Department of Automation, Tsinghua University, Beijing, China.
Department of Civil and Natural Resources Engineering, University of Canterbury, Christchurch, New Zealand.
Accid Anal Prev. 2016 Oct;95(Pt B):512-520. doi: 10.1016/j.aap.2015.06.007. Epub 2015 Jul 8.
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.
道路安全影响全球健康和发展；因此，研究影响事故和伤害的因素至关重要。由于事故、事故严重程度和可能的危险因素之间的关系可能因碰撞类型而异，我们试图为不同的事故类型（即单车事故与多车事故、轻伤与死亡和重伤事故）开发单独的预测模型。利用按时间和空间细分的事故和交通数据，可以确定可能导致香港事故风险的因素，包括交通流量、道路设计和天气条件。为了消除高度细分的事故预测模型中过多零值对预测性能的影响，应用了自举重采样方法。结果表明，使用自举重采样方法可以获得更准确和可靠的参数估计值，并且标准误差更小。结果表明，降雨、几何设计、交通控制和时间变化等因素都决定了事故风险和事故严重程度。这有助于阐明补救工程和交通管理与控制措施的制定。
