A Crash Counts by Severity Based Hotspot Identification Method and Its Application on a Regional Map Based Analytical Platform
This paper aims to develop a crash counts by severity based hotspot identification method by extending the traditional empirical Bayes method to a generalized nonlinear model-based mixed multinomial logit approach. A new safety performance index and a new potential safety improvement index are developed by introducing the risk weight factor and compared with traditional indexes by employing four hotspot identification evaluating methods. The comparison results reveal that the new safety performance index derived by the generalized nonlinear model-based mixed multinomial logit approach is the most consistent and reliable method for identifying hotspots. Finally, a regional map based analytical platform is developed by expanding the safety performance module with the new safety performance index and potential safety improvement functions.
KeywordsHotspot identification Crash severity Safety performance index Potential safety improvement index Regional map based analytical platform
This research was supported by the Youth Program of National Natural Science Foundation of China (Grant No. 71501137), the General Programs of China Postdoctoral Science Foundation (Grant No. 2015M572480), the International Postdoctoral Exchange Fellowship Program of China Postdoctoral Council (Grant No. 20150028), the project of Research Center for System Sciences and Enterprise Development (Grant No. Xq16B05), and also funded by Sichuan University (Grant No. skqy201647). The authors would like to give our great appreciation to the editors and anonymous referees for their helpful and constructive comments and suggestions, which have helped to improve this article.
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