Vehicle telematics provides granular data for dynamic driving risk assessment, but current methods often rely on aggregated metrics (e.g., harsh braking counts) and do not fully exploit the rich time-series structure of telematics data. In this paper, we introduce a flexible framework using continuous-time hidden Markov model (CTHMM) to model and analyze trip-level telematics data. Unlike existing methods, the CTHMM models raw time-series data without predefined thresholds on harsh driving events or assumptions about accident probabilities. Moreover, our analysis is based solely on telematics data, requiring no traditional covariates such as driver or vehicle characteristics. Through unsupervised anomaly detection based on pseudo-residuals, we identify deviations from normal driving patterns -- defined as the prevalent behaviour observed in a driver's history or across the population -- which are linked to accident risk. Validated on both controlled and real-world datasets, the CTHMM effectively detects abnormal driving behaviour and trips with increased accident likelihood. In real data analysis, higher anomaly levels in longitudinal and lateral accelerations consistently correlate with greater accident risk, with classification models using this information achieving ROC-AUC values as high as 0.86 for trip-level analysis and 0.78 for distinguishing drivers with claims. Furthermore, the methodology reveals significant behavioural differences between drivers with and without claims, offering valuable insights for insurance applications, accident analysis, and prevention.

翻译：车辆遥测技术为动态驾驶风险评估提供了细粒度数据，但现有方法通常依赖于聚合指标（如急刹车次数），未能充分利用遥测数据丰富的时间序列结构。本文提出一种使用连续时间隐马尔可夫模型（CTHMM）的灵活框架，用于建模和分析行程级遥测数据。与现有方法不同，CTHMM直接对原始时间序列数据进行建模，无需预先设定急驾驶事件的阈值或假设事故概率。此外，我们的分析完全基于遥测数据，无需传统协变量（如驾驶员或车辆特征）。通过基于伪残差的无监督异常检测，我们识别出与正常驾驶模式（定义为驾驶员历史或群体中普遍观察到的行为）的偏离，这些偏离与事故风险相关。在受控数据集和真实数据集上的验证表明，CTHMM能有效检测异常驾驶行为和事故概率升高的行程。在真实数据分析中，纵向和横向加速度的较高异常水平始终与更大的事故风险相关，利用该信息构建的分类模型在行程级分析中ROC-AUC值高达0.86，在区分有索赔记录的驾驶员时达到0.78。此外，该方法揭示了有索赔与无索赔驾驶员之间的显著行为差异，为保险应用、事故分析和预防提供了重要见解。