AP-R510-16 Distraction and Attitudes Towards Safe Pedestrian Behaviour

8/16/2019 AP-R510-16 Distraction and Attitudes Towards Safe Pedestrian Behaviour

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Research Report

AP-R510-16

Distraction and Attitudes Towards Safe

Pedestrian Behaviour


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Distraction and Attitudes Towards Safe Pedestrian Behaviour

Prepared by

Alexia Lennon, Amy Williamson, Mark King, Ioni Lewis, and Mazharul Haque

Publisher

Austroads Ltd.Level 9, 287 Elizabeth StreetSydney NSW 2000 AustraliaPhone: +61 2 8265 3300

[email protected]

Project Manager

Edward Rose

Abstract

Pedestrians account for around 14% of Australian road deathsannually. International research suggests that pedestrian distractionfrom smart phones may lead to greater risk of trauma. Ownership ofsmart phones in Australia is high, suggesting this may present anemerging road safety challenge.

Austroads commissioned research to identify: community attitudestowards, and personal, social and environmental factors influencing,safe pedestrian behaviour; groups most at risk of distracted walking;most likely locations; and effective countermeasures. The project hadthree components: a literature review; an intercept survey with

pedestrians; and an on-line survey of pedestrians.The literature review identified that pedestrian distraction fromtechnology is influenced by age, gender, and type of activity. Non-intersection locations are more likely than intersections for pedestriancrashes generally, though there is no research on distraction-specificcrashes. Illegal pedestrian behaviours may be both common andriskier. Many effective countermeasures address general pedestriancrash risk, but few address distraction from mobile phones whilecrossing, and these have been educational in nature, with unproveneffectiveness.

Surveys results suggested that use of smart phone while crossing theroad was low for the sample overall, but significantly higher among18-30 year olds, with 30% indicating they used their smart phones for texting or internet access at risky levels while crossing the road. Risk

perception and attitudes towards using a smart phone while crossingwere important factors influencing likely behaviour for 18-30 year olds.Countermeasures should integrate education and engineeringinterventions within a Safe System approaches, target pedestriansunder 30 years, and be located at high pedestrian activity locations.

About Austroads

Austroads is the peak organisation of Australasian roadtransport and traffic agencies.

Austroads’ purpose is to support our member organisations todeliver an improved Australasian road transport network. Tosucceed in this task, we undertake leading-edge road andtransport research which underpins our input to policydevelopment and published guidance on the design,construction and management of the road network and itsassociated infrastructure.

Austroads provides a collective approach that delivers value for

money, encourages shared knowledge and drives consistencyfor road users.

Austroads is governed by a Board consisting of seniorexecutive representatives from each of its eleven memberorganisations:

Roads and Maritime Services New South Wales

Roads Corporation Victoria

Department of Transport and Main Roads Queensland

Main Roads Western Australia

Department of Planning, Transport and InfrastructureSouth Australia

Department of State Growth Tasmania

Department of Transport Northern Territory Territory and Municipal Services Directorate, Australian

Capital Territory

Commonwealth Department of Infrastructure and RegionalDevelopment

Australian Local Government Association

New Zealand Transport Agency.Keywords

Pedestrian distraction, smart phone distraction, pedestrian mobilephone use, pedestrian attitudes, Theory of Planned Behaviour, HealthBeliefs Model, Mobile Phone Involvement, hypothetical pedestriancrossing scenarios.

ISBN 978-1-925451-00-9

Austroads Project No. SS1957

Austroads Publication No. AP-R510-16

Publication date February 2016

Pages 96

© Austroads 2016

This work is copyright. Apart from any use as permitted underthe Copyright Act 1968, no part may be reproduced by anyprocess without the prior written permission of Austroads.

This report has been prepared for Austroads as part of its work to promote improved Australian and New Zealand transport outcomes byproviding expert technical input on road and road transport issues.

Individual road agencies will determine their response to this report following consideration of their legislative or administrative arrangements,available funding, as well as local circumstances and priorities.

Austroads believes this publication to be correct at the time of printing and does not accept responsibility for any consequences arising from theuse of information herein. Readers should rely on their own skill and judgement to apply information to particular issues.


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Austroads 2016 | page i

Summary and Recommendations

This research investigated pedestrian distraction due to use of technological devices and alcohol impairment.Pedestrian behaviour and pedestrian safety (what pedestrians should do) were examined. An extensivereview of the literature was conducted. In addition, brief interviews with pedestrians (N = 211) and an on-linesurvey of pedestrians (N = 268) were conducted. Findings as they relate to questions in the project brief aresummarised below.

What factors influence pedestrian behaviour compared to pedestrian safety?

Age:

Adolescents and young adults are the most likely to be distracted pedestrians.

Mobile phone-related injury has been found to be higher among younger people, especially teens, in US

studies.

Rates of smart phone ownership in Australia are high, especially among the young and makeperformance of cognitively or visually demanding interactive use possible while crossing the road.

Location:

Most pedestrian fatalities occur at non-intersection locations. At signalised intersections, crossing within20m of a crossing but not at the crossing increases crash risk by factor of eight compared to legalcrossing.

Odds of distracted crossing are higher when at signalised crossings than at unsignalised.

Location of the crossing relative to the origin and destination of the pedestrian, availability of pedestrian

signals, the number of lanes and whether the road is one-way versus two-way influence pedestrianbehaviour and the likelihood of disobeying a crossing signal.

Pedestrian crash risk by type of location appears to be multi-factorial in nature, with different sets offactors associated with risk across different location-time frameworks.

Illegal road use

Illegal road use (e.g. crossing away from marked crossings; crossing on red signal) by pedestrians iswidespread (e.g. ~20% of crossings at signalised intersections, Brisbane).

Exposure: Engaging in distracting smart phone tasks while walking and crossing

Up to 40% of pedestrians may be distracted by mobile phones when crossing the road

Smart phone use for texting or internet access while crossing is widespread for 18-30 year olds, withapproximately 30% reporting high frequency of using smart phones to text or access internet functionswhile crossing the road.

Type of mobile phone activity

Texting, talking and cognitively demanding smart phone use is associated with greater reaction times andincreased errors for competing tasks.

Type of smart phone activity (voice call, text, internet access) is the strongest predictor (compared topresence/absence of signals, presence/absence of median strip) of self-reported use of a mobile phonewhile crossing for pedestrians aged 18-30 years.

Listening to music through headphones/earpods may represent a lower level of distraction than texting,voice calls or internet access/use.


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Austroads 2016 | page ii

Risk perception

The majority of pedestrians are aware of the risks of injury from crossing while distracted, and self-reportthat they do not engage in this behaviour.

Younger adults are significantly less aware than older adults of their susceptibility to injury fromdistraction by using a smart phone while crossing.

18-30 year olds regard voice calls as less risky than texting/internet use while crossing the road.

Attitudinal research suggests adults 18-30 year olds with lower risk perceptions, positive attitudes towardsdrink walking and perceptions that their friends/family also drink walk or approve of this behaviour aremore likely to drink walk.

A substantial minority of pedestrians may constitute a high risk subgroup. These are the high frequencysmart phone users who engage in the more highly distracting smart phone activities (e.g. initiating textmessages, initiating internet access) while crossing the road

Pedestrians appear to perceive the advantages of not using smart phones while crossing the road as wellas regard doing so as not likely to result in boredom, wasting their time or preventing them responding toimportant messages. They also agreed that stopping using their phones while crossing would be easy to

do. More positive attitudes towards smart phone use while crossing the road and greater perceptions that

friends/important others would approve were associated with greater intentions to using a smart phonewhile crossing the road.

Mobile phone involvement

Around 35% of 18-30 year olds may be overinvolved with their mobile phones and therefore use them atinappropriate or unsafe times (e.g. when crossing the road; while driving). This group may also be moredifficult to influence in relation to safer behaviour.

Impairment by alcohol

Crash statistics suggest that more than a third of fatally injured pedestrians have a BAC >.05, the majorityof these having a BAC >.15.

Drink walking is prevalent among young adult (18-24 years old) patrons of licensed venues.

Most alcohol-involved pedestrian casualties are men, struck at night-time, on Friday or Saturday, arewalking home, have been drinking for several hours, and are struck within two to three hours of finishingdrinking.

Around 20% of the pedestrians surveyed in this research indicated they had walked after drinking two ormore standard drinks in the previous hour at least once per week during the previous 3 months, and themajority of these (2/3) thought they were affected by the alcohol at the time.

Drink walkers may be aware that they are impaired but choose to drink walk regardless.

In the pedestrians surveyed for this report, drink walking-related injuries and ‘near misses’ were rare,affecting only 2% of the sample.

In what ways do these factors affect pedestrian behaviour especially in relation to

pedestrian safety?

Distraction while crossing and walking increases erratic behaviour and decreases safety-related

behaviour

Pedestrians distracted by mobile phones walk more slowly, change directions more often, acknowledgeothers less, look left and right less, are less likely to look at traffic before starting to cross, miss more safeopportunities to cross, take longer to initiate crossing, are more likely to cross unsafely into oncoming

traffic, spend more time looking away from the road, and make more errors than pedestrians who are notdistracted.


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Austroads 2016 | page iii

Cognitive and visual distraction by mobile phones is associated with riskier crossing decisions and lesssafe behaviour.

Texting, talking and cognitively demanding smart phone use appears to compete for attentional resourcesand is associated with greater reaction times and increased errors for competing tasks

Risk of injury is increased for pedestrians using mobile phones while crossing

Males and younger pedestrians (under 31 years old) are at greater risk of injury from distracted crossing.

Lower perceptions of risk, or problematic mobile phone involvement may elevate exposure (more

frequent engagement in distracted crossing)

Around 35% of 18-30 year olds may be overinvolved with their mobile phones and therefore use them atinappropriate or unsafe times (e.g. when crossing the road; while driving). This group may also be moredifficult to influence in relation to safer behaviour.

Greater tendency to use mobile phones while crossing, or problematic levels of mobile phoneinvolvement may lower perceptions of the risks of distraction while crossing the road, or may increasewillingness to ignore these risks.

Pedestrians generally appear aware of the risk of distraction while crossing and are also receptive tosafety interventions that promote not using smart phones while crossing

In relation to drink walking, attitudinal research suggests adults 18-30 years old with lower riskperceptions, positive attitudes towards drink walking and perceptions that their friends/family also drinkwalk or approve of this behaviour are more likely to drink walk

Impairment by alcohol increases crash risk and decreases safe road use

Crash data analysis and simulator-based studies verify the association between alcohol impairment andless safe crossing behaviours and increased crash involvement.

Drink walking-related injuries and ‘near misses’ were rare in the sample surveyed for this report, affecting

only 2%. However, if such involvement levels generalise to the general population, it suggests that largenumbers of young people may be at risk (as they were more likely to report frequent drink walking).

In which locations are these behaviours most relevant when considering pedestrian road safety?

Crash data shows that mid-block pedestrian crashes are high, and although pedestrians perceivesmartphone use as more dangerous at these locations and therefore avoid it, their compensation for therisk appears to be inadequate.

Most smartphone use while crossing is reported for signalised crossings, where there may be a protectiveeffect due to both the extra degree of control afforded by the signals and the advantage of crossing in agroup where not everyone needs to monitor safety while crossing.

What measures have been used to influence and change pedestrian behaviours? How appropriate

would these measures be in an Australian and New Zealand context?

Most interventions focus on single approaches (e.g. education, engineering, enforcement).

Education and awareness intervention programs and campaigns are common but evaluation of these israre and so effectiveness is unknown.

Interventions using enforcement for illegal pedestrian behaviour are uncommon, with logistical difficulties(such as generally low concentrations of pedestrians behaving illegally or wide dispersal of locations forsuch behaviours, affecting detection) and the costs involved (such as use of police time to detect andissue infringement notices) generally making these rare.

Engineering countermeasures to pedestrian behaviour are relatively common, and many have beenevaluated and found to be effective.


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Austroads 2016 | page iv

– Separating pedestrians from motorised traffic in either time or space improves pedestrian safetygenerally. On-road separation in space, such as footpaths, has been found to increase pedestriansafety by as much as double, and provision of footpaths addresses the issue of distracted walking(though not distracted crossing). Thus safety benefits are likely if footpath infrastructure were to beinstalled where it does not currently exist on the transport network. However, for pedestrian crossing,separation in space is impractical in most situations on the transport network in Australia and New

Zealand, especially outside central urban areas and areas of high pedestrian activity. Separation intime may be more applicable as a countermeasure to distracted pedestrian crossing.

– Signalisation affects pedestrian decisions about locations for crossing and safety.

– Raised median strips on multi-lane roads have lowered crash rates.

– Lowering general vehicle speed limits across high pedestrian concentration areas and local roads iseffective at reducing pedestrian crash rates and injury severity.

– Novel approaches to entertaining pedestrians while they wait to cross at signals has been found toreduce illegal crossing behaviours but may provide effectiveness only in the short term, while noveltyvalue is high.

– Pedestrian countdown timers have not been shown to be effective.

– Technology used to warn pedestrians (e.g. audio beacons) is effective and low cost.

– Smart technologies (e.g. ‘intelligent’ road furniture/vehic les) that warn motorists of pedestrians aregrowing in use but as yet are unevaluated. However, these may offer benefits that are worth exploring,particularly if used in combination with education.

– Real time intersection traffic analysis (including pedestrian traffic) via video technology may provide aneffective method of identifying where traffic and engineering countermeasures for distractedpedestrians should be focused.

Drink walking

Alcohol impairment in road safety is addressed by a number of countermeasures such as ‘Safe Night Out’,

lock-out programs, designated driver programs, and responsible service of alcohol (RSA). While they donot specifically address drink walking, strategies which are effective at reducing high levels of alcoholconsumption, or pedestrian intoxication (e.g. RSA), or that minimise pedestrian activity whileintoxicated/impaired are likely to have the greatest benefit.

Education strategies have been used but effectiveness is unknown.

Effective engineering countermeasures to intoxicated pedestrian crashes include “Dwell on red”, andpedestrian fences in late night licensed precincts.

Lowering vehicle speeds and altering traffic signal timings at high alcohol times and locations (e.g. late-night entertainment precincts) and pedestrian fencing in proximity to licensed venues has beenimplemented in Australian cities (but not evaluated).

Enforcement countermeasures have included implementation of ‘lock-out’ laws/policies to reducemovement between venues and greater police activity and these have been found to provide someeffectiveness in reducing traffic incidents generally and potentially of benefit for pedestrian crashinvolvement.

The Drink Safe Walk Safe project, which had a combination of educational, engineering and enforcementmeasures, was evaluated as effective and may provide an approach for wider implementation.

Many of the countermeasures identified have been used in the Australian and/or New Zealand context andthus widespread implementation would be suitable if/as required.


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Austroads 2016 | page v

Recommended measures to address road safety issues, gaps in existing research, and

recommended areas for future research

An integrated approach to pedestrian safety, in keeping with the principles of a safe system andincorporating educational, enforcement and engineering measures should be considered and adopted wherepossible.

In addition, countermeasures to smart phone use when crossing should:

Focus on young people (under 31 years old) because the behaviour is most common in this group

Be located in high pedestrian volume areas (e.g. intersections and city CBD areas, around main trip-generating locations such as universities) because the behaviour is likely to be more common in suchlocations, and in order to reach as many pedestrians as possible.

Take the form of signs on signal posts as a low cost option.

Be included in the road safety education content (e.g. school-based) that targets adolescents, pre-licenceand learner drivers. Such materials should attempt to influence attitudes and perceptions of the risk inrelation to smart phone/mobile phone use while engaged in other demanding and safety related tasks

Public education should seek to:

Increase pedestrian awareness of crash risk associated with distraction from phones when crossing(especially texting/internet access)

Challenge perceived disadvantages of not using a smart phone while crossing

Challenge perceptions that it is possible to cross safely while texting/using the internet

Encourage young people to look out for, and model safe behaviour for, their friends

Countermeasures to drink walking should:

Take an integrated approach, incorporating a combination of educational, enforcement and engineeringmeasures and involving proprietors of licensed premises, staff of licensed premises, and communityrepresentatives in the planning and implementation of the measures.

Incorporate responsible service of alcohol (RSA). Jurisdictions could consider mandating this andenforcing its operation.

Incorporate educational approaches (e.g. display of education based posters, screening of drink walking-related television commercials within licensed premises). Jurisdictions could consider mandating aminimum level of such.

Incorporate those engineering countermeasures that have proven or promising effectiveness such as:

– “Dwell on red”

– pedestrian fences in late night licensed precincts

– localised lower vehicle speed limits (using variable signage if required)

– altering traffic signal timings at high alcohol times and locations

Incorporate targeted enforcement, especially at high alcohol times and locations and target motoristbehaviour as well as pedestrian behaviour.

Future research could investigate:

The evidence for, and size of, the crash-risk from distracted crossing and from drink walking.

The technical feasibility, user acceptance and effectiveness of countermeasures that provide feedback tosmart phone users (ranging from an auditory or visual warning to blanking the screen) identified as aboutto cross the road (using GPS coordinates or sensors/detectors of mobile phones-in-use close to thecrossing/intersection).


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Austroads 2016 | page vi

Feasibility and effectiveness of pavement/intersection treatments such as ‘no mobile phone’ symbolspainted at the road edge where pedestrians gazing downwards at their phones are likely to see them.

The use of video monitoring methods of determining high risk locations for smart phone use whilecrossing the road.

The feasibility of electronic enforcement of smart phone use while crossing the road.

The feasibility (including assessing the level of community acceptance) of introducing and enforcinglegislation governing distracted or alcohol impaired walking and crossing.

The identification and development of countermeasures specifically targeting the small but higher riskyoung pedestrian group (estimated as comprising 10% of 18-30 year olds in this report) who are lessaware of their susceptibility to injury as pedestrians, more likely to engage in risky pedestrian behavioursgenerally and also frequently use smart phones while crossing the road.


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Contents

1. Introduction ............................................................................................................................................ 1

1.1

Background and aims ....................................................................................................................... 1

1.2

Structure of the report ...................................................................................................................... 1

2. Literature Review ................................................................................................................................... 3

2.1 Background and Scope .................................................................................................................... 3

2.2 General factors that influence pedestrian behaviour and safe road use ......................................... 4

2.3

Pedestrian distraction from use of hand-held technological devices ............................................... 5

2.3.1 Mobile phone use ................................................................................................................ 6

2.3.2

Personal music devices ....................................................................................................... 7

2.3.3 Internet use .......................................................................................................................... 8

2.4

Locations where pedestrian crashes are most likely ....................................................................... 8

2.5

Influence of the built environment on likelihood of pedestrian crashes ......................................... 10

2.5.1 Land use ............................................................................................................................ 10

2.5.2 Road factors and speed limits ........................................................................................... 11

2.5.3

Environmental factors ........................................................................................................ 12

2.6 Walking after having consumed alcohol (“drink walking”) .............................................................. 13

2.6.1 Summary of the research on pedestrian distraction from mobile phones or alcohol

impairment ......................................................................................................................... 15

2.7 Countermeasures to distracted or risky pedestrian behaviour ....................................................... 16

2.7.1

Education and Awareness Campaigns ............................................................................. 16

2.7.2

Enforcement countermeasures ......................................................................................... 26

2.7.3 Engineering ....................................................................................................................... 28

2.7.4

Summary of countermeasures and their effectiveness ..................................................... 34

3.

Investigating Pedestrian Use of Potentially Distracting Technology While Walking

and Crossing the Road ........................................................................................................................ 36

3.1

Intercept interviews ........................................................................................................................ 36

3.1.1 Method ............................................................................................................................... 37

3.1.2 Results ............................................................................................................................... 38

3.1.3

Discussion ......................................................................................................................... 43

3.2 Online Survey ................................................................................................................................. 44

3.2.1

Theoretical underpinnings ................................................................................................. 44

3.2.2

Participants and data collection ......................................................................................... 46

3.2.3 Survey Design ................................................................................................................... 47

3.2.4 Materials ............................................................................................................................ 47

3.2.5

Results ............................................................................................................................... 50

3.2.6 Discussion ......................................................................................................................... 64

4. Conclusions and Recommendations ................................................................................................. 68

4.1 Recommended measures to address road safety issues .............................................................. 72

4.2 Recommended areas for future research ...................................................................................... 74

References ................................................................................................................................................ 75Appendix A Review of Research Relating to Factors Influencing Pedestrian Behaviour

and Safe Road Use ............................................................................................................... 85


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Appendix B Intercept Interview Schedule ............................................................................................... 93

Appendix C Summary of Results for Intercept Interview Responses to Drink Walking Questions . 96

Tables

Table 3.1: Proportions of pedestrians who use their smart phones while walking and crossing theroad by activity and categories of frequency (self-report) ............................................................ 39

Table 3.2: Walking while using a smart phone by level of exposure and age group .................................... 40

Table 3.3: Crossing the road while using a smart phone by age group and level of exposure .................... 42

Table 3.4: Proportions of pedestrians (%) engaging in different smart phone activities while crossing

the road (N=268) .......................................................................................................................... 51

Table 3.5: Crossing the road and smart phone use (N=268) by age and exposure category ...................... 52

Table 3.6: Pedestrian Behaviour Scale question wording and Mean (SD) by age group ............................. 53

Table 3.7: Comparison of high and low self-reported usual risky pedestrian behaviour for high and low

levels of frequency (high, low) use of smart phone while crossing (n = 247) .............................. 54

Table 3.8: Mobile phone involvement level (high, low) by age group and gender ........................................ 55

Table 3.9: Comparison of high and low mobile phone involvement for self-reported usual riskypedestrian behaviour (high, low) and frequency of smart phone use while crossing the road

(high, low) for both all age (Full sample, n = 247) and young people only (n = 168)................... 57

Table 3.10: Pedestrian responses (mean rating) to susceptibility, severity, benefits, barriers and

self-efficacy measures (Health Belief Model) ............................................................................... 58

Table 3.11: Health Beliefs Model variable scores by age group ..................................................................... 59 Table 3.12: Questions and mean responses (SD) for measures of attitudes, group norms, perceptions

of control and intentions (TPB) in relation to using smart phones while crossing ....................... 60

Table 3.13: Summary of hierarchical regression analysis for Theory of Planned Behaviour standard

(Attitude, Subjective Norms, Perceived Behavioural Control) and additional (mobile phone

involvement, Group norm) predictors of intentions to cross the road while using a smartphone for text/internet access (entire sample, 18-65 year olds, N=268) ..................................... 62

Table 3.14: Summary of hierarchical regression analysis for Theory of Planned Behaviour standard(Attitude, Subjective Norms, Perceived Behavioural Control) and additional (mobile phone

involvement, Group norm) predictors of intentions to cross the road while using a smart

phone for text/internet access (18-30 year olds only) .................................................................. 62 Table 3.15: Mean ratings of the likelihood of crossing in scenarios varying by i) type of smart phone

activity (voice call, text/internet), ii) presence or absence of pedestrians signals, and iii)median strips (present, absent) by age group. ............................................................................ 63

Figures

Figure 2.1: Newspaper advertisement from the Pedestrian Council of Australia’s ‘Lambs to the

slaughter’ campaign ..................................................................................................................... 17

Figure 2.2: Advertisements from the Pedestrian Council of Australia’s ‘Don’t Tune Out’ campaign ............. 18

Figure 2.3: Join the Drive’s ‘Share the Road’ campaign ................................................................................ 18

Figure 2.4: City of Melbourne’s Share the Road Campaign: Red Man, Green Man ...................................... 19

Figure 2.5: Auckland City Council’s ‘Don’t Step into Danger: Fire’ campaign ............................................... 20 Figure 2.6: Auckland City Council’s ‘Don’t Step into Danger: Snake’ campaign ........................................... 20

Figure 2.7: Wellington City Council ‘Cross the road with a clear head’ campaign ......................................... 21

Figure 2.8: Word Cloud illustrating the range of potential distractions that people nominated whenwalking around the city ................................................................................................................. 22

Figure 2.9: Christchurch City Council’s “Could they stop? Cross safely” campaign ...................................... 22 Figure 2.11: Never let a mate walk home drunk” campaign. ............................................................................ 23

Figure 2.11: ‘The Dancing Red Man’ campaign ............................................................................................... 30

Figure 2.12: Examples of components of countermeasures ............................................................................ 32 Figure 2.13: An illustration of Volvo’s pedestrian and cyclist detection technology ......................................... 33


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Austroads 2016 | page 1

1. Introduction

1.1 Background and aims

This report reflects the results of CARRS-Q research in response to an Austroads tender to investigatepedestrian distraction – specifically due to use of technological devices or due to alcohol impairment – whileinteracting with the road environment.

This report documents the methods and findings in relation to each of the nine key project objectives:

1. Identify community and pedestrian attitudes to safe pedestrian behaviour

2. Identify factors that influence pedestrian behaviour and safety

3. Identify areas of road safety concern

4. Identify those pedestrian groups most at risk of distracted walking and the locations where risk isincreased

5. Ascertain current levels of knowledge of the contribution to distracted walking or impairment by alcohol,drugs or fatigue

6. Summarise current measures to influence pedestrian behaviour

7. Explore personal, social and environmental factors that influence pedestrian behaviour and particularlyuse of potentially distracting technology while walking

8. Identify or propose countermeasures that have the potential to address community and pedestrianattitudes and perceptions of risk in relation to distracted walking and crossing, or that encourage safepedestrian behaviour, especially in the context of Australia and New Zealand

9. Identify areas for future research in the distracted walking/pedestrian area

1.2 Structure of the report

To address Objectives 1-3, 5 and 6, the report begins with a critical review of the Australian, New Zealandand international literature in relation to pedestrian distraction. This is presented in Chapter 2.

The background to the problem is firstly presented as a way of introducing the nature of pedestrian safetyresearch. The scope of the review is also outlined in this section. An overview of factors that influencepedestrian behaviour and safe road use is given and evidence for the extent or prevalence of pedestriandistraction from technology and alcohol impairment as well as known or suspected impact on safety is

summarised. In particular, evidence relating to the types of locations where distracted walking is most likelyis presented. Finally, countermeasures to distracted or risky pedestrian behaviour that have been trialled in

Australia or New Zealand and evidence of their effectiveness is discussed. These includes engineeringcountermeasures to problem pedestrian behaviours and recent developments that have taken place indetailed real time intersection traffic analysis (including pedestrian traffic) via video technology. Othertechnological advancements and the issue of pedestrian visibility and conspicuity have also been brieflysummarised.


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In order to meet Objectives 1, 2, 4 and 7, for this project, two empirical studies were conducted: an interceptinterview and an online survey. A description of the rationale, approach, method, results and findings fromthese studies are presented in Chapter 3. The focus in the intercept interview was on estimating the extent towhich pedestrians are exposed to potential crash risk as a result of using technology, that is, the size of theproblem. An additional aim was to identify factors that might be influential in relation to distracted pedestrianbehaviour so that these might inform the design of the subsequent study (the online survey). The online

survey was intended to obtain a larger sample of pedestrians drawn from a more diverse, broader and largerpopulation of pedestrians than was possible in the intercept interviews.

Lastly, objectives 8 and 9 are addressed in the conclusions and recommendations in Chapter 4.


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2. Literature Review

2.1 Background and Scope

Unlike driving, where public activity is completely regulated and there are access restrictions (i.e. driverlicensing), walking is a natural and largely unregulated activity undertaken from childhood onwards as anincidental part of everyday life. Crossing or walking along roads forms a minor part of total walking, butpresents the highest risk because of the interaction with motor vehicles. Pedestrians are therefore animportant vulnerable road user group and represent, globally, 22% of all road deaths (WHO 2013). In

Australia, pedestrians represent approximately 14% of road fatalities, accounting for 2,022 deaths in the tenyears 2003-2012 (calculated from data reported in BITRE 2013). In New Zealand the proportion ofpedestrian deaths is around 11% of the annual road deaths, with 33 pedestrians being killed in 2012 andmore than 370 pedestrians killed over the 2003-2012 period (Ministry of Transport 2013).

In addition to their relatively low mass (compared to motorised traffic), pedestrians are also renderedvulnerable by their inherent lack of protection in a crash. This is exacerbated by factors which increase thelikelihood of pedestrian interaction with motorised traffic. For these reasons, use of roads by pedestrians isregulated to some extent, and certain regulations protective of pedestrians apply to drivers. However, illegaluse of the road by pedestrians is widespread (e.g. 20% of crossings at signalised intersections at a sampleof sites in Brisbane: King, Soole & Ghafourian 2009) and enforcement is rare for logistical reasons. Inaddition, most road crossing requires pedestrians to integrate visual and auditory information, make

judgements of speed and driver intention, and decide when it is safe to cross within the constraints of theirwalking speed and ability to vary it. Even for pedestrians who can successfully integrate this informationunder normal circumstances, distraction or temporary impairment (e.g. from alcohol) can interfere with thedecision making process at a range of points – pedestrians may fail to notice important auditory or visualinformation, or make incorrect judgements of speed (especially where multiple lanes or vehicles areinvolved), or incorrectly make an attribution of driver intention, or misjudge their own ability to get across in a

given gap. The challenges for pedestrians also vary by location, with signalised crossings requiring the leastdecision-making, while mid-block crossing of busy multi-lane roads with no centre median arguablypresenting the greatest challenge. Additional contextual factors such as night-time, lack of street lighting orrain are also contributing factors.

There is little evidence that pedestrians lack knowledge of the rules for crossing roads (King, Soole &Ghafourian 2009). A particular group most likely to be ignorant of the rules, children, receive education aboutcrossing roads in school. The reasons for unsafe road use are therefore more likely to lie with pedestrianmotives, their general walking behaviours (given that road crossing is a small element of walking activity) andother activities or practices to which walking is incidental. Some examples are:

1. engaging in a conversation while walking and focusing on the personal interaction so that attention toenvironmental changes is reduced;

2. listening to music or a podcast through headphones so that ability to hear vehicles is reduced;

3. leaving a venue at which alcohol has been consumed to walk somewhere else and having impaired abilityto make decisions about safe crossing;

4. hurrying to meet a time-critical deadline (e.g. a bus departure) such that there is increased willingness toaccept potential risks in accuracy of judgements about driver intention or available gaps.


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These examples illustrate that distraction and impairment both have the potential to exacerbate crash risk forpedestrians. With the growth in use of mobile technology for entertainment and communication, pedestriansare increasingly likely to ‘multitask’ while walking, elevating the risk of distraction during road use. The areaof pedestrian distraction is not well researched, and focuses on crash outcomes (whether pedestrians whowere distracted generally by mobile phones have a higher risk) or observed behaviour (proportion ofpedestrians distracted and association with unsafe behaviour). There is little research into motivations or

attitudes that might influence or inform countermeasure development. There is even less research into theimpairment of pedestrians by drugs or fatigue (Tulu et al. 2013). In contrast, studies into the factors affectingdrink walking are growing.

Due to the nature of this research, many of these studies have adopted observational methodologies.Because this relies purely on observed behaviour, it is somewhat limiting in terms of what we can infer aboutunderlying reasons for the behaviour. Such studies do, however, provide useful insights into how peopleactually behave in a real world setting. More recently, not without their own limitations, throughadvancements in simulation technology there has been an increase in the number of studies that haveexamined pedestrian behaviour using a ‘virtual’ environment in which various aspects of the experimentaldesign can be manipulated, such as vehicle speed and gaps in which people choose to cross. Analyses ofcrash data also provide useful information about the demographic and geographic characteristics ofpedestrian incidents. Advances in statistical and geospatial modelling techniques also offer novel insight into

where and when pedestrian crashes occur and where countermeasures should therefore be focused.

For the purpose of this review, we have excluded literature that relates specifically to primary-school agedchild pedestrians, brain injured pedestrians, disabled pedestrians, those with a specific visual impairment,and pedestrian behaviour at railway level crossings. We have drawn from national and international literature,with a focus on what countermeasures have been trialled in Australia and New Zealand, though materialfrom elsewhere around the world has been included where appropriate. Peer-reviewed and grey literaturehas been covered.

2.2 General factors that influence pedestrian behaviour and safe road

usePedestrian behaviour is influenced by a wide range of factors, including demographic factors such as gender(Gannon, Rosta, Reeve, Hyde & Lewis 2014; Rosenbloom, Nemrodov & Barkan 2004; Holland & Hill 2010;Tom & Granie 2011) and age (Oxley Fildes, Ihsen, Charlton & Day 1997; Dommes, Granie, Cloutier,Coquelet & Huguenin-Richard 2014¹; Cavallo & Dommes 2014; Dommes, Cavallo, Dubuisson, Tournier &Vienne 2014²; Dunbar 2012; Lobjois & Cavallo 2007). A range of other factors have also been examined.These include personality and attitudes (Schwebel, Stavrinos & Kongable 2009), self-identity (Holland, Hill &Cooke, 2009), conformity and group norms (McGhie, Lewis & Hyde 2012), socioeconomic factors (Zhuang &Wu, 2011; Tulu, Washington, King & Haque, 2013), cultural factors (Nordfjaern & Simsekoglu 2013;Rosenbloom, Shahar & Perlman 2008; Rosenbloom 2009) and other factors such as trip length and purpose(Kothuri, Clifton & Monsere 2014). The research into the influence of these factors is presented in Appendix A.

As seen in the evidence presented in Appendix A, pedestrian safety is influenced by multiple factors,including demographics such as gender and age, social conformity and group identity, and cultural orreligious factors. Age-related declines in performance of crossing tasks have been widely documented, withperceptual and cognitive deficits arguably playing a substantial role in many of the older pedestrian crashes(Oxley et al 1997). The adoption of compensating strategies as well as the apparent improvement seen inthe simulator-based training of older pedestrians (Dommes et al 2012) offers promise when considering whatcan be done to improve the safety of this at-risk group. Gender differences in visual search strategies anddecision making is another well researched influence on pedestrian safety (Tom & Granie 2011).Understanding how gender roles influence the internalisation of traffic rules is important when consideringhow to reduce pedestrian crashes (Granie 2009).


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The potential influence of group differences in terms of attitudes, beliefs, perceived risk and intendedbehaviour also has important implications for road safety (Holland & Hill 2007; Yagil 2000). Understanding,for example, potential motives or intentions among young adults is important when considering thatindividuals with lower perceptions of risk of negative outcomes from impaired walking, who hold positiveattitudes towards this behaviour or who perceive it as something that their friends/family engage in orapprove of, are more likely to drink walk (McGhie et al 2012, Gannon et al 2014, discussed in detail in the

next section). Personality or temperament factors may provide a way of targeting at-risk pedestrians forintervention purposes (Schwebel et al 2009).

Finally, though less relevant to Australia, our understanding of pedestrian behaviour in developing countriesis in need of development in order to have a positive impact on the high burden of injury and death in suchcountries, especially where rapid motorisation is occurring. Learning more about these less well understoodcontributing factors will be an important way forward for these regions where pedestrian risk is so high (Tuluet al 2013). The consideration of cultural factors in different regions will also be important for future researchand countermeasure development.

The main focus of this report is distraction from the use of technology (Nasar, Hecht & Wener 2008; Hatfield& Murphy 2007; Stavrinos, Byington & Schwebel 2009; Neider, McCarley, Crowell, Kaczmarski & Kramer2010) and impairment resulting from alcohol use (Gannon, Rosta, Reeve, Hyde & Lewis 2014; Lang, Tay,

Watson, Edmonston & O’Connor 2003; McGhie, Lewis & Hyde 2012). The literature regarding these factorsis reviewed in the sections which follow.

2.3 Pedestrian distraction from use of hand-held technologicaldevices

In Australia, there are 11.9 million adult smart phone users (Australian Communications and Media Authority2013) and 94% of young adults aged 18-24 years old use a mobile phone (Department of BroadbandCommunications and Digital Economy 2008). Madden et al (2013) showed that 78% of American teenagers(aged 12-17 years old) now have a mobile phone, with almost half of these owning smart phones. In the

Netherlands, nearly every young person has a mobile phone; including nearly one quarter of 8 year olds,45% of nine years olds, 60% of 10 year olds, and 69% of 11 year olds (SWOV 2013).

Given these high mobile phone ownership levels, it is not surprising that recent years have seen thepublication of several studies that have investigated the effects of such technological devices on pedestrianinjury risk. Prevalence of pedestrian use of mobile phones has been examined in terms of texting, having aconversation while holding the phone, and using the internet. Many of these studies involve the observationof pedestrians at intersections or midblock crossings. More recently other studies have used virtualenvironments to manipulate aspects of the pedestrian task experimentally. Analysis of hospital or police-reported crash data has provided some insight into pedestrian injury according to demographic as well asgeographic and socioeconomic factors. Literature in these areas is reviewed next.


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2.3.1 Mobile phone use

Several studies report the deleterious effects of mobile phone use on crossing behaviour and safety,especially among younger people. US hospital emergency data shows an increase in the proportion ofpedestrian injuries that involved distraction from mobile phones in recent years (Nasar & Troyer, 2013).Mobile phone use while crossing the road and mobile phone-related injuries have been found to be higher

for people under the age of 31 years (Nasar & Troyer, 2013; Nieuwesteeg & McIntyre, 2010) and particularlyhigh among teens (Ferguson, Green & Rosenthal, 2013. In research on teenage pedestrian distraction frommobile phones, a US report published by Safe Kids Worldwide reported observations of 34,000 students atroad crossings in front of schools and findings from discussion groups with over 2,400 students. Twentypercent of high school students and 12% of middle school students were observed crossing the street whiledistracted. Students were most often texting on a phone (39%), or using headphones (39%). A further 20%were talking on their mobile phone. Girls were 1.2 times more likely than boys to be walking while distracted,with 17% of girls observed to be distracted and 14% of boys. The odds of being distracted were found to be26% higher if there was a traffic light present, suggesting that teens may be more willing to use technologywhen they perceive their surroundings to be safe. Results from the discussion groups revealed that half ofstudents (49%) reported using a mobile phone while walking to school. Four out of ten reported listening tomusic while walking to school. Seventy eight percent of students reported that it’s a problem for children ofother ages, not children their own age (Ferguson et al 2013).

Earlier observational studies have shown that between 8-33% of pedestrians perform a distracting activingwhile crossing the road and that mobile phone users cross unsafely into oncoming traffic more often thanother observed pedestrians (Nasar, Hecht & Wener, 2008; Thompson, Rivara, Ayyagari & Ebel, 2013;Bungum, Day & Henry, 2005; Hatfield & Murphy, 2007; Cooper, Schneider, Ryan & Cox, 2012; Basch, Ethan,Rajan & Basch, 2014; Brumfield & Pulugurtha, 2011). Thompson et al (2013) observed 1,101 pedestriansand found that nearly one third (29.8%) performed a distracting activity while crossing (6.2% talking on ahandheld phone (phone to ear), 11.2% listening to music and 7.3% texting). Text messaging, talking on amobile phone, and talking with a companion increased crossing time. Bungum et al. (2005) observed 866individuals and found that approximately 20% of pedestrians were distracted in some way as they crossedthe street. Further, only 13.5% of walkers looked left and right while crossing the street and waited on thecurb until the light had turned green before stepping into the intersection. Using a case-control design to

match for time and demographics (gender and approximate age), Hatfield and Murphy (2007) observed 546pedestrians at signalised and unsignalised pedestrian crossings in three Sydney suburbs (reflecting low,medium and high socio-economic status). Results showed that, of the 182 pedestrians who were using theirmobile phones while crossing, 140 were talking on a handheld mobile, six were talking on a hands-freemobile and 36 were texting. They found that among females, pedestrians who crossed while talking on amobile phone crossed more slowly, and were less likely to look at traffic before starting to cross, to wait fortraffic to stop or to look at traffic while crossing, compared to matched controls. For males, pedestrians whocrossed while talking on a mobile phone crossed more slowly at unsignalised intersections. Cooper et al(2012) observed 12 intersections in San Francisco and found that 8% of pedestrians used their mobiledevices while crossing. Female pedestrians were more likely than males to talk on their mobile phone whilecrossing the street, but males were more likely to violate traffic signals while walking (or bicycling). Basch etal (2014) observed the 10 intersections with the highest frequency of pedestrian-motor vehicle collisions in

Manhattan, New York. More than one in four of the 3784 pedestrians observed was distracted by mobileelectronic devices while crossing dur ing the ‘walk’ (28.8%) and ‘don’t walk’ (26.3%). In a study conducted atthe University of North Carolina (UNC), Brumfield and Pulugurtha (2011) observed seven midblock crossingsat the University campus. Results showed that 29% of pedestrians were noticeably distracted while crossingthe road (16% talking on a mobile phone and 7% texting). While they also found that the chance of conflictwas the same for distracted and attentive pedestrians, the study also showed that drivers were 40% morelikely to give way to distracted pedestrians than to those who appeared attentive. In an experimentalmovement study that compared normal walking with reading or writing text messages while walking,Schabrun, Van den Hoorn, Moorcraft, Greenland and Hodges (2014) found that, among other posture-related variables, when participants read or wrote a text, they walked at a slower speed.


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Experimental studies have shown the effects of distraction and inattention caused by mobile phone usewhen crossing the road (Stavrinos, Byington & Schwebel, 2009; 2011; Masuda, Sekine, Sato & Haga, 2014;Hyman, Boss, Wise, McKenzie & Caggiano, 2010). Stavrinos et al (2009) examined mobile phoneconversations among college students, using two experiments in a simulated task. Their first experimentexamined whether pedestrians would display riskier behaviour when distracted by a naturalistic mobilephone conversation. Results showed that, when distracted, participants exhibited significantly riskier

behaviour for three of the four variables measured. That is, they left significantly less time to spare, missedmore opportunities to cross, and were hit or almost hit significantly more times than when not distracted.Interestingly, the fourth variable, attention to traffic, was not affected by distraction; participants made theappropriate motions to look left and right before crossing, but at some point during the decision makingprocess, failed to actually capture/process the information adequately.

In a second experiment, Stavrinos et al 2011 examined the impact of three types of distraction on pedestriansafety: engaging in a cell phone conversation, engaging in a cognitively challenging spatial task by phone,and engaging in a cognitively challenging mental arithmetic task by phone. Results of this experimentshowed that all forms of distraction resulted in significantly riskier pedestrian behaviour across all fourvariables measured (time left to spare, missed opportunities, attention to traffic, hits/close calls). Resultsindicated that a naturalistic cell phone conversation was generally just as detrimental as more cognitivelydemanding tasks such as counting backwards by threes or engaging in a spatially focused conversation

(Stavrinos et al 2011).

Masuda et al (2014) investigated the inattention of pedestrians due to using a numerical or touch screenmobile phone for texting. Twenty-four university students participated in a manipulation of mobile phone taskand signal detection talk while walking around a 3x3 meter square. They were instructed to walk at a fasterpace than usual whilst also maintaining as straight a line as possible, and turn right at the corners. In fourconditions (texting, talking, a more interactive cognitive task, and a control), auditory and visual signals werepresented to the participants as they walked and they were asked to press a mouse key when the visualsignal changed colour or when the auditory pitch changed. Reaction time to either visual or auditory signalwas significantly longer under the three experimental conditions than in the control. The number of errorswas also larger in the mobile phone use conditions. Finally, Hyman et al. (2010) investigated the effects ofdivided attention during walking. In the first of two studies, they observed the walking behaviour of individuals

talking on a mobile phone, individuals walking with no electronic devices, individuals walking and listening toa music device (as a different type of divided attention), and individuals walking in pairs (conversationaldivided attention). Results showed that mobile phone users walked more slowly, changed directions morefrequently, and were less likely to acknowledge other people than those in the other conditions. In a secondstudy, these researchers investigated the possibility that talking on a mobile phone leads to inattentionblindness, as measured by whether individuals noticed an unusual stimulus – a unicycling clown. Mobilephone users were less likely to notice this stimulus along their walking route.

2.3.2 Personal music devices

The effect of portable personal music devices on pedestrian behaviour has also been investigated withmixed results that suggest that walking while listening to music with headphones represents a different type

of distraction from that of using a mobile phone for talking or texting (Walker, Lanthier, Risko & Kingstone,2012). Contrary to results in relation to distraction from talking on a mobile phone, an observational study of347 pedestrians at a university campus showed that among males, pedestrians listening to personal musicdevices (not selecting a song or playing with the actual device) displayed more looking behaviour (asmeasured by number of head movements left and right) than those not listening to such devices. Femalesshowed no differences in looking behaviour for the two conditions (Walker et al., 2012).


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Simulator studies have also produced contrasting results, finding that distracted participants are more likelyto look away from the street environment (and look toward other places such as their telephone or musicdevice) than undistracted participants (Schwebel, Stavrinos, Byington, Davis, O’Neal & de Jong, 2012). Aftercontrolling for demographics, walking frequency and frequency of media use, Schwebel et al (2012)examined the effect of talking or texting on a mobile phone with that of listening to music throughheadphones among college students and showed that distracted participants in all conditions were more

likely to be hit by a vehicle than those who were not distracted. Neider, McCarley, Crowell, Kaczmarski andKramer (2010) compared the effects of talking on a mobile phone with that of using portable music devices.Using a virtual environment and a within-subjects design, 36 pedestrians navigated through a series ofunsigned intersections in three conditions: no distraction, listening to music through headphones, andconversing on a mobile phone using a hands free device. Participants were less likely to successfully crossthe road when conversing on a mobile phone than when listening to music, even though they took more timeto initiate their crossing than when conversing on a mobile phone. Neider, Gaspar, McCarley, Crowell,Kaczmarski and Kramer (2011) went on to assess these two behaviours among older adults using anothersimulated task. Results showed that older adults were more vulnerable to dual-task impairments thanyounger adults when the crossing task conditions were more difficult.

2.3.3 Internet use

With the increasing popularity of smart phones, the effect of mobile internet use on pedestrian injury has alsoreceived some attention. In their national survey of 802 teens aged 12-17 years, Madden et al (2013)showed that 37% of American teens have a smart phone, up from just 23% in 2011. Findings also showedthat 74% of teens aged 12-17 reported accessing the internet on mobile phones, tablets, and other mobiledevices at least occasionally. One in four teens are “mobile-mostly” internet users and older girls areespecially likely to report this (34% compared to 24% of boys). Byington and Schwebel (2013) conducted astudy to investigate crossing behaviour while accessing the internet among 92 college students. In a virtualenvironment, participants crossed a street 20 times, half the time while undistracted and half the time whileconducting a mobile internet task. When distracted, participants waited longer to cross the street, missedmore safe opportunities to cross, took longer to initiate crossing when a safe gap was available, looked leftand right less often, spent more time looking away from the road and were more likely to be hit or almost hit

by an oncoming vehicle. Furthermore, participants reported using mobile internet with great frequency indaily life, including while crossing the road. Results were controlled for gender, age, ethnicity, and pedestrianand mobile internet experience.

2.4 Locations where pedestrian crashes are most likely

Pedestrian crossings can be grouped according to two main locations: at an intersection (signalised orunsignalised) or in the middle of a block (‘midblock’). Most of the research into the locations of pedestriancrashes is centred on these locations, with the majority of pedestrian fatalities shown to occur at non-intersection locations (Balk, Bertola, Shurbutt & Do 2014; Kim, Brunner & Yamashita, 2008; Gitelman,Balasha, Carmel, Hendel & Pesahov, 2012). Other locations include car parks, driveways, footpaths,

underpasses or overpasses, and standing or sitting on the road.

While signalised intersections appear to be safer for pedestrians than uncontrolled intersections, they are stilldangerous situations for pedestrians. A recent UK report identified that 25% of pedestrian deaths andserious injuries in London occur at pedestrian crossing facilities (e.g. pelican, puffin or zebra crossings) ofwhich half (13%) were signalised (Greater London Authority, 2014). A recent examination of Victorian police-reported casualty pedestrian collisions in Melbourne CBD (for the period 2000-2011) showed that almost halfof pedestrian-involved crashes occurred at signalised intersections (Oxley, Yuen, Corben, Hoareau & Logan,2013). Being struck on either the ‘near side’ (that is, the beginning of crossing from the pedestrianperspective) or ‘far side’ (that is, the end of the crossing task from the pedestrian perspective) of the road isanother location variable that has been examined. The Transport Accident Commission (TAC) in Victoriareported that between 2009 and 2013, of the 209 pedestrian fatalities (in Victoria), 32% involved thepedestrian crossing the road and being struck from the near side, 19% from the far side and 13% while

playing, working, lying or standing on a carriageway (TAC 2014).


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From a safety perspective, understanding the characteristics of where pedestrians prefer to cross a road,conditions under which they decide to cross and compliance with traffic control are critical (Sisiopiku & Akin2003). This section will focus on those studies that have investigated the location of pedestrian crashes. Thefollowing section will cover those studies that have investigated built environment, situational andenvironmental factors.

In a Victorian study that conducted 200 telephone interviews with injured pedestrians, Nieuwesteeg andMcIntyre (2010) found that pedestrians were usually not at fault when crossing at intersections, but wereusually at fault when crossing midblock. Right turns at an intersection were found to be particularlyproblematic, with vehicles not giving way to pedestrians. Near-side crashes were common in midblock caseswith parked cars highlighted as a risk factor. Respondents were aged 16-39 years (n=110) and 60 years andover (n=90).

Based on observations in Brisbane, King, Soole and Ghafourian (2009) showed that most pedestrianswaited for the ‘green man’ signal before crossing and the most common illegal behaviour was crossing awayfrom the signals but within 20m, followed by crossing against a ‘flashing red man’, then against a ‘steady redman’. Relative risk ratios were calculated using crash data for the observation sites over an 11 year period.This showed that crossing against the lights and crossing close to the lights both had a crash risk percrossing event of approximately eight t imes that of legal crossing at signalised intersections. Using

observations and survey data, pedestrian behaviour at, and perceptions towards, various facilities (includingsignalised and unsignalised intersections and midblock crossings) was examined by Sisiopiku and Akin(2003). Video footage was collected at the footpaths of the study site, and perceptions and preferenceinformation was collected through surveying users of the site. Results showed that most pedestrians crossedat a designated location, and compliance at crossings throughout the study site was 71.4%. Markedmidblock crossings were found to be the most ‘influential’ pedestrian facility. It was also evident that thecrossing location, relative to the origin and destination of the pedestrian, was influential on pedestriandecisions to cross at a designated location. The effect of the availability of a pedestrian signal on pedestriandecisions to cross at a specific location was quite high (74%) (Sisiopiku & Akin 2003).

Alhajyaseen, Asano and Nakamura (2013) recorded video footage at several signalised intersections inJapan to analyse the gap acceptance behaviour of left-turning traffic and their interactions with pedestrians.

They found that drivers accepted shorter lags in the presence ofsingle pedestrians while being moreconservative in relation to the gaps between multiple pedestrians. Findings suggest that drivers pay moreattention as the number of pedestrians increases. Drivers also accepted shorter gaps between pedestrianscrossing the near-side for the driver when compared to gaps between pedestrians on the far side (relative tothe driver). The authors attributed this to the lower visibility of near-side pedestrians and their relativeposition to the driver’s line of sight while turning, whereas far -side pedestrians can be easily seen by left-turners.

The relationship between marked crossings (i.e. zebra crossings) and pedestrian crash risk is unclear. In thepresence of a marked crossing it can be argued that pedestrians may falsely believe they are safer and thusattempt to cross the road without due caution (WHO 2013). Results from a before and after, observationaland questionnaire-based evaluation of the installation of a marked crossing in Edinburgh (Havard & Willis,2012) suggested that pedestrians were more likely to use the location to cross the road, waited less time tocross and walked more slowly after the zebra crossing had been installed than before. Pedestrians reportedthat they felt safer, and less vulnerable to traffic, and were more confident.

A large matched-case study on pedestrian crossings provides evidence that the infrastructure factorsinfluencing crash risk may be complex. This study involved 1000 marked crossings and 1000 matchedunmarked comparison sites in the USA (US Department of Transportation, Zegeer, Stewart, Huang &Lagerwey, 2002). Legal crossings exist at all public intersections in the USA wherever there is a footpath onat least one side of the street. Midblock crossings can only exist if specifically marked. The study sites wereintersection or midblock locations with no traffic signals or stop signs on the main road approach(uncontrolled locations). The marked crossings had one of six marking patterns (i.e. zebra, dashed lines or aladder pattern). Very few had any type of supplementary warning signs and none had traffic calmingmeasures or special pedestrian devices. The matched unmarked intersections sites were typically the

opposite leg of the same intersection as the selected marked crossing site or a midblock location on thesame street, usually a block or two away. Results showed that on two-lane roads, the presence of a markedcrossing alone at an uncontrolled location was associated with no difference in pedestrian crash rate,


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compared to an unmarked crossing. On multi-lane roads with high traffic volume, having a marked crossingalone was associated with a higher pedestrian crash rate compared to an unmarked crossing. Findingsindicate that marked crossings should not be implemented without additional safety measures (Zegeer, et al.,2002).

2.5 Influence of the built environment on likelihood of pedestriancrashes

Although research on pedestrian distraction by smart phones is growing, the topic is relativelyunderdeveloped compared to other aspects of pedestrian safety, and the authors did not find any that relatedspecifically to how features of the built environment influence likelihood of pedestrian distraction. Researchon drink walking is also relatively scant, though blood alcohol levels for pedestrians is included in officialcrash data where testing has been carried out. Accordingly, this section summarises research on the impactof features of the built environment on pedestrian crash frequency rather than tendency for pedestrians to bedistracted. Aside from the location of crossings in terms of whether they are at an intersection or midblock,various aspects of the built environment can affect pedestrian behaviour and safety. These include whetherfootpaths or off road walking areas are provided, land use characteristics (i.e. employment density)

demographics (i.e. population density), transit supply (i.e. presence of metro stations) and road networkcharacteristics (i.e. number of intersections and speed limits) (Miranda-Moreno, Morency & El-Geneidy 2011).Pedestrian collisions occur more frequently in urban areas than rural settings in high income countries, andthe opposite is true in some low and middle income countries (WHO 2013).

2.5.1 Land use

Alavi (2013) found that pedestrian crash risk is multi-factorial in nature, with different sets of factorsassociated with risk across different space-time frameworks. In his analysis of pedestrian safety in theMelbourne CBD, the three most powerful predictors of pedestrian collision rate (during daytime hours) wereland uses surrounding the intersection (floor space area of entertainment areas and number of legs withshops), road characteristics (major versus minor intersections, type of division of roads, hook turn possibilityand percentage of left-turn movements) and public transport (number of bus stops and distance from nearestrailway station). During hours of darkness, collision rate was highly correlated with the land usecharacteristics (floor space area of entertainment areas, capacity of amusement and gaming centres,capacity of cinemas, theatre, concert halls and stadiums, capacity of accommodation) and roadcharacteristics (major versus minor intersections and type of division of roads). For midblock locations, themost powerful predictor of pedestrian collisions during the day was public transport (distance from nearestrailway station, number of tram stops and routes, number of bus stops and routes) followed by the land use(density of shops, floor space area of offices, capacity of non-commercial accommodation and floor spacearea of entertainment areas) and road characteristics (number of driveways and length of midblock). At nightthough, midblock collisions were mainly predicted by the length of the midblock, followed by land use (floorspace areas of non-commercial accommodation, capacity of cinemas, theatres, concert halls and stadiums,floor space area of entertainment areas and capacity of bars, taverns and pubs) and public transport

characteristics (number of tram stops). Various factors were associated with injury severity. At anintersection, the correlates of major trauma were time of day, vehicle movement, pedestrian age, vehiclecolour, and land use and public transport characteristics. At midblock, correlates were time of day, land usecharacteristics, and the interaction of day or week and speed zone.


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In earlier work, Oxley et al (2003) found that in Melbourne CBD during night hours, particularly on weekends,pedestrian collisions were clustered around night clubs and bars and involved a higher proportion of youngadult males crossing at intersections. In contrast, collisions occurring during business hours were evenlydistributed throughout weekdays, across multiple locations on streets, more prevalent around publictransport facilities and with less severe injury outcomes than those occurring during night hours.Nieuwesteeg and McIntyre (2010) showed that pedestrians are usually injured in familiar locations that they

are likely to be confident with, and are most likely to be struck in shopping, residential and business areas,on routine type trips such as going to work, school or shopping. Schneider, Ryznar and Khattak (2004)combined pedestrian crash data with survey data from drivers and pedestrians on their perceptions ofsafety/danger around a university campus and showed that certain locations on the campus were perceivedas dangerous, though pedestrian crashes had not yet occurred there, while the actual locations of police-reported crashes were not perceived to be dangerous by pedestrians or drivers.

2.5.2 Road factors and speed limits

The impact of road factors such as number of traffic lanes, speed and location characteristics on pedestriancrashes has been investigated in several studies (Hanson, Noland & Brown, 2014; Wang, Haque, Chin &Yun, 2013; Balk et al 2014; Garder, 2004; Islam et al 2014; Luoma & Peltola, 2013). Many of these studies

have analysed crash data to identify which road factors have the biggest impact on pedestrian safety. Theseinclude an analysis of pedestrian crash data from the State of Maine for the five year period 1994-1998. Forthis study, analyses showed that 68% of all crashes occurred in clear weather, 75% occurred on dry roads,61% happened in daylight and 71% on level, straight roads with adequate sight distance. Most crasheshappened away from an intersection, and in the absence of any traffic control device or signage. However,19% occurred at a three-leg intersection, and a further 17% at a four-leg intersection. In a prediction model,high speeds and wide roads were found to lead to more crashes (Garder, 2004). Islam et al (2014) analysedpedestrian crash data from Connecticut and found that greater crossing distance and small building setbacks(distance at which buildings are located from the edge of the road) were associated with larger numbers ofpedestrian-vehicle crashes. The latter finding was counterintuitive in that vehicle speed is usually lower inareas where setback is small. Greater pedestrian activity and more complex interactions however mayaccount for this. Luoma and Peltola (2013) investigated the effect of walking direction along rural two-lane

roads on pedestrian risk. They analysed police-reported road crashes from Finland between 2006 and 2010in which a vehicle had struck a pedestrian walking along the road. They showed that the mean effect offacing traffic compared to walking along the direction of vehicle traffic was a 77% decrease in pedestrianaccidents. Wang, Haque, Chin and Yun (2013) examined pedestrian injuries in Singapore using crash datafrom 2003 to 2008, containing approximately 4000 pedestrian crashes. Severity of injury was found to behigher during night time, with the likelihood of fatal or serious injury being higher for crashes on roads withhigh speed limits, roads with multiple lanes, school zones, and roads with two-way traffic.

Lack of footpaths is common in suburban and regional areas in Australia, however there is little literature thatexamines the role this plays in pedestrian crashes. A small number of US studies have shown that thepresence or absence of footpaths plays a role in the probability of a crash occurring and the severity of theresulting injuries. A study using Police Accident Reports from 1993 to 1997 in New Hampshire found that theprobability of a crash is two times more likely at a site without a footpath than at a site with one

(Ossenbruggen, Pendharkar & Ivan, 2001). Hanson, Noland and Brown (2014) found that severity ofpedestrian casualties has been shown to be associated with lack of footpaths and street buffers (i.e. plantedareas, bike lanes, on-street parking) on one or both sides of the street, high speed roads, roads with six ormore lanes and a median, and lack of traffic lighting when it is dark. This study used data derived fromGoogle Street View as well as pedestrian casualty police record data in New Jersey between 2007 and 2009(Hanson et al 2014). Similarly, Yu (2015) used crash data from 2008 to 2012 in Texas and found that theprobability of a pedestrian being severely injured or killed in a crash decreased in areas with more footpaths.


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High density of licensed restaurants and venues for alcohol consumption may be associated with increasedpotential for vehicle accidents and possibly pedestrian injury in the US (Gruenewald et al 2002, cited in Palk,Davey, & Freeman 2009). In a spatial analysis of pedestrian injury ‘hot spots’ in San Francisco, LaScala,Gerber and Gruenewald (2000) showed a positive correlation between reported ‘had been drinkingpedestrian collisions’ and number of bars per kilometre of roadway. In Australia, other studies suggest thatwhen alcohol has been made more available through the introduction of extended liquor trading hours, there

is a positive relationship between alcohol availability, licenced premises and traffic accidents across anumber of Australian cities (Palk et al 2009). Palk et al (2009), also highlight that a number of cross sectionalstudies have demonstrated a similar close and positive relationship between the density of licensedpremises and motor vehicle crashes.

Balk et al (2014) analysed the characteristics of more than 70,000 crossings at 20 locations in theWashington DC metropolitan area. Among other findings, they showed that the longer the distance thatpedestrians were required to travel to cross the road, the more likely they were to cross entirely during thewalk phase of the light cycle. Pedestrians were more likely to cross during the ‘don’t walk’ phase on one-waystreets than on two-way streets. When physical barriers like guardrails and fences were present between theroadway and footpath, pedestrians were less likely to cross at unmarked non-intersection areas.

Finally, in an observational study of pedestrians in Florida (which has the highest pedestrian fatality rate in

the U.S), Gawade (2014), identified various site characteristics, demographics and pedestrian behaviours.Results showed that as intersection size increased, pedestrians were more likely to cross away fromcrossings. Users of a T-intersection were less likely to violate the pedestrian signal and jaywalk than userson four-segment intersections. Those who did not use the footpath also tended to cross on red and jaywalkrather than crossing on green; indicating that those who violate one pedestrian law may also tend to violateothers.

2.5.3 Environmental factors

Several studies have also investigated the effect of environmental conditions such as adverse weatherconditions (Li & Fernie 2010), low light conditions (King, Wood, Lacherez & Marszalek 2012) and night time

conspicuity (Tyrell, Wood & Carberry 2004; Yagil, 2000) on pedestrian crashes. The effect of winter weatheron pedestrian compliance rates has also been investigated with results showing that road crossing behaviourwas less safe in inclement weather conditions than in fine weather (Li & Fernie,2010). Most pedestrianfatalities are found to occur during the evening or at night time (Stimpson, Wilson & Muelleman 2013;Cairney & Coutts 2003; Eichelberger, Cicchino & McCartt 2013; Harruff, Avery & Alter-Pandya 1998). Arecent UK report that was launched at the 2014 National Road Safety Conference analysed the injury datafrom 30,000 adult pedestrians who were injured in road collisions between 6pm and 6am during the period2009-2013. Results showed that males were at greatest risk of being injured as a pedestrian at night; thatcasualties often lived in similar types of communities; and that their actions often contributed to the collisionthrough alcohol impairment, wearing dark clothing and/or dangerous actions in the carriageway (Road SafetyGreat Britain 2015). Yagil (2000) found that darkness was perceived to be the most influential physical factorfor pedestrian safety and increased the tendency to wait for the ‘walk’ sign.

The influence of optimism and group-serving interpretations of safety was investigated among pedestrians(and cyclists) in terms of road use in general and under low light conditions with results supporting theexistence of a group-serving bias, both overall and under the low light conditions (King, Wood & Marszalek,2012). The authors presented the example that pedestrians and cyclists show a lower level of agreementthan drivers with the statement that ‘it is dangerous for pedestrians and cyclists to use the road in low lightingconditions’. An implication of this finding is that pedestrians and cyclists overestimate their visibility to othersat night. Tyrrell, Wood and Carberry (2004) examined pedestrian estimates of their own visibility in anexperimental closed-road circuit task and confirmed that pedestrians do indeed overestimate theirconspicuity to approaching drivers at night. Some authors have investigated the effect of daylight saving timewhich shifts an hour of daylight to the busier evening traffic hours on pedestrian fatalities (Ferguson,Preusser, Lund, Zador & Ulmer 1995).


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2.6 Walking after having

Documents

AP-R510-16 Distraction and Attitudes Towards Safe Pedestrian Behaviour