Drivers who perform frequent high-risk events (e.g., hard braking maneuvers) pose a significant threat to traffic safety. Existing studies commonly estimated high-risk event occurrence probabilities based upon the assumption that data collected from different time periods are independent and identically distributed (referred to as i.i.d. assumption). Such approach ignored the issue of driving behavior temporal covariate shift, where the distributions of driving behavior factors vary over time. To fill the gap, this study targets at obtaining time-invariant driving behavior features and establishing their relationships with high-risk event occurrence probability. Specifically, a generalized modeling framework consisting of distribution characterization (DC) and distribution matching (DM) modules was proposed. The DC module split the whole dataset into several segments with the largest distribution gaps, while the DM module identified time-invariant driving behavior features through learning common knowledge among different segments. Then, gated recurrent unit (GRU) was employed to conduct time-invariant driving behavior feature mining for high-risk event occurrence probability estimation. Moreover, modified loss functions were introduced for imbalanced data learning caused by the rarity of high-risk events. The empirical analyses were conducted utilizing online ride-hailing services data. Experiment results showed that the proposed generalized modeling framework provided a 7.2% higher average precision compared to the traditional i.i.d. assumption based approach. The modified loss functions further improved the model performance by 3.8%. Finally, benefits for the driver management program improvement have been explored by a case study, demonstrating a 33.34% enhancement in the identification precision of high-risk event prone drivers.

中文翻译：

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考虑驾驶行为时间协变量偏移的高风险事件易发驾驶员识别

频繁执行高风险事件（例如紧急制动操作）的驾驶员对交通安全构成重大威胁。现有研究普遍基于不同时间段收集的数据独立同分布的假设（称为独立同分布假设）来估计高风险事件发生概率。这种方法忽略了驾驶行为时间协变量偏移的问题，其中驾驶行为因素的分布随时间变化。为了填补这一空白，本研究的目标是获得时不变的驾驶行为特征，并建立它们与高风险事件发生概率的关系。具体来说，提出了由分布表征（DC）和分布匹配（DM）模块组成的广义建模框架。DC模块将整个数据集分成分布差距最大的几个片段，而DM模块通过学习不同片段之间的共同知识来识别时不变的驾驶行为特征。然后，采用门控循环单元（GRU）进行时不变驾驶行为特征挖掘，以进行高风险事件发生概率估计。此外，针对高风险事件的稀有性导致的数据学习不平衡，引入了修改后的损失函数。利用在线叫车服务数据进行实证分析。实验结果表明，与传统的基于独立同分布假设的方法相比，所提出的广义建模框架的平均精度提高了 7.2%。修改后的损失函数进一步将模型性能提高了 3.8%。最后，通过案例研究探讨了驾驶员管理程序改进的好处，表明高风险事件易发驾驶员的识别精度提高了 33.34%。