A classification tree based modeling approach for segment related crashes on multilane highways
作者： Anurag Pande , Mohamed Abdel-Aty , Abhishek Das , None
DOI: 10.1016/J.JSR.2010.06.004
关键词: Poison control 、 Crash 、 Segmentation 、 Engineering 、 Decision tree learning 、 Geometric design 、 Transport engineering 、 Pedestrian 、 Sample (statistics) 、 Intersection (aeronautics)
摘要: Abstract Introduction This study presents a classification tree based alternative to crash frequency analysis for analyzing crashes on mid-block segments of multilane arterials. Method The traditional approach modeling counts that occur over period time works well intersection where each itself provides well-defined unit which aggregate the data. However, in case requires segmentation arterial corridor into arbitrary lengths. In this we have used random samples time, day week, and location (i.e., milepost) combinations compared them with sample from same corridor. For non-crash cases, geometric design/roadside traffic characteristics were derived their milepost locations. variables are non-event specific therefore more relevant roadway safety feature improvement programs. First model is comparing all data then four groups (rear-end, lane-change related, pedestrian, single-vehicle/off-road crashes) separately cases. models provide list significant as measure classify ADT along day/day week significantly related types different being likely at times. Conclusions From performance it was apparent using information may not be suitable single vehicle/off-road crashes. Impact Industry community an additional tool assess without having segment
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