Application of a random effects negative binomial model to examine tram-involved crash frequency on route sections in Melbourne, Australia

Farhana Naznin; Graham Currie; David Logan; Majid Sarvi

doi:10.1016/j.aap.2016.03.012

Application of a random effects negative binomial model to examine tram-involved crash frequency on route sections in Melbourne, Australia

Accid Anal Prev. 2016 Jul:92:15-21. doi: 10.1016/j.aap.2016.03.012. Epub 2016 Mar 29.

Authors

Farhana Naznin¹, Graham Currie², David Logan³, Majid Sarvi⁴

Affiliations

¹ Institute of Transport Studies, Department of Civil Engineering, Building 60, Monash University, Clayton, Victoria 3800, Australia. Electronic address: farhana.naznin@monash.edu.
² Institute of Transport Studies, Department of Civil Engineering, Building 60, Monash University, Clayton, Victoria 3800, Australia. Electronic address: graham.currie@monash.edu.
³ Monash University Accident Research Centre, Building 70, Monash University, Clayton, Victoria 3800, Australia. Electronic address: david.logan@monash.edu.
⁴ Department of Infrastructure Engineering, The University of Melbourne, Australia. Electronic address: majid.sarvi@unimelb.edu.au.

PMID: 27035395
DOI: 10.1016/j.aap.2016.03.012

Abstract

Safety is a key concern in the 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 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 analyze crash frequency data obtained from Yarra Trams, the tram operator in Melbourne. The RENB modelling approach can account for spatial and temporal variations within observation groups in panel count data structures by assuming that group specific effects are randomly distributed across locations. The results identify many significant factors effecting tram-involved crash frequency including tram service frequency (2.71), tram stop spacing (-0.42), tram route section length (0.31), tram signal priority (-0.25), general traffic volume (0.18), tram lane priority (-0.15) and ratio of platform tram stops (-0.09). 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: Random effects negative binomial model; Safety; Tram-involved crash frequency.

MeSH terms

Accidents, Traffic / statistics & numerical data*
Australia
Environment Design / statistics & numerical data*
Humans
Models, Statistical
Motor Vehicles
Railroads
Safety