that perform well in cities may perform less well in inter-urban driving but there is little objective evidence and the issue is far from clear cut. It is also at least theoretically possible that the benefits to car drivers on highways of decreasing the height of HGVs could outweigh any dis-benefit to HGV drivers, such that the same issue represented a further net safety benefit.

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13 Conclusions

Overall, the research undertaken and the projections, forecasts and calculations made lead to the following main conclusions:

1) On average, the total number of VRU collisions in London relevant to direct vision from goods vehicles is relatively small but this is subject to considerable annual variation.

2) London appears to suffer from the particular problem of collisions between VRUs and HGVs during low speed manoeuvres disproportionately. Analysis of the collision rate per VRU kilometre suggests this is not solely because of a higher density of VRUs in London.

3) There is considerable evidence that similar nearside turn problems occur across Europe, despite the presence of large differences in infrastructure, including, for example, segregated cycle lanes. There is also evidence of problems with HGVs moving off from rest, but in other EU countries this may be of a lesser priority when compared to GB/London.

4) In quantifying the vehicles and casualties affected by proposed restrictions on the use of HGVs with poor direct vision, there is considerable technical uncertainty in all areas. For this reason, ranges of values have been used throughout the analysis.

5) The range for casualties considers a simple definition based on manoeuvre, impact point and the presence of the contributory factor ‘blind spot’ as the low estimate, and a high estimate that excludes the contributory factor blind spot because of concerns around reliability and includes uplifts to account for under-reporting in STATS19. This resulted in an estimated range of 4 to 6 fatalities per year within the target population, on average, for the period 2011 to 2015.

6) Loughborough Design School provided vision ratings made in accordance with ‘The Standard’ for a range of HGV makes and models representing slightly less than half of all vehicles registered in 2015 and about three-quarters of all new vehicles at that time. Severe limitations in the data collected by DVLA meant that it was impossible to link this data directly to data from ANPR cameras in London. With national SMMT data it was possible, but remained difficult because of a lack of information about the height of HGVs in service. For these reasons, extreme ranges were used as a baseline and a narrower, but less reliable, range was used based on a small sample where the ‘most sold’ height of HGV variants were known. Average ratings by vehicle type were defined at GB level and assumed to apply in London.

7) The result was an estimate that a large proportion of the current vehicle fleet would be rated 0-star and, therefore, affected by any policy options that implemented a ban in the short term.

8) Emerging information from other activities undertaken by TfL clearly highlighted that the costs would therefore be orders of magnitude higher than the benefits, such that a number of initial policy options were revised. The casualty impact assessment focussed on the core policy option relating to a ban and a second option to operate an ‘HGV safe system scheme’ that still permitted vehicles with poor direct vision performance within London, but only if they proved they used other safety systems to compensate.

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9) The baseline for TfL was a ‘do minimum’ assumption where, even if no formal restriction was in place, the standard would be used to encourage best practice through existing schemes such as FORS and CLOCS and commercially through TfL’s own contracts. This alone was predicted to have significant benefits compared to doing nothing, potentially preventing a total of 2 to 9 fatalities and 1 to 9 seriously injured casualties by 2030 at a casualty prevention value of between around £5m and £27m.

10) The two policy options were measured against the ‘do minimum’ option and their benefits are, therefore, in addition to those of the ‘do minimum’ option.

a) An ‘outright restriction’ banning 0-star HGVs from 2020 and 0- to 2-star HGVs from 2024 would likely prevent a total of 4 to 18 fatalities and 2 to 15 serious casualties by 2030, at a value of £11m to £48m

b) An ‘HGV safe system scheme’ was estimated to prevent 2 to 15 fatalities and 2 to 15 serious casualties by 2030, at a value of £6m to £40m.

11) Although the effects of both systems are similar on a cumulative basis to 2030, this masks substantial differences in the timings of effects. The ‘outright restriction’ was expected to promote a compliance culture where few operators voluntarily exceeded the minimum standard because of the additional costs it forced them to bear. Thus, the benefits of the ban came early in the analysis period but did not increase with time. The ‘safe system’ approach was considered more likely to promote a best practice approach and competition between suppliers of safety equipment to produce the best. Thus, the benefits were found to be considerably lower earlier in the assessment period but to increase over time as the quality of the ‘safe system’ increased. By 2030, the annual benefits of the safe system approach were predicted to be higher than for the ban.

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TfL (2013). Safe Streets for London. Transport for London: London.

Volvo (2017). Volvo trucks safety report 2017. Volvo Trucks: Gothenburg.

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Appendix A Selected STATS19 field definitions

Code Vehicle manoeuvre Code Vehicle manoeuvre

1 Reversing 10 Waiting to turn right

2 Parked 11 Changing lane to left

3 Waiting to go ahead, held up 12 Changing lane to right

4 Slowing or Stopping 13 Overtaking moving veh. on its o/s

5 Moving off 14 Overtaking stationary veh. on its o/s

6 U-turn 15 Overtaking on nearside

7 Turning left 16 Going ahead - LH bend

8 Waiting to turn left 17 Going ahead - RH bend

9 Turning right 18 Going ahead - other

Table 14: STATS19 manoeuvre codes

Code First point of impact

0 Did not impact

1 Front

2 Back

3 Offside

4 Nearside

Table 15: STATS19 first point of impact codes

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Appendix B Comparing London with the UK

In order to define areas of the UK that could be compared to London in terms of VRU

casualty rates, various methods of defining cities, conurbations, administrative districts etc

were examined. The existence of common transport policies was also considered. The

comparison systems considered were:

Metropolitan counties

There are 6 metropolitan counties, which include within them a number of cities and towns.

These were established in 1974 and, although their county councils were abolished in 1986,

they still exist legally and the boundaries are used for some administrative purposes. There

are separate transport policies in some of the subordinate areas, e.g. Leeds City Council and

Bradford Metropolitan District Council. The metropolitan counties are sometimes referred

to as ‘Former Metropolitan Counties’ (FMCs).

Ranked in order of population size (with London)24 they are:

1. Greater London 8,173,941

2. West Midlands 2,736,460

3. Greater Manchester 2,682,528

4. West Yorkshire 2,226,058

5. Merseyside 1,381,189

6. South Yorkshire (Sheffield) 1,343,601

7. Tyne & Wear (Newcastle) 1,104,825

Metropolitan areas (EU figures)25

24 2011 census

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The EU uses NUTS 3 levels to define areas of population, with the largest in the UK shown

as:

1. London 13,842,667

2. Manchester 3,247,384

3. West Midlands urban area 2,481,387

4. Glasgow 1,821,971

5. Liverpool 1,519,703

6. Leicester 1,388,354

7. Newcastle upon Tyne 1,160,109

8. Cardiff 1,125,588

9. Stoke-on-Trent 1,113,876

10. Bristol 1,108,998

11. Leeds 1,104,858

Clearly the relative sizes of these areas do not correspond with those of the metropolitan

counties, furthermore they do not include some outlying areas which are covered by nearby

urban transport systems.

Urban areas

These are defined and ranked by population by Demographia26 as:

25 http://ec.europa.eu/eurostat

26 http://www.demographia.com/

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1. London 10,470,000

2. Manchester 2,685,000

3. Birmingham 2,550,000

4. Leeds-Bradford 1,955,000

5. Glasgow 1,235,000

6. Southampton-Portsmouth 895,000

7. Liverpool 880,000

8. Newcastle 800,000

9. Nottingham 765,000

10. Sheffield 715,000

11. Bristol 660,000

Again, administrative districts are mixed and suburbs and other relevant areas are not

included.

Primary Urban Area PUA

A further definition is provided by the ‘centreforcities’ think tank27 . However, here London

is defined as 45 areas:

Barking and Dagenham, Barnet, Bexley, Brent, Bromley, Broxbourne, Camden, City of

London, Croydon, Dartford, Ealing, Elmbridge, Enfield, Epping Forest, Epsom and Ewell,

Gravesham, Greenwich, Hackney, Hammersmith and Fulham, Haringey, Harrow, Havering,

Hertsmere, Hillingdon, Hounslow, Islington, Kensington and Chelsea, Kingston upon Thames,

Lambeth, Lewisham, Merton, Newham, Redbridge, Richmond upon Thames, Runnymede,

Southwark, Spelthorne, Sutton, Three Rivers, Tower Hamlets, Waltham Forest,

Wandsworth, Watford, Westminster, Woking.

27 http://www.centreforcities.org/wp-content/uploads/2016/01/2016-PUA-Table.pdf

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These include several which are not part of the GLA or covered by TfL (e.g. Epping Forest,

Epsom and Ewell and Woking). The additional areas increase the population included by

some 14% over that covered by TfL.

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Appendix C Data on HGV manoeuvre, impact point and blind spots

The data presented in this annex relates only to HGVs in excess of 7.5 tonnes in collision with vulnerable road users in London

Pedestrian fatal injury 1st point of impact


Reversing 0 0 0 0 1

Parked 0 0 0 0 0

Waiting to go - held up 0 0 0 0 0



U-turn 0 0 0 0 0


Waiting to turn left 0 0 0 0 0


Waiting to turn right 0 0 0 0 0

Changing lane to left 0 0 0 0 0

Changing lane to right 0 0 0 0 0

Overtaking moving vehicle - offside 0 0 0 0 0


Overtaking - nearside 0 0 0 0 0




Total 1 22 1 1 10

Table F1: Pedestrian fatalities showing HGV manoeuvre and point of impact

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Figure F1: Pedestrian fatalities showing manoeuvre and point of impact (HGV only)

Pedal cyclist fatalities (HGV only)

Cyclist fatal injury 1st point of impact


Reversing 0 0 0 0 0

Parked 0 0 0 0 0




U-turn 0 0 0 0 0




02468

1012141618

First point of impact

Manoeuvre

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Cyclist fatal injury 1st point of impact











Total 0 1 0 1 16

Table F2: Pedal cyclist fatalities showing HGV manoeuvre and point of impact

Figure F2: Pedal cyclist fatalities showing manoeuvre and point of impact (HGV only)

02468

101214


Manoeuvre

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Motorcyclist fatalities (HGV only)

Motorcyclist fatal injury 1st point of impact


Reversing 0 0 0 0 0

Parked 0 0 0 0 0




U-turn 0 0 0 0 0













Total 0 1 0 1 1

Table F3: Motorcyclist fatalities showing HGV manoeuvre and point of impact

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Figure F3: Motorcyclist fatalities showing manoeuvre and point of impact (HGV only)

0

1

2

3


Manoeuvre

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Appendix D Location of injuries – pedestrians and pedal cyclists

Interactive versions of these maps can be found at:

https://drive.google.com/open?id=1Z9x93mbWaGDxx_8Tbncu7YDyd8w&usp=sharing

and

https://drive.google.com/open?id=1OGAHJ45LXUzLE6niO3TGMPj_wdQ&usp=sharing

Pedestrians - manoeuvre ‘moving off’

Fatal Serious Slight

All GLA 21 17

CCZ 0 0

Figure 104: Location of pedestrian serious injury collisions – ‘moving off’ manoeuvre

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All GLA 21 17 27

CCZ 0 0 3

Figure 105: Location of pedestrian slight injury collisions – ‘moving off’ manoeuvre

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Pedestrians - all manoeuvres


All GLA 49 101

CCZ 3 14

Figure 106: Location of pedestrian serious injury collisions – all manoeuvres

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All GLA 49 101 255

CCZ 3 14 49

Figure 107: Location of pedestrian slight injury collisions – all manoeuvres

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Pedal cyclists – manoeuvre ‘turn left’


All GLA 14 27

CCZ 4 11

Figure 108: Location of pedal cyclist serious injury collisions – ‘turning left’ manoeuvre

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All GLA 14 27 110

CCZ 4 11 22

Figure 109: Location of pedal cyclist slight injury collisions – ‘turning left’ manoeuvre

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Pedal cyclists – all manoeuvres


All GLA 24 74

CCZ 7 23

Figure 110: Location of pedal cyclist serious injury collisions – all manoeuvres

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All GLA 24 74 412

CCZ 7 23 94

Figure 111: Location of pedal cyclist slight injury collisions – all manoeuvres

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Appendix E Time of injuries – pedestrians and pedal cyclists

Pedestrians - moving off

Figure 27: Time of pedestrian injury collisions – moving off

0

2

4

6

8

10

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Serious

07:00 – 18:00: 76%

0

2

4

6

8

10

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Slight

07:00 – 18:00: 89%

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Pedestrians - all manoeuvres

Figure 28: Time of pedestrian injury collisions – all manoeuvres

0

2

4

6

8

10

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Serious

07:00 – 18:00: 79%

0

10

20

30

40

50

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Slight

07:00 – 18:00: 82%

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Pedal cyclists – turning left

Figure 29: Time of pedal cyclist injury collisions – turning left

0

2

4

6

8

10

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Serious

07:00 – 18:00: 81%

0

10

20

30

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Slight

07:00 – 18:00: 79%

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Pedal cyclists – all manoeuvres

Figure 30: Time of pedal cyclist injury collisions – all manoeuvres

0

5

10

15

20

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Serious

07:00 – 18:00: 70%

0

20

40

60

80

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Slight

07:00 – 18:00: 78%

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Appendix F Calculation of collision values - example

RAS60004

Total value of prevention of accidents by severity and road type £ million

Built-up roads Non built-up roads Motorways All roads

Fatal 1,386 1,651 204 3,241

Serious 3,075 1,370 159 4,604

Slight 2,081 624 159 2,865

RAS60002

Average value of prevention of reported road accidents by road type £


Fatal 1,922,917 2,066,360 2,121,965 2,005,664

Serious 221,054 248,472 258,769 229,757

Slight 22,880 27,598 32,964 24,194

Calculated number

Number of accidents


Fatal 721 799 96 1616

Serious 13909 5513 616 20038

Slight 90940 22624 4838 118402

RAS60003

Total value of prevention of reported accidents by severity and cost element £million

Lost output Medical and Ambulance Human costs Police costs Insurance and admin Damage to property

Fatal 1,073 9 2,107 32 1 19

Serious 528 317 3,599 46 4 110

Slight 387 164 1,846 70 15 383

Calculated cost

Average £

Lost output Medical and Ambulance Human costs Police costs Insurance and admin Damage to property

Fatal 664,237 5,708 1,303,841 19,502 327 12,049

Serious 26,360 15,822 179,616 2,286 204 5,469

Slight 3,272 1,388 15,589 590 124 3,231

= RAS60004 / RAS60002

= RAS60003 / Calculated

number

A Direct Vision Standard for HGVs

TRL

Crowthorne House, Nine Mile Ride,

Wokingham, Berkshire, RG40 3GA,

United Kingdom

T: +44 (0) 1344 773131

F: +44 (0) 1344 770356

E: [email protected]

W: www.trl.co.uk

ISSN

ISBN

CPR4037

mailto:[email protected]