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

• Published in: Accident analysis and prevention 2016-07, Vol.92, p.15-21
• Main Authors: Naznin, Farhana, Currie, Graham, Logan, David, Sarvi, Majid
• Format: Article
• Language: English
• Subjects: Accidents, Traffic - statistics & numerical data; Australia; Binomials; Crashes; Environment Design - statistics & numerical data; Humans; Mathematical models; Models, Statistical; Motor Vehicles; Priorities; Railroads; Random effects negative binomial model; Safety; Traffic engineering; Traffic flow; Tram-involved crash frequency; Trams; Transit
• ISSN: 0001-4575; 1879-2057
• DOI: 10.1016/j.aap.2016.03.012

•Key factors influencing tram-involved crash frequency was identified.•A random effects negative binomial model was adopted.•Major crash contributing factors were tram stop spacing and tram service frequency.•Other factors were tram route section length and general traffic volume.•The results showed positive road safety benefits of tram priority measures. 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.