Vehicle telematics provides detailed observations of driving behaviour that can be useful for automobile insurance risk assessment. Existing approaches often summarize these data through counts of harsh events or individually unusual observations, although driving movements naturally develop over time. Building on a previously proposed hidden Markov model (HMM) framework that identifies pointwise anomalies, we identify short intervals in which a sequence of observations is unusual when considered together. We then examine the original acceleration signals to measure the magnitude and temporal concentration of these episodes and identify recurring patterns of vehicle movement. Temporal concentration describes whether acceleration activity is distributed evenly across an episode or concentrated in relatively few observations. Using a controlled telematics dataset containing normal, aggressive, and drowsy driving by the same drivers, we evaluate the proposed measures through comparisons matched by driver and road type. We find that statistical anomaly measures depend on the driving population used to train the HMM. More importantly, mean temporal concentration alone distinguishes normal from abnormal driving more effectively than the original pointwise anomaly indices across different reference populations. The recurring movement patterns further reveal different characteristics of abnormal driving, with stronger longitudinal acceleration during aggressive driving and more frequent lateral corrections or excursions during drowsy driving. The framework therefore goes beyond pointwise anomaly detection, providing interpretable behavioural features that can be calculated for unlabeled trips, used for trip-level assessment, aggregated across a driver's trip history, and subsequently evaluated alongside existing telematics and rating variables for insurance risk modelling and ratemaking.

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