title: Bootstrap resampling approach to disaggregate analysis of road crashes in Hong Kong.
abstract: Road safety affects health and development worldwide; thus, it is essential to examine the factors that influence crashes and injuries. As the relationships between crashes, crash severity, and possible risk factors can vary depending on the type of collision, we attempt to develop separate prediction models for different crash types (i.e., single- versus multi-vehicle crashes and slight injury versus killed and serious injury crashes). Taking advantage of the availability of crash and traffic data disaggregated by time and space, it is possible to identify the factors that may contribute to crash risks in Hong Kong, including traffic flow, road design, and weather conditions. To remove the effects of excess zeros on prediction performance in a highly disaggregated crash prediction model, a bootstrap resampling method is applied. The results indicate that more accurate and reliable parameter estimates, with reduced standard errors, can be obtained with the use of a bootstrap resampling method. Results revealed that factors including rainfall, geometric design, traffic control, and temporal variations all determined the crash risk and crash severity. This helps to shed light on the development of remedial engineering and traffic management and control measures.
authors.affiliation: Department of Automation, Tsinghua University, Beijing, China. Electronic address: peixin@mail.tsinghua.edu.cn.
authors.forename: Xin
authors.initials: X
authors.lastname: Pei
authors.affiliation: Department of Civil and Natural Resources Engineering, University of Canterbury, Christchurch, New Zealand. Electronic address: tony.sze@canterbury.ac.nz.
authors.forename: N N
authors.initials: NN
authors.lastname: Sze
authors.affiliation: Department of Civil Engineering, The University of Hong Kong, Hong Kong, China. Electronic address: hhecwsc@hku.hk.
authors.forename: S C
authors.initials: SC
authors.lastname: Wong
authors.affiliation: Department of Automation, Tsinghua University, Beijing, China. Electronic address: yaody@mail.tsinghua.edu.cn.
authors.forename: Danya
authors.initials: D
authors.lastname: Yao
mesh_terms: D000063:Accidents, Traffic; D001334:Automobile Driving; D015982:Bias; D004738:Engineering; D004779:Environment Design; D006723:Hong Kong; D006801:Humans; D015233:Models, Statistical; D018570:Risk Assessment; D012307:Risk Factors; D012449:Safety; D014887:Weather; D014947:Wounds and Injuries
keywords: Bootstrap resampling method; Count data model; Injury severity; Multiple-vehicle crash; Single-vehicle crash