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首页> 外文期刊>Accident Analysis & Prevention >Developing Bicycle-Vehicle Crash-Specific Safety Performance Functions in Alabama Using Different Techniques
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Developing Bicycle-Vehicle Crash-Specific Safety Performance Functions in Alabama Using Different Techniques

机译：使用不同的技术在阿拉巴马州开发自行车车辆碰撞特异性安全性能

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This study develops bicycle-vehicle safety performance functions (SPFs) for five facilities in the Highway Safety Manual (HSM). These are urban two-lane undivided segments (U2U), urban four-lane divided/undivided segments (U4DU), rural two-lane undivided segments (R2U), urban four-leg and three-leg signalized intersections (USG), and urban four-leg and three-leg stop-controlled intersections (UST). Two modeling techniques were explored, the Conway-Maxwell-Poisson (COM-Poisson) model (to accommodate bicycle-vehicle crash under dispersion) and a machine learning technique, the multivariate adaptive regression splines (MARS). MARS is a non-black-box model and can effectively handle non-linear crash predictors and interactions. A total of 1,311 bicycle-vehicle crashes from 2011 through 2015 in Alabama were collected and their respective police reports were reviewed in details. Results from the SPFs for roadway segments using COM-Poisson showed that bicycle vehicle crash frequencies were reduced along curved and downgrade/upgrade stretches and when having heavy traffic flow (along U2U segments). For urban signalized (USG) intersections, the absence of right-turn lanes on minor roads, the presence of bus stops, and the increase in the major road annual average daily traffic (AADT) were significant factors contributing to the increase in the number of bicycle-vehicle crashes. However, the presence of divided medians on major approaches was found to reduce bicycle-vehicle crashes at USG and UST intersections. MARS outperformed the corresponding COM-Poisson models for all five facilities based on mean absolute deviance (MAD), mean square prediction error (MSPE), and generalized R-square. MARS is recommended as a promising technique for effectively predicting bicycle-vehicle crashes on segments and intersections.
机译：本研究开发了公路安全手册（HSM）中的五种设施的自行车车辆安全性能功能（SPF）。这些都是城市双车道未分割的段（U2U），城市四路分割/不分割的段（U4DU），农村双车道未分割的段（R2U），城市四腿和三腿信号交叉口（USG）和城市四腿和三腿停止控制的交叉点（UST）。探索了两种建模技术，康威 - 麦克斯韦尔 - 泊松（COM-POISSON）模型（以适应色散的自行车车辆崩溃）和机器学习技术，多变量自适应回归花键（火星）。火星是一个非黑盒式模型，可以有效地处理非线性碰撞预测器和相互作用。从2011年到2015年，在阿拉巴马州共有1,311辆自行车车辆崩溃，并详细审查了各自的警察报告。使用COM-Poisson的道路段SPF的结果显示，沿着弯曲和降级/升级的延伸和较重的交通流量（沿U2U段）减少了自行车车辆碰撞频率。对于城市信号（USG）交叉路口，在小路上没有右转车道，公交车站的存在，以及主要道路年平均日常交通（AADT）的增加是有助于增加数量的重要因素自行车车祸。然而，发现分割的中位数的主要方法是在USG和UST交叉路口减少自行车车祸。 MARS基于平均绝对偏差（MAD），均线预测误差（MSPE）和广义R-Square的所有五个设施的相应的Com-Poisson模型建议火星作为有效的技术，可有效地预测自行车车辆崩溃在段和交叉口。

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