Road network safety evaluation using Bayesian hierarchical joint model |
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| Affiliation: | 1. Department of Civil Engineering, University of Costa Rica, Costa Rica;2. Department of Transportation and Logistics Management, National Chiao Tung University, Taiwan, ROC;3. Department of Civil and Environmental Engineering, The Pennsylvania State University, United States;1. School of Transportation Engineering, Tongji University, Shanghai 201804, China;2. The Key Laboratory of Road and Traffic Engineering, Ministry of Education, China;3. Department of Civil and Environmental Engineering, University of Windsor, Windsor, Ontario N9B 3P4, Canada;1. Postdoctoral Fellow, Center for Transportation Research (CTR), University of Texas at Austin, United States;2. Beaman Professor, Department of Civil & Environmental Engineering, The University of Tennessee, United States;3. Graduate Research Assistant, Department of Civil & Environmental Engineering, The University of Tennessee, United States;1. School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan, China;2. Transport Planning and Research Institute, Ministry of Transport, Beijing, China |
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| Abstract: | Safety and efficiency are commonly regarded as two significant performance indicators of transportation systems. In practice, road network planning has focused on road capacity and transport efficiency whereas the safety level of a road network has received little attention in the planning stage. This study develops a Bayesian hierarchical joint model for road network safety evaluation to help planners take traffic safety into account when planning a road network. The proposed model establishes relationships between road network risk and micro-level variables related to road entities and traffic volume, as well as socioeconomic, trip generation and network density variables at macro level which are generally used for long term transportation plans. In addition, network spatial correlation between intersections and their connected road segments is also considered in the model.A road network is elaborately selected in order to compare the proposed hierarchical joint model with a previous joint model and a negative binomial model. According to the results of the model comparison, the hierarchical joint model outperforms the joint model and negative binomial model in terms of the goodness-of-fit and predictive performance, which indicates the reasonableness of considering the hierarchical data structure in crash prediction and analysis. Moreover, both random effects at the TAZ level and the spatial correlation between intersections and their adjacent segments are found to be significant, supporting the employment of the hierarchical joint model as an alternative in road-network-level safety modeling as well. |
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| Keywords: | Safety evaluation Road network crash prediction Bayesian hierarchical joint model Micro-level variables Macro-level variables |
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