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.
| Original language | English |
|---|---|
| Pages (from-to) | 15-21 |
| Number of pages | 7 |
| Journal | Accident Analysis and Prevention |
| Volume | 92 |
| DOIs | |
| Publication status | Published - 1 Jul 2016 |
Keywords
- Safety
- Tram-involved crash frequency
- Random effects negative binomial model
Cite this
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Application of a random effects negative binomial model to examine tram-involved crash frequency on route sections in Melbourne, Australia. / Naznin, Farhana; Currie, Graham; Logan, David; Sarvi, Majid.
In: Accident Analysis and Prevention, Vol. 92, 01.07.2016, p. 15-21.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Application of a random effects negative binomial model to examine tram-involved crash frequency on route sections in Melbourne, Australia
AU - Naznin, Farhana
AU - Currie, Graham
AU - Logan, David
AU - Sarvi, Majid
PY - 2016/7/1
Y1 - 2016/7/1
N2 - 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.
AB - 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.
KW - Safety
KW - Tram-involved crash frequency
KW - Random effects negative binomial model
UR - http://www.scopus.com/inward/record.url?scp=84961942647&partnerID=8YFLogxK
U2 - 10.1016/j.aap.2016.03.012
DO - 10.1016/j.aap.2016.03.012
M3 - Article
VL - 92
SP - 15
EP - 21
JO - Accident Analysis and Prevention
JF - Accident Analysis and Prevention
SN - 0001-4575
ER -