Investigation of hit-and-run crash occurrence and severity using real-time loop detector data and hierarchical Bayesian binary logit model with random effects
Investigation of hit-and-run crash occurrence and severity using real-time loop detector data and hierarchical Bayesian binary logit model with random effects
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DOI：
10.1080/15389588.2017.1371302
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发表时间：
2018-02
影响因子：
2
通讯作者：
Meiquan Xie;W. Cheng;G. Gill;Jiao Zhou;X. Jia;Simon Choi
中科院分区：
文献类型：
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作者：
Meiquan Xie;W. Cheng;G. Gill;Jiao Zhou;X. Jia;Simon Choi
摘要 目的：大多数致力于识别肇事逃逸 (HR) 事故影响因素的广泛研究都利用了典型的最大似然估计二元 Logit 模型，但没有一个使用实时交通数据。为了填补这一空白，本研究重点调查导致人力资源崩溃的因素以及人力资源的严重程度。方法：本研究通过采用在顺序 Logit 结构中具有随机效应的分层贝叶斯模型来分析 4 年碰撞和实时环路检测器数据。除了评估随机效应对模型适应性和复杂性的影响之外，还检查了模型的预测能力。计算逐步增量的敏感性和特异性，并利用受试者工作特征（ROC）曲线以图形方式说明模型的预测性能。结果：在实时流量变量中，上游检测器的平均占用率和速度与 HR 碰撞可能性呈正相关。平均上游速度和上下游速度差与严重HR碰撞事故的发生相关。除了实时因素外，其他对 HR 和严重 HR 碰撞有影响的变量还包括路段长度、恶劣天气条件、路灯故障的黑暗照明条件、酒后驾驶、内侧肩宽和夜间。结论：本研究提出了HR的潜在交通状况和严重的HR发生，这是指上游相对拥堵的交通状况，上游速度高，长段速度偏差显着。上述研究结果表明，交通执法应致力于减少上述交通条件下的危险驾驶。此外，执法机构可能会利用酒精检查站来打击夜间酒后驾驶 (DUI)。在工程改进方面，可以建造更宽的内路肩，以潜在地减少 HR 情况，并且应安装路灯并将其保持在工作状态，以使道路不易发生此类事故。
ABSTRACT Objective: Most of the extensive research dedicated to identifying the influential factors of hit-and-run (HR) crashes has utilized typical maximum likelihood estimation binary logit models, and none have employed real-time traffic data. To fill this gap, this study focused on investigating factors contributing to HR crashes, as well as the severity levels of HR. Methods: This study analyzed 4-year crash and real-time loop detector data by employing hierarchical Bayesian models with random effects within a sequential logit structure. In addition to evaluation of the impact of random effects on model fitness and complexity, the prediction capability of the models was examined. Stepwise incremental sensitivity and specificity were calculated and receiver operating characteristic (ROC) curves were utilized to graphically illustrate the predictive performance of the model. Results: Among the real-time flow variables, the average occupancy and speed from the upstream detector were observed to be positively correlated with HR crash possibility. The average upstream speed and speed difference between upstream and downstream speeds were correlated with the occurrence of severe HR crashes. In addition to real-time factors, other variables found influential for HR and severe HR crashes were length of segment, adverse weather conditions, dark lighting conditions with malfunctioning street lights, driving under the influence of alcohol, width of inner shoulder, and nighttime. Conclusions: This study suggests the potential traffic conditions of HR and severe HR occurrence, which refer to relatively congested upstream traffic conditions with high upstream speed and significant speed deviations on long segments. The above findings suggest that traffic enforcement should be directed toward mitigating risky driving under the aforementioned traffic conditions. Moreover, enforcement agencies may employ alcohol checkpoints to counter driving under the influence (DUI) at night. With regard to engineering improvements, wider inner shoulders may be constructed to potentially reduce HR cases and street lights should be installed and maintained in working condition to make roads less prone to such crashes.