No abstract

Destination prediction is an active area of research, especially in the context of intelligent transportation systems. Intelligent applications, such as battery management in electric vehicles and congestion avoidance, rely on the accurate prediction of the future destinations of a vehicle. Destination prediction methods can utilise mobility patterns and can harness the latent information within vehicle trajectories. Existing approaches make use of the spatial information contained within trajectories, but this can be insufficient to achieve an accurate prediction at the start of an unfolding trajectory, since several destinations may share a common start to their trajectories. To reduce the prediction error in the early stages of a journey, we propose the Destination Prediction by Trajectory Subclustering (DPTS) method for iteratively clustering similar trajectories into groups using additional information contained within trajectories, such as temporal data. We show in our evaluation that DPTS is able to reduce the mean distance error in the first 40–60% of journeys. The implication of reducing the distance error early in a journey is that location-aware applications could provide more accurate functionality earlier in a journey. In this article, we (i) propose the Destination Prediction by Trajectory Subclustering (DPTS) method by extending an existing destination prediction method through incorporating an iterative clustering stage to decompose groups of similar trajectories into smaller groups and (ii) evaluate DPTS against the baseline performance of the existing method.

Knowledge of drivers’ mobility patterns is useful for enabling context-aware intelligent vehicle functionality, such as route suggestions, cabin preconditioning, and power management for electric vehicles. Such patterns are often described in terms of the Points of Interest (PoIs) visited by an individual. However, existing PoI extraction methods are general purpose and typically rely on detecting periods of low mobility, meaning that when they are applied to vehicle data, they often extract a large number of false PoIs (for example, incorrectly extracting PoIs due to stopping in traffic), reducing their usefulness. To reduce the number of false PoIs that are extracted, we propose using features derived from vehicle signals, such as the selected gear and status of doors, to classify candidate PoIs and filter out those that are irrelevant. In this paper, we (i) present Activity-based Vehicle PoI Extraction (AVPE), a wrapper method around existing PoI extraction methods, that utilizes a postclustering classification stage to filter out false PoIs, (ii) evaluate the benefits of AVPE compared to three state-of-the-art general purpose PoI extraction algorithms, and (iii) demonstrate the effectiveness of AVPE when applied to real-world driving data.