摘要
在以往的道路交通事故研究中，驾驶行为常常被忽视。由此，针对涉及交通碰撞的车辆收集由车载导航系统发送的异常(极端)驾驶行为数据，包括急转弯、急加速和突然刹车行为。利用这些数据和高速公路撞车记录，训练多个分类学习者建立行为驱动的风险预测模型。为了进一步研究驾驶行为对碰撞风险的影响，应用了部分依赖图(PDPs)。回归分析表明，当包含特定偏差行为频率、行为过程中的速度和加速度等导数特征时，模型的效果更强。行为RUSBoost模型优于其他模型，实现了0.782的AUC预测指标，并优于传统的流量驱动的机器学习模型。PDP分析表明，在加速度超过0.5g的情况下，突发制动行为是导致高速公路碰撞的主要因素。本研究证实了通过增加导航软件的行为数据来预测碰撞风险的潜力，研究结果为对策奠定了基础。
Abstract
Driving behavior has frequently been overlooked in previous road traffic crash research. Hereby, abnormal (extreme) driving behavior data transmitted by the onboard navigation systems were collected for vehicles involved in traffic crashes, including sharp-lane-change, sharp-acceleration, and sudden-braking behaviors. Using these data in conjunction with expressway crash records, multiple classification learners were trained to establish a behavior-driven risk prediction model. To further investigate the influence of driving behavior on crash risk, partial dependence plots (PDPs) were applied. Regression analyses indicate that models have a stronger effect when derivative features such as frequency of specific deviant behavior, speed, and acceleration in the behavior process are included. The behavioral RUSBoost model surpasses other models, achieving an AUC prediction metric of 0.782 and outperforming traditional traffic-flow-driven machine learning models. PDP analysis demonstrates that the sudden-braking behavior is the leading contributory factor of expressway crashes, particularly when the acceleration exceeds 0.5 G. This study confirms the potential of predicting crash risks through augmenting behavior data from navigation software; the findings lay a foundation for countermeasures.
NETL
NSTL
Wiley-Hindawi