[论文解读] Real-Time Crash Risk Analysis of Urban Arterials Incorporating Bluetooth, Weather, and Adaptive Signal Control Data

Jinghui Yuan, Mohamed Abdel‐Aty+4 more|arXiv (Cornell University)|May 20, 2018

Traffic and Road Safety参考文献 22被引用 6

一句话总结

本研究采用贝叶斯条件逻辑回归，开发了一种基于蓝牙交通数据、自适应信号控制信息和天气状况的实时城市主干道碰撞风险模型。5–10分钟的碰撞前时间间隔数据预测最为准确，平均速度、上游流量和降雨天气显著增加碰撞风险，优于其他建模方法。

ABSTRACT

Real-time safety analysis has become a hot research topic as it can reveal the relationship between real-time traffic characteristics and crash occurrence more accurately, and these results could be applied to improve active traffic management systems and enhance safety performance. Most of the previous studies have been applied to freeways and seldom to arterials. Therefore, this study attempts to examine the relationship between crash occurrence and real-time traffic and weather characteristics based on four urban arterials in Central Florida. Considering the substantial difference between the interrupted traffic flow on urban arterials and the free flow on freeways, the adaptive signal phasing was also introduced in this study. Bayesian conditional logistic models were developed by incorporating the Bluetooth, adaptive signal control, and weather data, which were extracted for a period of 20 minutes (four 5-minute interval) before the time of crash occurrence. Model comparison results indicate that the model based on 5-10 minute interval dataset is the most appropriate model. It reveals that the average speed, upstream volume, and rainy weather indicator were found to have significant effects on crash occurrence. Furthermore, both Bayesian logistic and Bayesian random effects logistic models were developed to compare with the Bayesian conditional logistic model, and the Bayesian conditional logistic model was found to be much better than the other two models. These results are important in real-time safety applications in the context of Integrated Active Traffic Management.

研究动机与目标

• 开发一种用于城市主干道的实时碰撞风险分析模型，此类道路在主动安全研究中相较于高速公路仍属代表性不足。
• 将异构的实时数据源——蓝牙交通流、自适应信号控制和天气状况——整合到统一的安全建模框架中。
• 评估不同贝叶斯建模方法（条件、逻辑、随机效应）在碰撞风险预测中的性能表现。
• 识别预碰撞数据提取的最佳时间窗口，以优化模型的准确性和响应速度。

提出的方法

• 收集佛罗里达州中部四条城市主干道上蓝牙检测器在碰撞事件前20分钟内的实时交通数据。
• 引入自适应信号控制数据，以反映信号交叉口的动态特性，体现与高速公路条件不同的间歇性交通流。
• 采用贝叶斯条件逻辑回归模型来建模碰撞风险，同时考虑病例对照匹配及路段内相关性。
• 提取并处理天气指标，包括一个二元降雨天气标志，以评估气象条件对碰撞可能性的影响。
• 使用模型比较指标评估模型性能，并选定5–10分钟的碰撞前时间间隔为最优。
• 将贝叶斯条件逻辑模型与标准贝叶斯逻辑模型及贝叶斯随机效应逻辑模型进行对比，评估其预测准确性。

实验结果

研究问题

• RQ1 在城市主干道上，为最大化碰撞风险预测准确性，预碰撞数据采集的最佳时间窗口是什么？
• RQ2 实时交通变量（如平均速度和上游流量）如何影响信号控制城市道路的碰撞发生？
• RQ3 天气状况（尤其是降雨）在多大程度上影响城市主干道的实时碰撞风险？
• RQ4 与仅包含交通数据的模型相比，引入自适应信号控制数据在碰撞风险建模中如何提升性能？
• RQ5 在贝叶斯条件逻辑模型、标准贝叶斯逻辑模型和贝叶斯随机效应逻辑模型中，哪一种模型在实时碰撞风险预测中表现最佳？

主要发现

• 5–10分钟的预碰撞数据时间窗口产生了最准确的模型，优于更短或更长的时间窗口。
• 较低的平均速度和较高的上游流量与城市主干道上碰撞风险的显著增加相关。
• 通过二元天气标志表示的降雨存在，显著提高了碰撞发生的可能性。
• 贝叶斯条件逻辑模型在性能上优于标准贝叶斯逻辑模型和贝叶斯随机效应逻辑模型。
• 引入自适应信号控制数据通过考虑城市主干道特有的间歇性交通流特征，提升了模型的真实性。
• 本研究证实，整合多源实时数据可显著增强主动交通管理系统在城市安全应用中的预测能力。