Examining route section level tram-involved crash frequency using the random effects negative binomial model

Farhana Naznin, Graham Currie, David Logan, Majid Sarvi

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)

Abstract

Safety is an overriding concern in design, operation and development of light rail systems including trams or streetcars as they impose crash risks on road users in terms of crash frequency and severity. The aim of this study is to identify the key traffic, transit and route factors that influence tram-involved crash frequencies along tram route sections in Melbourne. A random effects negative binomial (RENB) regression model was developed to analyse crash frequency data obtained from Yarra Trams, the tram operator in Melbourne tram network. The RENB modelling approach can account for spatial and temporal variations within observation groups in panel count data structures by assuming that the group specific effects are randomly distributed across locations. The results identify many significant factors affecting crash frequency. They are, in order of affect; tram stop spacing (-0.43), tram route section length (0.31), tram signal priority (-0.263), general traffic volume (0.17) and tram lane priority (-0.148). Platform stops (-0.09) and service frequency (0.004) also influence crash frequency. Findings provide useful insights on route section level tram-involved crashes in an urban tram or streetcar operating environment. The method described represents a useful planning tool for transit agencies hoping to improve safety performance.
Original languageEnglish
Title of host publicationAustralasian Transport Research Forum 2015 Proceedings
EditorsS Travis Waller, Hanna Grzybowska, Emily Moylan, Matthew Jones, Sherri Fields
Place of PublicationSydney NSW Australia
PublisherAustralasian Transport Research Forum
Pages1-10
Number of pages10
Publication statusPublished - 2015
EventAustralasian Transport Research Forum 2015 - Sydney, Australia
Duration: 30 Sep 20152 Oct 2015
Conference number: 37th
http://atrf.info/conference.aspx

Conference

ConferenceAustralasian Transport Research Forum 2015
Abbreviated titleATRF 2015
CountryAustralia
CitySydney
Period30/09/152/10/15
Internet address

Keywords

  • Safety
  • Tram-involved crash frequency
  • Random effects negative binomial model

Cite this

Naznin, F., Currie, G., Logan, D., & Sarvi, M. (2015). Examining route section level tram-involved crash frequency using the random effects negative binomial model. In S. T. Waller, H. Grzybowska, E. Moylan, M. Jones, & S. Fields (Eds.), Australasian Transport Research Forum 2015 Proceedings (pp. 1-10). Sydney NSW Australia: Australasian Transport Research Forum.
Naznin, Farhana ; Currie, Graham ; Logan, David ; Sarvi, Majid. / Examining route section level tram-involved crash frequency using the random effects negative binomial model. Australasian Transport Research Forum 2015 Proceedings. editor / S Travis Waller ; Hanna Grzybowska ; Emily Moylan ; Matthew Jones ; Sherri Fields. Sydney NSW Australia : Australasian Transport Research Forum, 2015. pp. 1-10
@inproceedings{d4470d8ffc954154a7c34731da401de6,
title = "Examining route section level tram-involved crash frequency using the random effects negative binomial model",
abstract = "Safety is an overriding concern in design, operation and development of light rail systems including trams or streetcars as they impose crash risks on road users in terms of crash frequency and severity. The aim of this study is to identify the key traffic, transit and route factors that influence tram-involved crash frequencies along tram route sections in Melbourne. A random effects negative binomial (RENB) regression model was developed to analyse crash frequency data obtained from Yarra Trams, the tram operator in Melbourne tram network. The RENB modelling approach can account for spatial and temporal variations within observation groups in panel count data structures by assuming that the group specific effects are randomly distributed across locations. The results identify many significant factors affecting crash frequency. They are, in order of affect; tram stop spacing (-0.43), tram route section length (0.31), tram signal priority (-0.263), general traffic volume (0.17) and tram lane priority (-0.148). Platform stops (-0.09) and service frequency (0.004) also influence crash frequency. Findings provide useful insights on route section level tram-involved crashes in an urban tram or streetcar operating environment. The method described represents a useful planning tool for transit agencies hoping to improve safety performance.",
keywords = "Safety, Tram-involved crash frequency, Random effects negative binomial model",
author = "Farhana Naznin and Graham Currie and David Logan and Majid Sarvi",
year = "2015",
language = "English",
pages = "1--10",
editor = "Waller, {S Travis} and Hanna Grzybowska and Emily Moylan and Matthew Jones and Sherri Fields",
booktitle = "Australasian Transport Research Forum 2015 Proceedings",
publisher = "Australasian Transport Research Forum",
address = "Australia",

}

Naznin, F, Currie, G, Logan, D & Sarvi, M 2015, Examining route section level tram-involved crash frequency using the random effects negative binomial model. in ST Waller, H Grzybowska, E Moylan, M Jones & S Fields (eds), Australasian Transport Research Forum 2015 Proceedings. Australasian Transport Research Forum, Sydney NSW Australia, pp. 1-10, Australasian Transport Research Forum 2015, Sydney, Australia, 30/09/15.

Examining route section level tram-involved crash frequency using the random effects negative binomial model. / Naznin, Farhana; Currie, Graham; Logan, David; Sarvi, Majid.

Australasian Transport Research Forum 2015 Proceedings. ed. / S Travis Waller; Hanna Grzybowska; Emily Moylan; Matthew Jones; Sherri Fields. Sydney NSW Australia : Australasian Transport Research Forum, 2015. p. 1-10.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

TY - GEN

T1 - Examining route section level tram-involved crash frequency using the random effects negative binomial model

AU - Naznin, Farhana

AU - Currie, Graham

AU - Logan, David

AU - Sarvi, Majid

PY - 2015

Y1 - 2015

N2 - Safety is an overriding concern in design, operation and development of light rail systems including trams or streetcars as they impose crash risks on road users in terms of crash frequency and severity. The aim of this study is to identify the key traffic, transit and route factors that influence tram-involved crash frequencies along tram route sections in Melbourne. A random effects negative binomial (RENB) regression model was developed to analyse crash frequency data obtained from Yarra Trams, the tram operator in Melbourne tram network. The RENB modelling approach can account for spatial and temporal variations within observation groups in panel count data structures by assuming that the group specific effects are randomly distributed across locations. The results identify many significant factors affecting crash frequency. They are, in order of affect; tram stop spacing (-0.43), tram route section length (0.31), tram signal priority (-0.263), general traffic volume (0.17) and tram lane priority (-0.148). Platform stops (-0.09) and service frequency (0.004) also influence crash frequency. Findings provide useful insights on route section level tram-involved crashes in an urban tram or streetcar operating environment. The method described represents a useful planning tool for transit agencies hoping to improve safety performance.

AB - Safety is an overriding concern in design, operation and development of light rail systems including trams or streetcars as they impose crash risks on road users in terms of crash frequency and severity. The aim of this study is to identify the key traffic, transit and route factors that influence tram-involved crash frequencies along tram route sections in Melbourne. A random effects negative binomial (RENB) regression model was developed to analyse crash frequency data obtained from Yarra Trams, the tram operator in Melbourne tram network. The RENB modelling approach can account for spatial and temporal variations within observation groups in panel count data structures by assuming that the group specific effects are randomly distributed across locations. The results identify many significant factors affecting crash frequency. They are, in order of affect; tram stop spacing (-0.43), tram route section length (0.31), tram signal priority (-0.263), general traffic volume (0.17) and tram lane priority (-0.148). Platform stops (-0.09) and service frequency (0.004) also influence crash frequency. Findings provide useful insights on route section level tram-involved crashes in an urban tram or streetcar operating environment. The method described represents a useful planning tool for transit agencies hoping to improve safety performance.

KW - Safety

KW - Tram-involved crash frequency

KW - Random effects negative binomial model

UR - http://atrf.info/

M3 - Conference Paper

SP - 1

EP - 10

BT - Australasian Transport Research Forum 2015 Proceedings

A2 - Waller, S Travis

A2 - Grzybowska, Hanna

A2 - Moylan, Emily

A2 - Jones, Matthew

A2 - Fields, Sherri

PB - Australasian Transport Research Forum

CY - Sydney NSW Australia

ER -

Naznin F, Currie G, Logan D, Sarvi M. Examining route section level tram-involved crash frequency using the random effects negative binomial model. In Waller ST, Grzybowska H, Moylan E, Jones M, Fields S, editors, Australasian Transport Research Forum 2015 Proceedings. Sydney NSW Australia: Australasian Transport Research Forum. 2015. p. 1-10