Citation

Chen TY, Jou RC. Transp. Res. A Policy Pract. 2019; 119: 148-161.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.tra.2018.11.005

PMID

unavailable

Abstract

Public transportation is relatively safe and secure, although less convenient than private modes of transport. However, current trends indicate that, by 2030, road traffic injuries will be the fifth leading cause of death globally. This study proposes an approach for identifying hidden contributors to traffic risk in the major metropolitan cities of Taiwan. Our purpose is to offer a comprehensive econometrical framework, using Hierarchical Linear Modelling (HLM), which highlights important contributors to traffic accident risk at different levels of injuries for public transportation. Four models underlying HLM are used to characterize the traffic accident risk. Our empirical results indicate that random intercept and random slope with interaction of HLM (model 4) is the best model. In addition, there are significant regional differences in traffic accident risk depending on the use of public and private transportation, the length of bus routes, daily average number of bus frequency per route, gender, driving habits, and behaviour.

RESULTS show that, when the length of bus routes increases by 50% in a city with well-developed infrastructure, such as Taipei, the accident risk would reduce the crash risk from 1.66 to 1.43 (decreases by 0.23), corresponding to 3450 casualties, and the total accident expense can be reduced by NT$1US$1 = NT$30.13 billion. If daily average number of bus frequency per route in Taichung increases by 50%, there are almost 3000 fewer casualties, and the accident expense decreases by NT$9.6 billion. The results of this study provide suggestions to the government that developing public transportation can effectively decrease road traffic accident risk and accident expense.

Language: en

Keywords

Hierarchical linear modeling (HLM); Public transportation; Road traffic accident risk