Highlights Separate crash prediction models are developed with respect to collision type, e.g., single versus multiple-vehicle crashes and crash severity. A disaggregated crash database that include the information on weather condition, geometric design, traffic control, and temporal variation of 112 roadway segments in Hong Kong during the period from 2002 to 2006 was established. A bootstrap resampling method is used to resolve the problem of excessive zero crash counts. Roles of exposure and other possible risk factors on crash risk are investigated.
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.
Bootstrap resampling approach to disaggregate analysis of road crashes in Hong Kong
Accident Analysis and Prevention ; 95 ; 512-520
2015-06-06
9 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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