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• W4306294805 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." @default.
• W4306294805 created "2022-10-15" @default.
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• W4306294805 date "2022-10-15" @default.
• W4306294805 modified "2023-09-30" @default.
• W4306294805 title "Using Trajectory Subclustering to Improve Destination Prediction" @default.
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• W4306294805 doi "https://doi.org/10.1155/2022/3623881" @default.
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