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COMPARATIVE ANALYSIS OF FACTORS AFFECTING TRANSIT BUS CRASHES TO GENERAL TRAFFIC CRASHES

机译:影响公交客车事故与一般交通事故影响因素的比较分析

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This paper evaluates how different roadway cross-sectional elements and traffic characteristicsimpacts crashes involving transit buses and general traffic crashes. The contributing level of eachof the elements is compared between the two crash scenarios. One of the elements included inthe study is the impact of bus trips per day which haven’t been covered by many previousstudies. Zero Inflation Negative Binomial (ZINB) and the standard Negative Binomial (NB)models were applied in the analysis. The ZINB method was applied to the transit bus crashesmodeling due to the presence of many roadway segments with zero crash experiences. Asexpected, the model results presented a positive coefficient for the number of bus trips whichmeant that the higher the number of trips per day, the more crashes for that specific roadwaysegment. Other positive coefficient variables in the bus crashes model include the presence ofraised medians, presence of two way left turn lanes (TWLTL), and high posted speed limits.Negative coefficient variables were found to be number of lanes, median width, lane width andshoulder width and the presence of curb and gutter. While most of the variable coefficient signsin ZINB were the same as those in standard NB, their magnitudes and significances varied acrossthese two models. Study findings present safety factors to consider when assigning transit busroutes and scheduling on top of other general traffic safety measures.
机译:本文评估了不同的道路横截面元素和交通特征 影响涉及公交车的撞车事故和一般交通撞车事故。每个人的贡献水平 在两种崩溃情况之间比较元素的数量。包含的元素之一 该研究是每天公交出行的影响,以前很多人都没有涉及 学习。零通胀负二项式(ZINB)和标准负二项式(NB) 模型被应用在分析中。 ZINB方法应用于公交车撞车事故 由于存在许多具有零碰撞体验的道路段,因此可以进行建模。作为 预期的是,模型结果为公交车出行次数提供了正系数,其中 意味着每天的出行次数越多,该特定行车道的撞车事件就越多 部分。公交车碰撞模型中的其他正系数变量包括 中位数升高,存在两条左转弯车道(TWLTL)以及过高的限速限制。 发现负系数变量是车道数量,中位数宽度,车道宽度和 肩宽以及路缘和天沟的存在。虽然大多数可变系数符号 ZINB中的内容与标准NB中的内容相同,其大小和意义在 这两个模型。研究发现提出了分配公交车时要考虑的安全因素 在其他一般交通安全措施的基础上进行路线和调度。

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