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

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.