Given a road network and a set of trajectory data, the anomalous behavior detection (ABD) problem is to identify drivers that show significant directional deviations, hardbrakings, and accelerations in their trips. The ABD problem is important in many societal applications, including Mild Cognitive Impairment (MCI) detection and safe route recommendations for older drivers. The ABD problem is computationally challenging due to the large size of temporally-detailed trajectories dataset. In this paper, we propose an Edge-Attributed Matrix that can represent the key properties of temporally-detailed trajectory datasets and identify abnormal driving behaviors. Experiments using real-world datasets demonstrated that our approach identifies abnormal driving behaviors.

翻译：给定道路网络和一组轨迹数据，异常行为检测（ABD）问题旨在识别在行驶过程中表现出显著方向偏移、急刹车和急加速的驾驶员。ABD问题在诸多社会应用中具有重要意义，包括轻度认知障碍（MCI）检测以及为老年驾驶员提供安全路线推荐。由于时间精细化轨迹数据集规模庞大，ABD问题在计算上具有挑战性。本文提出了一种边缘属性矩阵（Edge-Attributed Matrix），该矩阵能够表示时间精细化轨迹数据集的关键特性，并识别异常驾驶行为。基于真实数据集的实验结果表明，我们的方法能够有效识别异常驾驶行为。