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3 Counties Alliance Highways and other Professional Services

North Derbyshire

Highway Assignment Model

Traffic Forecasting Report

April 2012

North Derbyshire

Highway Assignment Model

Traffic Forecasting Report

DCC Scheme Ref No: N/A DCC Project Engineer: G Blissett

SW Scheme Ref No: D133513 SW Project Engineer: D J Elliott

Document No: D133513/4/008

Issue No Document Status Date Prepared By Approved

By Authorised for Issue

2 FINAL April 2012 LK Adam Hall David Elliott

Derbyshire County Council

Environmental Services Department

County Hall

Matlock

Derbyshire

DE4 3AG

URS Infrastructure & Environment UK Limited

Royal Court

Basil Close

Chesterfield

Derbyshire

S41 7SL

North Derbyshire Highway Assignment Model Derbyshire County Council Traffic Forecasting Report

D133513/4/008 i April 2012

SCHEDULE OF REVISIONS

Revisions Issued Since Publication

Working Paper Issue Number

Revision Date Paragraphs amended

2 23/02/2012 1.1.1

3 19/04/2012 Reforecasting to include Bolsover potential sites and new NED uncertainty log


D133513/4/008 ii April 2012

CONTENTS

1.0 INTRODUCTION 5

1.1 Overview ........................................................................................................... 5

1.2 Forecasting Requirements ................................................................................ 5

1.3 Structure of the Report ...................................................................................... 6

2.0 REFERENCE CASE METHODOLOGY 7

2.1 Future Year Highway Networks ......................................................................... 7

2.2 Trip Demand Forecasting .................................................................................. 9

3.0 REFERENCE CASE OUTPUTS 14

3.1 Assignment ..................................................................................................... 14

3.2 Convergence .................................................................................................. 17

4.0 CORE FORECAST METHODOLOGY 19

4.1 Core Scenario Highway Networks ................................................................... 19

4.2 Data Sources .................................................................................................. 19

4.3 Comparison of NTEM and committed developments....................................... 20

4.4 Core Scenario Matrix Building ......................................................................... 24

5.0 CORE FORECAST OUTPUTS 27

5.1 Assignment ..................................................................................................... 27

5.2 Convergence .................................................................................................. 27

5.3 Comparison against Base Year ....................................................................... 28

6.0 NETWORK ANALYSIS 29

6.1 Introduction ..................................................................................................... 29

6.2 Junction Capacity ............................................................................................ 29

6.3 Link Capacity .................................................................................................. 31

6.4 Core and Reference Case Comparisons ......................................................... 34

7.0 INVESTIGATION OF RESPONSES TO CHANGES IN TRAVEL COST 42

7.1 Investigation of Variable Demand Responses ................................................. 42

8.0 CONCLUSIONS 46


D133513/4/008 iii April 2012

TABLES Table 2-1 2016 PPM and PPK Values .................................................................................. 7

Table 2-2 2026 PPM and PPK Values .................................................................................. 8

Table 2-3 Fuel and Income Adjustment Factors .................................................................. 11

Table 2-4 AM Base Year and Reference Demand Matrix Totals ......................................... 12

Table 2-5 IP Base Year and Reference Demand Matrix Totals ........................................... 12

Table 2-6 PM Base Year and Reference Demand Matrix Totals ......................................... 13

Table 3-1 Worst 10 V/C Nodes – AM 2016 (over 85%) ....................................................... 15


Table 3-3 Worst 10 V/C Nodes – IP 206 (over 85%) ........................................................... 15

Table 3-4 Worst 10 V/C Nodes – IP 2026 (over 85%) ......................................................... 15

Table 3-5 Worst 10 V/C Nodes – PM 2016 (over 85%) ....................................................... 16


Table 3-7 Summary Convergence Statistics ....................................................................... 17

Table 4-1 Classification of Future Inputs ............................................................................. 19

Table 4-2 TRICS generation rates, peak hour (Veh/hr) ....................................................... 21

Table 4-3 NTEM and planned development, comparison of trip increases from 2010 to 2016 – Light Vehicles............................................................................................................ 21

Table 4-4 NTEM and planned development, comparison of trip increases from 2010 to 2016 – Heavy Vehicles ......................................................................................................... 22

Table 4-5 NTEM and planned development, comparison of trip increases from 2010 to 2026 – Light Vehicles............................................................................................................ 22

Table 4-6 NTEM and planned development, comparison of trip increases from 2010 to 2026 – Heavy Vehicles ......................................................................................................... 23

Table 4-7: AM peak hour Demand Matrix Totals ................................................................ 25

Table 4-8: Inter-peak average hour Demand Matrix Totals ................................................ 25

Table 4-9: PM peak hour Demand Matrix Totals ................................................................ 26








Table 7-1 Elastic Assignment Parameter Values - 2026 ...................................................... 43


Table 7-3 Elastic Assignment Trip Summary PM Peak (pcus) ............................................. 43

FIGURES Figure 1 – Link V/C greater than 85% – AM 2016 ............................................................... 31

Figure 2 – Link V/C greater than 85% – IP 2016 ................................................................. 32

Figure 3 – Link V/C greater than 85% – PM 2016 ............................................................... 33

Figure 4 – Link V/C greater than 85% – AM 2026 ............................................................... 33

Figure 5 – Link V/C greater than 85% – IP 2026 ................................................................. 33

Figure 6 – Link V/C greater than 85% – PM 2026 ............................................................... 34

Figure 7 – Core and Reference Case Comparison – AM 2016 (outer) ................................ 35

Figure 8 – Core and Reference Case Comparison – AM 2016 (inner) ................................ 35

Figure 9 – Core and Reference Case Comparison – IP 2016 (outer) .................................. 36

Figure 10 – Core and Reference Case Comparison – IP 2016 (inner) ................................ 36

Figure 11 – Core and Reference Case Comparison – PM 2016 (outer) .............................. 37

Figure 12 – Core and Reference Case Comparison – PM 2016 (inner) .............................. 37

Figure 13 – Core and Reference Case Comparison – AM 2026 (outer) .............................. 38

Figure 14 – Core and Reference Case Comparison – AM 2026 (inner) .............................. 38

Figure 15 – Core and Reference Case Comparison – IP 2026 (outer) ................................ 39

Figure 16 – Core and Reference Case Comparison – IP 2026 (inner) ................................ 39


D133513/4/008 iv April 2012

Figure 17 – Core and Reference Case Comparison – PM 2026 (outer) .............................. 40

Figure 18 – Core and Reference Case Comparison – PM 2026 (inner) .............................. 40

Figure 19 – Cell by Cell Matrix Change - PM Peak 2026 ..................................................... 44

Figure 20 – Number of Trips Induced/Suppressed - PM Peak 2026 .................................... 45

APPENDICES A Origin and Destination Factors – Light B Origin and Destination Factors - Heavy C Worst 10 V/C Nodes (Over 85%) Reference Case D Uncertainty Log – Chesterfield Borough E Uncertainty Log – North East Derbyshire District F Uncertainty Log – Bolsover District G Comparison of Core Scenario and Base Year Demand Flows H Core Matrix Changes – Network Plots I Worst 10 V/C Nodes (Over 85%) Core Scenario


D133513/4/008 5 April 2012

1.0 INTRODUCTION 1.1 Overview 1.1.1 A traffic model of North Derbyshire is being developed to allow informed decisions

on land use proposals as part of the emerging core strategies and assessment of private sector proposals likely to emerge at various Examinations in Public. It will allow the testing of potential development impacts and provide agreed and consistent traffic flow forecasts together with a robust basis upon which to develop mitigation strategies, allowing informed advice to both district councils and prospective developers.

1.1.2 The Model Scoping Report (ref: D133513/4/002) set out, in more detail, the purpose of the model and its likely uses.

1.1.3 The traffic model will be used as a tool to assess the impact of schemes and

developments. Development zones were allocated to specific locations to aid forecasting.

1.1.4 A total of 402 zones, of different sizes, are included in the North Derbyshire Highway Assignment Model. A further 27 zones have been added to represent areas targeted for development. All development zones are allocated zone names above 402.

1.1.5 The traffic model has been calibrated to represent the traffic conditions observed in 2010. This is the model’s Base Year. Further details of the base year model are found in the Local Model Validation Report (ref: D133513/4/007).

1.1.6 Forecasting methodology was established in the Forecasting Information Report

(ref: D133513/4/06).

1.1.7 The Department for Transport (DfT) provide guidance on the development of traffic models, which are published under a series of ‘WebTAG’ guidance notes. The forecasting approach for the Chesterfield Traffic Model was developed using the guidance provided in the WebTAG unit 3.15.1.

1.1.8 This report, the Traffic Forecasting Report, describes the development of the future

year matrices for assignment onto the highway network, and the results of these assignments.

1.2 Forecasting Requirements 1.2.1 There are two forecasting scenarios to consider:

Reference Case – This contains the background level of trip growth as defined nationally by the national growth forecasting policy. It excludes specific development such as those identified by the core strategy. The growth forecasting data is obtained through the National Trip End Model (NTEM).

Core Scenario – This contains all approved and planned schemes and developments in the modelled area and represents the growth expected by the district planning authorities.


D133513/4/008 6 April 2012

1.2.2 Forecast years have been agreed, by the Client, to be 2016 and 2026, i.e. the end of Derbyshire County Council’s Local Transport Plan period. The Forecast includes all the currently known Core Strategy development and, therefore, it will look at a scenario that reflects the end of the core strategy horizon (2031).

1.2.3 Forecasts have been prepared using a combination of known Core Strategy development and national growth forecasts (described in further detail in this report). It is noted that a large scale of core strategy development in Bolsover and North East Derbyshire exceed the levels of national growth to 2026. The impact of trips from development in the core strategy is therfor more significant than any assumption that might be made about the national growth of trips to 2026 (or 2031) (Refer to paragraph 4.3.10 for further commentary).

1.2.4 Forecasts were produced for three time periods; AM Peak, Inter Peak and PM Peak, for each forecast year. The AM Peak represents the hour 0800-0900, PM Peak represents 1700-1800 and the Inter Peak is an average 1 hour between 1000-1600.

1.2.5 The model examines three vehicle types; car, LGV and HGV. There are six user

classes, car trips are split into three purpose types (commuting, employers business and other), LGVs are split into work and non work. Growth factors are calculated for each user class individually.

1.2.6 The outputs from the forecasting process will include:

Assigned flows and over-capacity reports for the reference case.

An assessment of the highway impacts as a result of the Core Scenario.

A basis for option testing of alternative development assumptions and possible highway schemes.

1.3 Structure of the Report 1.3.1 A summary of each Chapter of this report is provided below:

Chapter 2.0 describes methodology used to produce Reference Case demands.

Chapter 3.0 details the outputs from Reference Case assignments.

Chapter 4.0 details the methodology used to produce the Core forecasts.

Chapter 5.0 presents the Core forecast assignment results.

Chapter 6.0 presents an analysis of the network operation with the Core forecasts.

Chapter 7.0 investigates the response to travel cost changes

Change 8.0 provides conclusions


D133513/4/008 7 April 2012

2.0 REFERENCE CASE METHODOLOGY 2.1 Future Year Highway Networks

Introduction

2.1.1 The future year SATURN network is updated from the 2010 base year highway

networks which were reported in the LMVR (D133513/4/007).

2.1.2 The future year networks, including the schemes detailed in this section, represent the ‘Do Minimum’ highway network. The ‘Do Minimum’ provides a future year baseline which the impacts of potential schemes can be assessed against. Generalised Costs

2.1.3 The perceived cost of travel is measured in terms of Generalised Cost (GC) where

GC is the sum of the travel time multiplied by a time parameter and the travel distance multiplied by a distance parameter. The distance and time parameters are known as pence per kilometre (PPK) and pence per minute (PPM) respectively. The formula for calculating the Generalised Cost of any trip is given in DMRB: Volume 12, Section 1, para. 9.2.5 as:

GC = at + bd +T

where: GC is the generalised Cost a is a coefficient in pence/minute (PPM) t is the travel time in minutes along a link b is a coefficient in pence/km (PPK) d is the travel distance in km T is an out of pocket expense (eg toll) 2.1.4 The time and distance costs (PPM and PPK) were derived using WebTAG 3.5.6

methodology, based on future years, and average network speeds.

2.1.5 The PPM and PPK values used for forecasting in the North Derbyshire Traffic Model are detailed below in Tables 2-1 and 2-2.

2016 PPM/PPK

PPM PPK PPM/PPK Ratio

AM IP PM AM IP PM AM IP PM

UC1 Car (Commute) 10.98 10.90 10.75 5.10 5.10 5.10 2.15 2.14 2.11

UC2 Car (EB) 49.59 48.52 47.77 9.95 9.95 9.95 4.98 4.87 4.80

UC3 Car (Other) 13.91 14.47 14.89 5.10 5.10 5.10 2.73 2.83 2.92

UC4 LGV (Work) 19.89 19.89 19.89 12.48 12.48 12.48 1.59 1.59 1.59

UC5 LGV (Non Work) 12.76 12.76 12.76 7.20 7.20 7.20 1.77 1.77 1.77

UC6 HGV (All) 16.57 16.57 16.57 30.86 30.86 30.86 0.54 0.54 0.54

Table 2-1 2016 PPM and PPK Values


D133513/4/008 8 April 2012

2026 PPM/PPK

PPM PPK PPM/PPK Ratio

AM IP PM AM IP PM AM IP PM

UC1 Car (Commute) 12.44 12.36 12.21 4.01 4.01 4.01 3.10 3.08 3.05

UC2 Car (EB) 58.16 57.00 56.04 9.06 9.06 9.06 6.42 6.29 6.19

UC3 Car (Other) 15.49 16.10 16.64 4.01 4.01 4.01 3.86 4.01 4.15

UC4 LGV (Work) 23.47 23.47 23.47 12.08 12.08 12.08 1.94 1.94 1.94

UC5 LGV (Non Work) 14.58 14.58 14.58 6.72 6.72 6.72 2.17 2.17 2.17

UC6 HGV (All) 19.56 19.56 19.56 31.93 31.93 31.93 0.61 0.61 0.61

Table 2-2 2026 PPM and PPK Values

Planned Highway Schemes

2.1.6 To update the network it is necessary to identify planned future schemes, which

may reasonably be expected to be implemented before the particular forecast (future) year, for which a network is being produced.

2.1.7 The M1 widening scheme between J25 and J28 was completed in Autumn 2010 and this scheme has been included in all of the 2016 and 2026 networks. This length of the M1 is now dual 4 lane motorway standard with hard shoulders (D4M).

2.1.8 The M1 between J28 and J31 is very likely to be converted to managed-motorway

with Hard Shoulder Running (MM-HSR). The development of the future year models assumed this scheme would be implemented before 2016 and therefore appears in the 2016 and 2026 networks.

2.1.9 Given that hard shoulder running schemes vary in speed by time of day, by their

nature, it was necessary to make some assumptions regarding the likely operation of this scheme:

Hard Shoulder Running would be implemented continually through the AM and PM peak periods (i.e. 4 lane capacity).

Hard Shoulder Running would not be implemented in the Inter Peak.

Hard Shoulder Running would be accompanied by a 60mph speed limit.

Through junction running would occur at all junctions except J30 southbound.

No further junction improvements have been identified or assumed.

2.1.10 The hard shoulder running scheme was coded into the highway model in the time periods described above by applying a specific hard shoulder running speed flow curve to those links upon which it would be implemented. This speed flow curve, provided by the Highways Agency (HA), increases the capacity and reduces the speed of these links.

2.1.11 Local highway schemes have also been identified. As part of a continuation of the Staveley Northern Loop road, there would be a new link constructed between the A619 at the Lime Avenue junction. This would join into a fourth arm of the Northern Loop roundabout along Hall Lane. This would occur before 2016 and is therefore included in the 2016 and 2026 networks.

2.1.12 The new link has been assumed to have a 30mph speed limit and to be just over

718m in length. The junction at Lime Avenue would be signalled and there would


D133513/4/008 9 April 2012

be enough space for two lanes of capacity at both the existing A619 and new link stop lines.

2.1.13 A new link road is to be constructed between the A632 and Markham Road at M1

J29A, along the western side of the motorway as part of the Markham Vale development. It is assumed that the link would have a 30mph speed limit, would be 1050m in length and implemented before 2016.

2.1.14 It has been assumed that the junction with the A632 will be signalised and each

arm would provide enough capacity for two lane approaches. It is assumed that the junction at Markham Road would tie into the existing roundabout which forms part of M1 J29A.

2.1.15 Starting from the 2010 base year network, there are two further schemes to

signalise junctions within the modelled area. These are the roundabout junction of Littlemoor Road and Newbold Road and the roundabout junction of Hasland Road and Mansfield Road. Signal times were optimised in the traffic model to reduce queuing delay at all arms. Both signal junctions are included in both the 2016 and 2026 networks.

2.1.16 A further scheme has been identified to ban the right turn at Cemetery Lane onto

the A632. This scheme is included in both the 2016 and 2026 networks.

2.1.17 No other highway schemes have been identified or coded into the future year SATURN networks.

2.1.18 There were no schemes identified as occurring between 2016 and 2026, and

therefore the 2026 network replicates the 2016 network.

Planned Public Transport Schemes 2.1.19 Public transport interventions could take the form of alterations to existing bus

services, new bus routes, increased service frequency or major light or heavy rail interventions. There have been no public transport interventions identified at this time.

2.1.20 No future year public transport schemes have been coded into the highway model in either 2016 or 2026.

2.2 Trip Demand Forecasting

Introduction 2.2.1 The forecast trip matrices in the traffic model represent how patterns of trip

demands will shift over time. Changes may occur as a result of growth in incomes, change in transport prices, demographic factors and land-use changes.

2.2.2 A basic procedure underlying most transport models, including the North Derbyshire Model, is a ‘trip generation’ stage, followed by ‘trip distribution’ and ‘assignment’ stages. The general volume of travel, for distinct journey purpose types, is related to person-type characteristics at a zonal level. Thus the trip demand is related to the forecast population and person types in each zone.


D133513/4/008 10 April 2012

Data Sources 2.2.3 Growth factors to forecast demand matrices were extracted from NTEM, a

database developed by DfT.

2.2.4 The software collates growth information using forecasting parameters and data that are extracted from the National Trip End Model (NTEM). NTEM is a fully integrated part of the DfT’s National Transport Model (NTM) framework. The later versions of the DfT’s software TEMPRO also include the NTM Traffic Growth Calculation functionality.

2.2.5 The NTM forecasts give traffic growth by region, road type and whether the area is

built up or not. Factors calculated by TEMPRO should be used to tailor this published traffic forecast to local circumstances

2.2.6 For modelling non car vehicle types in highway models, growth factors from the

NTM should be used.

2.2.7 NTEM growth factors are trip end factors. NTM growth factors are road based factors.

2.2.8 The application of the trip generation process produces a ‘Reference Demand’ trip

demand for the forecasting years. 2.2.9 It is important to note that this ‘Reference Case’ trip demand is not intended to

represent a realistic forecast of what might happen. This is because the ‘Reference Case’ trip demand can produce traffic flows that are greater than the highway network can accommodate. In such a case, the result will be a highway network (i.e. supply model) that experiences some degree of congestion, and which congestion varies by time of travel, with consequential increases in travel costs. These costs will suppress demand for travel, such that some trips would not occur.

2.2.10 The consequential reduction in the number of trips reduces congestion on the

highway network and releases some of the initially suppressed trips. By iterating between the trip generation demand model and costs in the supply model a condition is reached in which demand and supply models are in equilibrium.

2.2.11 The ‘Reference Demand’ forecasts therefore represent the future demand for travel

given that there is no future change in travel costs in the supply model (i.e. highway network).

2.2.12 Car trips and Goods vehicle reference case demand matrices were developed using TEMPRO’s two separate datasets; NTEM for cars and NTM for goods vehicles. These are described below in more detail. Private Car trips

2.2.13 Growth factors for cars, user classes 1, 2 and 3 (Commute, Employers Business

and Other trip purposes), were derived using the TEMPRO software with NTEM dataset, version 6.2, for East Midlands.

2.2.14 NTEM factors are available on a ward level and therefore different growth factors can be applied by location.


D133513/4/008 11 April 2012

2.2.15 Model zones are generally based on Census Output Areas (OA). It is possible to have multiple Output Areas within an NTEM ward.

2.2.16 To derive equivalences between the NTEM ward boundaries and the North

Derbyshire hghway model zones, the two zoning systems were overlaid in GIS. Zones wholly contained within a ward boundary used the specified growth factor for that ward. Model zones located over more than one ward had a growth factor applied based upon the proportional area contained within each ward. The larger outer zones took the standard NTEM ‘Great Britain’ factor.

2.2.17 Growth factors were calculated for separate origin and destination movements and

vary by time period and forecast year.

2.2.18 The final origin and destination factors used for each model zone are presented in Appendix A at the back of this report.

2.2.19 NTEM factors were applied to the base year light vehicle trip end totals and

furnesssed to the resulting trip end forecasts.

2.2.20 NTEM factors do not take into account changes in value of time and vehicle operating costs over time. Additional global traffic growth factors, representing the impact of income growth and fuel cost are therefore applied to NTEM factors.

2.2.21 Fuel and Income adjustment factors were applied to the matrices after growth.

These represent the travel responses to income growth and fuel cost changes in the future.

2.2.22 The factors used, derived from WebTAG unit 3.5.12, are given below in Table 2-3.

Forecast Year Income Factor Fuel Factor 2010-2016 1.017 1.030

2010-2026 1.048 1.039

Table 2-3 Fuel and Income Adjustment Factors

Goods Vehicles

2.2.23 Growth factors from NTM were applied to good vehicles (user classes 4 to 6 - Light

Goods Vehicle Work, Light Goods Vehicle Non Work and Heavy Goods Vehicle). 2.2.24 Factors from NTM for the ‘East Midlands’ region were used in the study area and

‘England’ factors were used on the larger outer zones. 2.2.25 The factors used for these vehicle classes are presented in Appendix B.

2.2.26 NTM factors were applied to the base year heavy vehicle matrices and furnesssed.

Reference Demand Matrices 2.2.27 Tables 2-4 to 2-6 present the total trips contained within the Reference Case

matrices. These are disaggregated to show the total trips by user class. Base Year totals are presented for comparison.


D133513/4/008 12 April 2012

User Class

Vehicle Type

Base Year 2016

Reference 2026

Reference

1 Car

(Commute) 52,013 57,464 67,469

2 Car

(Employers Business)

4,712 5,211 6,144

3 Car

(Other) 18,202 20,130 23,715

Sub Total Cars 74,927 82,805 97,328

4 LGV

(Work) 2,429 2,760 3,436

5 LGV

(Non Work) 2,513 2,856 3,555

6 HGV 3,513 3,634 3,876

Sub Total Goods

Vehicles 8,455 9,250 10,867

Total Vehicle 83,382 92,055 108,195

6 HGV (pcu) 7,026 7,267 7,752

Total pcu 86,895 95,689 112,071

Table 2-4 AM Base Year and Reference Demand Matrix Totals

User Class

Vehicle Type

Base Year 2016

Reference 2026

Reference

1 Car

(Commute) 12,847 14,354 17,411

2 Car


4,243 4,732 5,769

3 Car

(Other) 24,572 27,410 33,462

Sub Total Cars 41,662 46,496 56,642

4 LGV

(Work) 2,663 3,025 3,763

5 LGV

(Non Work) 2,668 3,032 3,774

6 HGV 3,221 3,331 3,553

Sub Total Goods

Vehicles 8,552 9,388 11,090

Total Vehicle 50,214 55,884 67,732

6 HGV (pcu) 6,442 6,661 7,105

Total pcu 53,435 59,213 71,284

Table 2-5 IP Base Year and Reference Demand Matrix Totals


D133513/4/008 13 April 2012

User Class

Vehicle Type

Base Year 2016

Reference 2026

Reference

1 Car

(Commute) 56,832 62,809 73,630

2 Car


4,814 5,319 6,263

3 Car

(Other) 22,004 24,311 28,676

Sub Total Cars 83,650 92,439 108,569

4 LGV

(Work) 1,977 2,245 2,793

5 LGV

(Non Work) 3,223 3,662 4,557

6 HGV 2,987 3,089 3,296

Sub Total Goods

Vehicles 8,187 8,996 10,646

Total Vehicle 91,836 101,436 119,215

6 HGV (pcu) 5,974 6,177 6,591

Total pcu 94,823 104,524 122,510

Table 2-6 PM Base Year and Reference Demand Matrix Totals


D133513/4/008 14 April 2012

3.0 REFERENCE CASE OUTPUTS 3.1 Assignment 3.1.1 The Reference Case matrices described above in Chapter 2.0 were assigned onto

the ‘Do-Minimum’ networks via the Wardrop Equilibrium procedure. The procedure is based on the Wardrop principle of traffic equibilrium which states:

“Traffic arranges itself on congested networks such that the cost of travel on all routes used between each O-D pair is equal to the minimum cost of travel and all unused routes have equal or greater cost.”

3.1.2 The model stopping parameters determine the number of assignment loops

required to satisfy the convergence criteria, ensuring that a stable set of flows and delays have been modelled. Model stopping parameters were the same as those used in the base year assignments and described in the LMVR (D133513/4/007) in section 5.4.

3.1.3 Following the assignment process, the networks were analysed for links and

junctions which were overcapacity, using the volume/capacity (V/C) parameter. Junctions with a V/C ratio greater than 85% were identified as potential issues for further investigation and are described below.

3.1.4 There were two specific areas where the network was amended to assume a

highway intervention:

M1 J29A, the roundabout at the southbound diverge arm was assumed to gain an additional approach lane and hence a capacity improvement. Without this improvement, there would be excessive delays to the M1 Southbound Offslip arm, which could cause an undesirable rerouting of trips.

A632/Staveley Road signals, adjustments to stage times to distribute queues more evenly and prevent unrealistic routing.

3.1.5 The amendments were realistic in their scope (i.e. low cost) and could be

implemented through DCC capital works programme or linked to non-core strategy developments.

3.1.6 The amendments were made to the network and the reference case trip matrices

were assigned again. V/C values were re-checked to ensure the assumed highway interventions were effective.

3.1.7 There are further nodes with V/C ratios of greater than 85%. The worst 10 nodes

(where the V/C value at a junction node is greater than 85%) are listed in Table 3-1 to Table 3-6.


D133513/4/008 15 April 2012

No. Node V/C % Location

1 1245 105 A61 Whittington Moor

2 2003 104 Hall Lane Signals - Barrow Hill

3 1478 103 A38 Westbound approach to M1 J28

4 2107 96 M1 J29 Northbound Offslip

5 2229 95 M1 J29A East Roundabout

6 1509 91 A38 East of M1 J28

7 2093 91 Hornsbridge - A61 Derby Road approach

8 2091 90 Hornsbridge - A617 Hasland Bypass approach


10 1431 89 A632/Staveley Road Signalled junction

Table 3-1 Worst 10 V/C Nodes – AM 2016 (over 85%)






5 1509 101 A38 East of M1 J28




9 1324 92 B6038/B6039 Hasland






… … … …

Table 3-3 Worst 10 V/C Nodes – IP 206 (over 85%)




3 1475 100 M1 Northbound [mainline] approach to J28



6 1297 91 A619 Rotherway roundabout

7 2087 89 Hornsbridge - A617 Town Centre approach


9 1474 86 M1 J28




D133513/4/008 16 April 2012






5 1324 97 B6038/B6039 Hasland

6 1509 97 A38 East of M1 J28



9 1296 92 A61/Nethmoor Road Tupton


Table 3-5 Worst 10 V/C Nodes – PM 2016 (over 85%)






5 1509 103 A38 East of M1 J28


7 1324 100 B6038/B6039 Hasland



10 1395 95 A38 Westbound, West of M1 J28


3.1.8 Common junctions, in several forecast years and time periods, where over capacity

issues would arise are:

A61 Whittington Moor Roundabout.

Hornsbridge Roundabout.

M1 J28 and A38 approaches.

M1 J29 Northbound offslip.

M1 J29A Eastern Roundabout.

A619 Rotherway Roundabout.

Hall Lane Signals, Barrow Hill.

A632/Staveley Road Signalled junction.

3.1.9 These potential over capacity issues arise from the level of background development assumed by NTEM. The first five junctions in this list are Whittington Moor, Hornsbridge and the three M1 junctions, which are all located along major routes where traffic growth would be most expected. The A619 Rotherway junction is over capacity as a result of U-turners from Tapton to Brimington restricting the capacity for vehicles approaching from the A61 junction.

3.1.10 SATURN network plots that indicate the junctions with V/C values greater than 85% are provided in Appendix C.


D133513/4/008 17 April 2012

3.2 Convergence 3.2.1 Model convergence indicators are calculated within SATURN to determine whether

the model is producing stable and repeatable results. This is important to ensure that the differences between model outputs reflect changes between the modelled schemes, and are not attributed to instability within the model.

3.2.2 There are four statistics used to determine satisfactory convergence; Delta Statistic, Percentage Flows, Percentage Delays and Variational Inequality. The derivation of these four statistics is explained in the LMVR (D133513/4/007) section 5.5.

3.2.3 Satisfactory convergence was determined using four indicators: 3.2.4 Delta Statistic: this gives the difference between the costs along the chosen routes

and those along the minimum cost routes, summed across the whole network, and expressed as a percentage of the minimum costs. DMRB recommends (DMRB Volume 12, Section 2, Part 1, paragraph H2.6) that iterations continue until the delta value is less than 1% or has at least stabilized.

3.2.5 Percentage Flows: this is defined as the percentage of links on which flows change

by less than 5% between successive iterations. This 5% is referred to as the critical difference. Percentage flows is a measure of model stability, which gives a pragmatic view of the degree of convergence. DMRB recommends (DMRB Volume 12, Section 2, Part 1, paragraph H2.6) that assignment iterations should continue until flows are stable on 90% of the links for at least four successive iterations, based on a critical difference of 5%. As a refinement of this parameter SATURN also shows the percentage flows based on critical differences of <0.5%, 1%, 2% and 10%. These lower critical difference levels indicate how stable the model is.

3.2.6 Percentage Delays: this is defined as the percentage of delays at junctions that

have changed by less than 5% between successive iterations. This is also a measure of model stability giving a pragmatic view of the degree of convergence.

3.2.7 In addition to these parameters the percentage Variational Inequality (%VI)

compares the costs on the currently assigned routes using the current simulated delays to the costs on the previously assigned routes using the same delays. Current routes would be expected to be “cheaper” if the model were converging to Wardrop equilibrium. If %VI is positive this is true; if %VI is negative, then the former routes are actually cheaper than the current routes. The optimum assigned routes must have a low but positive percentage Variational Inequality

3.2.8 The results for the four statistics are presented below in Table 3-7 for each forecast

year and time period.

Forecast Year

Time Period

Delta Statistic

% Flows % Delay Variational Inequality

2016 AM 0.0138 98.9 98.8 0.00013

IP 0.00434 99.5 99.1 0.00014

PM 0.0200 98.7 98.6 0.00015

2026 AM 0.0415 99.1 98.5 0.00018

IP 0.0095 99.1 98.2 0.00017

PM 0.061 98.9 98.8 0.00021

Table 3-7 Summary Convergence Statistics


D133513/4/008 18 April 2012

3.2.9 The convergence results are better than the target values. This provides confidence

that the model is producing stable results. This is essential when assessing the impact of alternative schemes against the ‘Do-Minimum’ scenario to ensure flow differences are a result of an intervention, rather than ‘noise’ generated by a poorly converging model.


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4.0 CORE FORECAST METHODOLOGY 4.1 Core Scenario Highway Networks

4.1.1 At this stage, no highway improvements have been identified as part of the Core strategies of the local planning authorities. However, some transport interventions may need to be implemented in association with developments identified in the local development documents.

4.1.2 The Core Scenario networks were therefore the same ‘Do Minimum’ networks used for the Reference Case assignments and are detailed in Section 2.1.

4.2 Data Sources 4.2.1 The Core Scenario represents a planned package of developments that are

reasonably expected to be delivered within the specified forecast years.

4.2.2 This scenario has been based upon planning data provided by the planning authorities at Chesterfield Borough Council, Bolsover District Council, North East Derbyshire District Council and the highway authority that is Derbyshire County Council.

4.2.3 Each planning authority provided an uncertainty log, detailing individual

development within each district. Each developments’ likelihood of execution was assessed against a four point criterion set out in WebTAG unit 3.15.5 (The Treatment of Uncertainty in Model Forecasting). Table 4-1 below details the assessment criterion.

Probability of the Input Status Near certain: The outcome will happen or there is a high probability that it will happen.

Intent announced by proponent to regulatory agencies.

Approved development proposals.

Projects under construction.

More than likely: The outcome is likely to happen but there is some uncertainty.

Submission of planning or consent application imminent.

Development application within the consent process.

Reasonably foreseeable: The outcome may happen, but there is significant uncertainty.

Identified within a development plan.

Not directly associated with the transport strategy/ scheme, but may occur if the strategy/scheme is implemented.

Development conditional upon the transport strategy/scheme proceeding.

Or, a committed policy goal, subject to tests (e.g. of deliverability) whose outcomes are subject to significant uncertainty In Draft - TAG Unit 3.15.5

Hypothetical: There is considerable uncertainty whether the outcome will ever happen.

Conjecture based upon currently available information.

Discussed on a conceptual basis.

One of a number of possible inputs in an initial consultation process. Or,

A policy aspiration.

Table 4-1 Classification of Future Inputs


D133513/4/008 20 April 2012

4.2.4 Sites deemed to be ‘Near certain’ or ‘More than Likely’ were included in the Core Scenario traffic forecasts.

4.2.5 A summary of each districts’ planning assumptions is found below.

Chesterfield Borough Council

4.2.6 The uncertainty log provided by Chesterfield Borough Council is contained in

Appendix E.

4.2.7 Chesterfield Borough Council’s uncertainty log contained 1,019 additional residential units between 2010 and 2016, and 3,340 additional residential units between 2016 and 2026 to be included in the Core Scenario.

4.2.8 There are 10.8 ha of employment land to be developed between 2010 and 2016, and 28.2 ha of development land between 2016 and 2026 to be included in the Core Scenario.

North East Derbyshire District Council

4.2.9 The uncertainty log provided by North East Derbyshire District Council is contained

in Appendix F.

4.2.10 The uncertainty log contained 859 additional residential units between 2010 and 2016, and 4,381 additional residential units between 2016 and 2026 to be included in the Core Scenario.

4.2.11 There are 23.3 ha of employment land to be developed between 2010 and 2016, and 36.6 ha of development land between 2016 and 2026 core scenario.

Bolsover District Council

4.2.12 The uncertainty log provided by Bolsover District Council is contained in

Appendix G.

4.2.13 The uncertainty log contained 2,750 additional residential units between 2010 and 2016, and 3,539 additional residential units between 2016 and 2026 in the Core Scenario.

4.2.14 There are 80.0 ha of employment land to be developed between 2010 and 2016, and 102.3 ha of development land between 2016 and 2026 to be included in the Core Scenario.

4.3 Comparison of NTEM and committed developments 4.3.1 The employment area and residential unit numbers were converted into trips using

average trip generation factors calculated from the TRICS database and summarised below in Table 4-2.


D133513/4/008 21 April 2012

Time Period

Origin Factor

Destination Factor

Residential (per unit)

AM 0.377 0.111

IP 0.153 0.159

PM 0.186 0.318

Employment (per hectare)

AM 1.400 1.533

IP 1.322 1.606

PM 0.867 0.633

Table 4-2 TRICS generation rates, peak hour (Veh/hr)

4.3.2 Section 2.2 details the Reference Case Demand forecasting using the DfT’s NTEM growth assumptions.

4.3.3 NTEM growth is driven by a combination of an increase in travel due to an expected increase in wealth and an increase in travel due to new developments (i.e., new trip opportunities). It is therefore expected that NTEM growth in trips should be greater than the development driven growth in trips. Below is a comparison of NTEM growth and the planned development identified by the planning authorities.

4.3.4 Trip end growth from the planned developments and the reference NTEM demand are compared below in Table 4-3 to Table 4-6. The comparison is made for 2016 and 2026 forecast years and for light and heavy vehicles.

2016 Origin Destination

NTEM Dev. NTEM Dev.

AM Chesterfield 1254 499 1415 744

North East Derbyshire 1222 570 956 1450

Bolsover 747 1357 606 2060

Outer Areas 4657 0 4902 0

IP Chesterfield 917 340 980 329


Bolsover 556 934 494 901


PM Chesterfield 1189 737 1244 415


Bolsover 830 2035 768 1127


Table 4-3 NTEM and planned development, comparison of trip increases from 2010 to 2016 – Light Vehicles


D133513/4/008 22 April 2012


NTM Dev. NTM Dev.



Bolsover 88 42 77 46










Table 4-4 NTEM and planned development, comparison of trip increases from 2010 to 2016 – Heavy Vehicles


NTEM Dev. NTEM Dev.



Bolsover 2169 3525 1726 7303

Outer Areas 12821 0 13869 0



Bolsover 1776 2893 1496 2748




Bolsover 2377 6903 2174 2951

Outer Areas 15646 0 15016 0

Table 4-5 NTEM and planned development, comparison of trip increases from 2010 to 2026 – Light Vehicles


D133513/4/008 23 April 2012


NTM Dev. NTM Dev.



Bolsover 266 159 233 174




Bolsover 248 150 247 182




Bolsover 246 99 247 72


Table 4-6 NTEM and planned development, comparison of trip increases from 2010 to 2026 – Heavy Vehicles

4.3.5 In Table 4-3 to Table 4-6 above, the planned development numbers for Bolsover District are greater than those expected, based upon growth factors from NTEM, in all instances for light vehicles. Bolsover District Council is confident that this level of growth is acceptable given historical employment patterns and that the assumptions made are realistic. North East Derbyshire also have growth in trips greater than NTEM in the AM peak destinations and PM peak origins for light vehicles in 2016 and 2026. Development trip totals that exceed the corresponding NTEM trip totals are shown in bold in Table 4-3 to Table 4-6 above.

4.3.6 In the other instances, the planned development identified by the planning authorities is less than NTEM growth. In these cases, the planned development growth has been supplemented by NTEM growth (applied to existing trips) to ensure that the overall growth in each district is not less than NTEM growth.

4.3.7 In some time periods and districts, as noted above, planned development exceeds NTEM growth. In these cases, all the growth in trips generated by the planned development in the district was applied. In districts where planned development growth exceeds NTEM forecasts, no NTEM growth factors were applied. This resulted in the overall growth exceeding NTEM.

4.3.8 Planned development exceeding NTEM in one district is assumed not to impact on the growth levels of other districts within the model; (for example, development assumptions in Bolsover in excess of NTEM do not reduce background growth in North East Derbyshire).

4.3.9 In the outer areas, NTEM growth was added without any further development assumptions.

4.3.10 The additional NTEM growth between 2026 (end of LTP period) to 2031 (Core Strategy period) for Bolsover, North East Derbyshire and Chesterfield is 3%, 2% and 4% respectively. Table 4-3 to Table 4-6 above show that 2031 NTEM forecasts would be lower than the planned development in Bolsover and North East Derbyshire. Given this, it is reasonable to use the 2026 forecasts during the development of the Core Strategies.


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4.4 Core Scenario Matrix Building 4.4.1 Planned developments were assigned to model zones based on grid references

and site descriptions supplied by the planning authorities. In some cases, trips generated by new development were added to zones with existing trips.

4.4.2 Given that TRICS does not determine trip purpose in its trip generation calculations, the initial stage of the Core Scenario forecasting is based on Cars and Goods Vehicles (LGV and HGV) vehicle classes only.

4.4.3 Increases in the trip ends between the base model and the Core Scenario forecasts were calculated using a combination of planned development and NTEM as described in paragraphs 4.3.6 to 4.3.8 above.

4.4.4 The Core forecast’s trip ends were converted to matrix form by adjusting a prior matrix to the growth-factored trip end totals. Given the number of employment sites identified in the planning data, the AM matrix was created using the Furness procedure singly-constrained to the destination trip end totals (to ensure the right level of planned development). The IP matrix was doubly-constrained and the PM was singly-constrained to the origin trip end totals (again, to ensure that the planned employment trips were at the right level).

4.4.5 The trip distribution used in the Furness procedure were constructed using a combination of the distribution for similar land uses within the base model; i.e. from nearby zones with similar types and densities of development. This combination approach allowed best use of the observed trips, but allowed synthesised movements to be included where observed data was sparse ensuring a representative distribution. Trip distributions were derived for each time period and for Light and Heavy vehicle classes.

4.4.6 The Core Scenario growth matrices for both the Cars and Goods vehicle trips were split into the 6 model user classes by applying the base year proportions on a cell by cell basis. This process can only be applied where the zones already contained some pre-existing trips in the base matrices. The new development zones do not contain any trips in the base year matrices and therefore new development zones were split into user classes using the total base year proportions.

4.4.7 The Core Scenario growth matrices were then added to the base year matrices to produce the Core Scenario forecast demand matrices.

Core Scenario Demand Matrices 4.4.8 Tables 4-7 to 4-9 present the total trips contained within the Core Scenario

matrices. These are disaggregated to show the total number of trips by user class. Base Year totals are presented for comparison.


D133513/4/008 25 April 2012

User Class

Vehicle Type

Base Year 2016 Core Scenario

2026 Core Scenario

1 Car

(Commute) 52,013 58,837 73,518

2 Car


4,712 5,333 6,491

3 Car

(Other) 18,202 20,582 24,138

Sub Total Cars 74,927 84,752 104,147

4 LGV

(Work) 2,429 2,661 3,125

5 LGV

(Non Work) 2,513 2,791 3,355

6 HGV 3,513 3,797 4,388

Sub Total Goods

Vehicles 8,455 9,249 10,868

Total Vehicles 83,382 94,001 115,015

6 HGV (pcu) 7,026 7,593 8,775

Total pcu 86,895 97,797 119,402

Table 4-7: AM peak hour Demand Matrix Totals

User Class

Vehicle Type


2026 Core Scenario

1 Car

(Commute) 12,847 14,735 19,117

2 Car


4,243 4,770 5,842

3 Car

(Other) 24,572 27,384 32,752

Sub Total Cars 41,662 46,889 57,711

4 LGV

(Work) 2,663 2,919 3,449

5 LGV

(Non Work) 2,668 2,972 3,570

6 HGV 3,221 3,496 4,070

Sub Total Goods

Vehicles 8,552 9,387 11,089

Total Vehicles 50,214 56,276 68,800

6 HGV (pcu) 6,442 6,991 8,139

Total pcu 53,435 59,771 72,869

Table 4-8: Inter-peak average hour Demand Matrix Totals


D133513/4/008 26 April 2012

User Class

Vehicle Type


2026 Core Scenario

1 Car

(Commute) 56,832 63,785 78,128

2 Car


4,814 5,405 6,422

3 Car

(Other) 22,004 24,660 28,850

Sub Total Cars 83,650 93,850 113,400

4 LGV

(Work) 1,977 2,162 2,553

5 LGV

(Non Work) 3,223 3,592 4,317

6 HGV 2,987 3,241 3,775

Sub Total Goods

Vehicles 8,187 8,995 10,645

Total Vehicles 91,837 102,845 124,045

6 HGV (pcu) 5,974 6,481 7,549

Total pcu 94,823 106,085 127,819

Table 4-9: PM peak hour Demand Matrix Totals


D133513/4/008 27 April 2012

5.0 CORE FORECAST OUTPUTS 5.1 Assignment 5.1.1 The Core Forecast matrices described above in Chapter 4.0 were assigned onto

the ‘Do-Minimum’ networks via the Wardrop Equilibrium procedure. The procedure is based on the Wardrop principle of traffic equilibrium which states:

“Traffic arranges itself on congested networks such that the cost of travel on all routes used between each O-D pair is equal to the minimum cost of travel and all unused routes have equal or greater cost.”

5.1.2 The model stopping criteria determines the number of assignment loops required to

meet the convergence criteria, ensuring that a stable set of flows and delays have been modelled. Model stopping criteria were the same as those used in the base year assignments and described in Section 3.2.

5.1.3 The amendments described in 3.1.4 above were maintained in these networks. 5.2 Convergence 5.2.1 Model convergence indicators are calculated within SATURN to determine whether

the model is producing stable and repeatable results. This is important to ensure that the differences between model outputs reflect changes between the modelled schemes, and not general instability within the model.

5.2.2 There are four statistics used to determine satisfactory convergence; Delta Statistic, Percentage Flows, Percentage Delays and Variational Inequality and these were described in Section 3.2 above (paragraphs 3.2.4 to 3.2.7). The derivation of these four statistics is explained in the LMVR (D133513/4/007) section 5.5.

5.2.3 The results for the four statistics are presented below in Table 5-1 for each forecast

year and time period.

Forecast Year

Time Period

Delta Statistic


2016 AM 0.0190 99.0 98.7 0.00011

IP 0.00447 99.5 99.3 0.00012

PM 0.0163 99.0 98.4 0.00021

2026 AM 0.0468 98.9 98.1 0.00020

IP 0.0095 98.9 98.3 0.00018

PM 0.074 99.1 97.9 0.00026


5.2.4 The convergence results for the Core assignments are better than the target

values. This provides confidence that the model is providing stable results. This is essential when assessing the impact of alternative schemes against the ‘Do-Minimum’ scenario to ensure flow differences are a result of an intervention, rather than ‘noise’ generated by a poorly converging model.


D133513/4/008 28 April 2012

5.3 Comparison against Base Year 5.3.1 The 2016 and 2026 Core Scenario forecasts can be compared against the 2010

base year for each time period. This highlights areas of the network where traffic volumes would be expected to increase.

5.3.2 SATURN demand flow difference plots are presented in Appendix G

5.3.3 The flow difference plots show a general increase in flow across the study area as expected with 6 and 16 years additional traffic growth applied.

5.3.4 Large increases are shown on the M1 corridor in both 2016 and 2026. The A617,

A61 and A619 all experience flow increases in both forecast years.

5.3.5 It is noted that a few links are predicted to reduce in flow in the future years. The small reductions are a result of side road trips rerouting, as a result of the flow increases on the major routes making it harder to exit junctions.


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6.0 NETWORK ANALYSIS 6.1 Introduction 6.1.1 An analysis of the Core assignments has been undertaken to show the potential

capacity problems as a result of the Core Strategies. 6.2 Junction Capacity 6.2.1 The following tables show the junctions within the model area that are approaching

capacity in the Core scenario assignments. The worst 10 junctions with a V/C greater than 85% are detailed.







6 1509 95 A38 East of M1 J28



9 1324 91 B6038/B6039 Hasland









6 1509 104 A38 East of M1 J28

7 1324 102 B6038/B6039 Hasland








… … … …



D133513/4/008 30 April 2012











10 2106 87 M1 J29 Southbound Onslip







5 1509 99 A38 East of M1 J28

6 1324 98 B6038/B6039 Hasland










4 1509 105 A38 East of M1 J28

5 2233 104 Erin Road/M1 J29A



8 1324 102 B6038/B6039 Hasland




6.2.2 Appendix I shows network plots of the modelled area where junctions with V/C

values greater than 85% are highlighted.

6.2.3 As expected, the 2026 forecast year indicates a greater number of junctions approaching capacity than the 2016 forecast year. Inter-peak time periods show fewer junctions with V/Cs greater than 85%.


D133513/4/008 31 April 2012

6.2.4 Common junctions, in several forecast years and time periods, where over capacity issues arise are:

A61 Whittington Moor Roundabout.

Hornsbridge Roundabout.

M1 J28 and A38 approaches.

M1 J29 Northbound offslip.

M1 J29A Eastern Roundabout.

A619 Rotherway Roundabout.

Hall Lane Signals, Barrow Hill.

A632/Staveley Road Signalled junction.

6.2.5 Some of these junctions surround the major development areas in the Core Scenario, such as Markham Vale and Staveley Works. All junctions were already identified earlier in the Reference Case as potentially being over capacity (paragraph 3.1.9).

6.3 Link Capacity 6.3.1 The following plots show the links within the model area that are approaching

capacity in the Core scenario assignments. Links with a V/C values that are greater than 85% are highlighted.

Figure 1 – Link V/C greater than 85% – AM 2016


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Figure 2 – Link V/C greater than 85% – IP 2016


D133513/4/008 33 April 2012

Figure 3 – Link V/C greater than 85% – PM 2016

Figure 4 – Link V/C greater than 85% – AM 2026

Figure 5 – Link V/C greater than 85% – IP 2026


D133513/4/008 34 April 2012

Figure 6 – Link V/C greater than 85% – PM 2026

6.3.2 Common links, in several forecast years and time periods, where over-capacity

issues arise are:

Approaches to M1 at junction 28, 29 and 30.

A61 from Sheepbridge to Hornsbridge.

A61 Derby Road, various sections between Clay Cross and Chesterfield.

A632 Matlock Road.

A632 Chesterfield Road, Arkwright.

6.3.3 The common over-capacity links occur along routes where Core Scenario developments are implemented. Links to the M1 and the A61 are key routes and it is not unreasonable to expect development traffic to use these.

6.4 Core and Reference Case Comparisons 6.4.1 The following plots show the difference in link flows between the Core Scenario and

Reference Case assignments. Green signifies flows higher in the Core Scenario while blue denotes link flows higher in the Reference Case.


D133513/4/008 35 April 2012

Figure 7 – Core and Reference Case Comparison – AM 2016 (outer)

Figure 8 – Core and Reference Case Comparison – AM 2016 (inner)


D133513/4/008 36 April 2012

Figure 9 – Core and Reference Case Comparison – IP 2016 (outer)

Figure 10 – Core and Reference Case Comparison – IP 2016 (inner)


D133513/4/008 37 April 2012

Figure 11 – Core and Reference Case Comparison – PM 2016 (outer)

Figure 12 – Core and Reference Case Comparison – PM 2016 (inner)


D133513/4/008 38 April 2012

Figure 13 – Core and Reference Case Comparison – AM 2026 (outer)

Figure 14 – Core and Reference Case Comparison – AM 2026 (inner)


D133513/4/008 39 April 2012

Figure 15 – Core and Reference Case Comparison – IP 2026 (outer)

Figure 16 – Core and Reference Case Comparison – IP 2026 (inner)


D133513/4/008 40 April 2012

Figure 17 – Core and Reference Case Comparison – PM 2026 (outer)

Figure 18 – Core and Reference Case Comparison – PM 2026 (inner)


D133513/4/008 41 April 2012

6.4.2 The difference plots above showing flow changes between the Reference Case

Scenario and Core Scenario show that there is a general flow reduction to the north of Chesterfield with a flow increase towards the south and east of Chesterfield in all three time periods in 2016.

6.4.3 Flows increase in Clay Cross in 2016 as a result of the planned development in the town.

6.4.4 Flows on the A61 through Chesterfield are at a similar level in the 2016 Core

Scenario to that in the 2016 Reference Case.

6.4.5 A comparison of the 2026 Reference Case and Core Scenario assignments show that there is a general flow reduction to the north of Chesterfield and increases in flow to the south and east of Chesterfield. The reduction represents the reduction/removal of NTEM growth in the Core Scenario to compensate for the planned development.

6.4.6 There are large flow increases to the north of Staveley as a result of the planned

Staveley Works development in 2026.

6.4.7 There are also flow increases on the B6417 Rotherham Road to the east of Bolsover as a result of the planned development in Shirebrook and Whitwell in 2026.

6.4.8 2026 flows on the A61 through Chesterfield are of a similar level in the Core

Scenario to those in the Reference Case.

6.4.9 M1 flows in 2026 increase in the Core Scenario in comparison to the Reference Case, especially around J29A.


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7.0 INVESTIGATION OF RESPONSES TO CHANGES IN TRAVEL COST

7.1 Investigation of Variable Demand Responses

Introduction to Variable Demand 7.1.1 As described in Section 4.0, the ‘Core Scenario’ represents the future demand for

travel given that there is no future change in travel costs in the supply model (i.e. highway network). For example, if congestion builds up on the road network then this will increase travel times (costs). The possible responses to travel cost are;

Trip frequency.

Mode choice.

Trip distribution.

Time of day choice.

7.1.2 It follows that any change to the future cost of travel is likely to change the demand for travel. The costs of travel may become more expensive if congestion is allowed to increase. Conversely, the cost of travel may reduce as a result of a transport intervention; for example a bypass is likely to reduce travel costs, as would a junction improvement.

7.1.3 Changes to the transport network will initiate a response from travellers and a

change in some of their travel patterns. These responses are represented in a transport model by assigning the future year ‘Core Scenario’ trip demand (as detailed in Section 4.0) to the future year supply network and then iterating until equilibrium point in the travel costs is reached. The trip demands output from this equilibrium assignment are the ‘Variable Demand Model’ (VDM) trip matrices.

7.1.4 An elastic assignment, using the PM 2026 as a worst case scenario, was

undertaken to investigate the potential for induced or suppressed trips. Base Year Generalised Costs were used in the Core Scenario assignments to ensure that the outputs of the elastic assignment shows only the effects of congestion on the future year network, rather than expected changes in generalised costs.

7.1.5 This is not a full Variable Demand Forecast which models redistribution effects.

Elastic demand operates by adjusting the number of extra trips in each cell of a demand matrix as a proxy for the full range of possible responses to change in travel cost. Elastic Assignment applied to the Core Scenario Case

7.1.6 To provide an investigation of the potential for induced or suppressed trips an elastic assignment was undertaken using the PM 2026 Core Scenario forecast. This process suppresses the number of extra trips where they may be unrealistic. For example OD movements which may experience excessive delays would force a driver to change their travel patterns or, not make the trip. Alternatively, trips may be induced onto the network if there is sufficient capacity improvement in a future year. This elastic assignment process serves as an indicator for the attractiveness of new trips or the potential for trips to switch from other time periods, were a full VDM analysis undertaken.

7.1.7 Elasticity factors were calculated according to the guidance given in WebTAG 3.10.3. Low modal competition including time switching was deemed the most


D133513/4/008 43 April 2012

appropriate parameters to use for the North Derbyshire Highway Assignment model.

7.1.8 An elastic assignment was undertaken in SATURN by setting the MCGILL(uc)

parameter to 2 for each user class required, to select a power law demand function. The elastic assignment parameter POWER(uc) is set individually and is a measure of the amount of elasticity. The values are given below in Table 7-1. Car trips (User classes 1, 2 and 3) were assigned elastically and Goods Vehicles (user classes 4, 5 and 6) were assigned inelastically by setting MCGILL = 0.

Forecast Year PM UC1 Car (Commute) -0.399

UC2 Car (Employers Business) -0.652

UC3 Car (Other) -0.408

Table 7-1 Elastic Assignment Parameter Values - 2026

Forecast Year

Time Period

Delta Statistic


2026 PM 0.032 99.1 96.1 0.0002


7.1.9 The convergence statistics are better than the target values and the model

converges to a satisfactory equilibrium. 7.1.10 The matrix totals following elastic assignment are given below Table 7-3. These

show that, as with the Reference Case, there is an decrease in the number of trips after elastic assignment indicating a suppression effect, as expected.

Forecast Year

User Class Core

Demand Matrix

Elastic Demand Matrix

% of Core Demand

2026 1 78,128 77,296 98.9%

2 6,422 6,190 96.4%

3 28,850 28,310 98.1%

4 2,553 2,553 100%

5 4,317 4,317 100%

6 7,550 7,550 100%

Total 127,820 126,216 98.7%

Table 7-3 Elastic Assignment Trip Summary PM Peak (pcus)

7.1.11 The 2026 post elastic matrices decrease by 1.3% as a whole. Trip suppression can

vary in size across the network.

7.1.12 Appendix H also shows the effects of the elasticity test within the study area.

7.1.13 Flow changes on links within Chesterfield are relatively small (less than 100 vehicles per hour). The largest changes in trips are found on the major roads in the Study Area (A61, A619, M1) as expected, given that they carry more traffic.


D133513/4/008 44 April 2012

7.1.14 The graphs below in Figure 19 shows how many matrix cells fall within varying bands of percentage change in trips after elastic assignment. Figure 20 shows the number of trips in these bands.

Figure 19 – Cell by Cell Matrix Change - PM Peak 2026

7.1.15 The graphs above indicate that there is an overall suppression effect by running an

elastic assignment. Most cells experience no change; the level of suppression is typically up to a 10% increase.

7.1.16 Some cells experience reductions in their trip numbers in the -50% to -100% band. These are all cells which have low volumes of trips where small increments have a disproportionate impact. To show that the disproportionate impact is a negligible effect, the numbers of trips were plotted by percentage change, as follows in Figure 20.

0

50

100

150

200

250

300

350

400

450

-50

to

-1

00

-45

to

-5

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-40

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-4

5

-35

to

-4

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-30

to

-3

5

-25

to

-3

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-20

to

-2

5

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to

-2

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-1

5

-5 t

o -

10

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00

1 t

o -

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Ch

an

ge

5 t

o 0

.00

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D133513/4/008 45 April 2012

Figure 20 – Number of Trips Induced/Suppressed - PM Peak 2026

7.1.17 The number of trips suppressed by more than 20% is low.

7.1.18 These graphs show that the cells with large decreases over 50%, as raised in

7.1.15 above, are in fact relating to very small trip numbers and are not skewing the matrix with large reductions in absolute trips.

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8.0 CONCLUSIONS

8.1.1 A traffic model of North Derbyshire, encompassing Chesterfield, North East Derbyshire and Bolsover Districts, has been developed to allow informed decisions on land use proposals as part of the emerging Core Strategies.

8.1.2 Traffic forecasts have been produced for two growth scenarios, the Reference Case and the Core Scenario. Traffic forecasts were produced for three time periods and two future years.

8.1.3 The Reference Case forecasts have been derived using growth factors derived

from the Department for Transport’s National Trip End Model (NTEM) using the TEMPRO software program.

8.1.4 The Core Forecasts have been produced using detailed planning data from North

East Derbyshire, Chesterfield and Bolsover planning authorities. Each district provided an uncertainty log detailing planned development in each district and the likelihood of each site being developed within the model forecast years.

8.1.5 Highway schemes that were reasonably expected to be implemented within the

forecast years were coded into future year highway networks.

8.1.6 Elastic assignments were undertaken, serving as an indicator for the potential for induced or suppressed trips or trips switching from other time periods, should a full Variable Demand Model approach be undertaken. In both the Reference Case and Core Scenario, the elastic assignments suppressed a small amount of trips overall.

8.1.7 A network analysis of the Reference Case and Core Scenario assignments was

undertaken and junctions and links approaching capacity were identified. A comparison of the Reference Case and Core Scenario assignments was also presented.

8.1.8 The forecasts were found to be suitable for informing land use decisions and for the

testing the highway impacts of potential developments and highway schemes.

Documents

Traffic Forecasting Report - bolsover.gov.uk › images › LIVE › P › Plan_EB_NDHAMrep… · 1.1.8 This report, the Traffic Forecasting Report, describes the development of the