Queensland University of Technology (QUT), Centre for Accident Research and Road Safety - Queensland (CARRS-Q), 130 Victoria Park Road, Kelvin Grove, QLD 4059, Australia; Politeknik Sultan Mizan Zainal Abidin, Jln Paka, 23000 Dungun, Terengganu, Malaysia.
Queensland University of Technology (QUT), Centre for Accident Research and Road Safety - Queensland (CARRS-Q), 130 Victoria Park Road, Kelvin Grove, QLD 4059, Australia; Queensland University of Technology (QUT), Civil Engineering and Built Environment, Science and Engineering Faculty, 2 George St., S Block, Room 701, Brisbane, QLD 4000, Australia.
Accid Anal Prev. 2018 Oct;119:80-90. doi: 10.1016/j.aap.2018.07.006. Epub 2018 Jul 11.
Road safety in rural mountainous areas is a major concern as mountainous highways represent a complex road traffic environment due to complex topology and extreme weather conditions and are associated with more severe crashes compared to crashes along roads in flatter areas. The use of crash modelling to identify crash contributing factors along rural mountainous highways suffers from limitations in data availability, particularly in developing countries like Malaysia, and related challenges due to the presence of excess zero observations. To address these challenges, the objective of this study was to develop a safety performance function for multi-vehicle crashes along rural mountainous highways in Malaysia. To overcome the data limitations, an in-depth field survey, in addition to utilization of secondary data sources, was carried out to collect relevant information including roadway geometric factors, traffic characteristics, real-time weather conditions, cross-sectional elements, roadside features, and spatial characteristics. To address heterogeneity resulting from excess zeros, three specialized modelling techniques for excess zeros including Random Parameters Negative Binomial (RPNB), Random Parameters Negative Binomial - Lindley (RPNB-L) and Random Parameters Negative Binomial - Generalized Exponential (RPNB-GE) were employed. Results showed that the RPNB-L model outperformed the other two models in terms of prediction ability and model fit. It was found that heavy rainfall at the time of crash and the presence of minor junctions along mountainous highways increase the likelihood of multi-vehicle crashes, while the presence of horizontal curves along a steep gradient, the presence of a passing lane and presence of road delineation decrease the likelihood of multi-vehicle crashes. Findings of this study have significant implications for road safety along rural mountainous highways, particularly in the context of developing countries.
农村山区道路安全是一个主要关注点，因为山区公路由于复杂的拓扑结构和极端的天气条件，代表了一个复杂的道路交通环境，与在平坦地区的道路相比，与更严重的碰撞事故相关。使用碰撞建模来确定农村山区公路沿线的碰撞因素存在数据可用性方面的限制，特别是在马来西亚等发展中国家，并且由于存在过多的零观测值，还存在相关挑战。为了解决这些挑战，本研究的目的是为马来西亚农村山区公路的多车辆碰撞开发安全性能函数。为了克服数据限制，除了利用二手数据源外，还进行了深入的现场调查，以收集包括道路几何因素、交通特征、实时天气条件、横断面元素、路边特征和空间特征在内的相关信息。为了解决由于过多零值导致的异质性，采用了三种专门针对过多零值的建模技术，包括随机参数负二项式（RPNB）、随机参数负二项式-林德利（RPNB-L）和随机参数负二项式-广义指数（RPNB-GE）。结果表明，RPNB-L 模型在预测能力和模型拟合方面优于其他两种模型。研究发现，碰撞时的强降雨和山区公路上存在次要交叉口会增加多车辆碰撞的可能性，而陡峭坡度上存在水平曲线、存在超车车道和存在道路标线会降低多车辆碰撞的可能性。本研究的结果对农村山区公路的道路安全具有重要意义，特别是在发展中国家的背景下。
