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Using trajectory sub-clustering to improve destination prediction
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Van Hinsbergh, James, Griffiths, Nathan, Xu, Zhou and Mouzakitis, Alexandros (2022) Using trajectory sub-clustering to improve destination prediction. Mobile Information Systems, 2022 . 3623881. doi:10.1155/2022/3623881 ISSN 1574-017X.
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Official URL: https://doi.org/10.1155/2022/3623881
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.
| Item Type: | Journal Article | |||||||||
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| Subjects: | Q Science > QA Mathematics T Technology > TE Highway engineering. Roads and pavements T Technology > TL Motor vehicles. Aeronautics. Astronautics |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||
| Library of Congress Subject Headings (LCSH): | Intelligent transportation systems, Trajectories (Mechanics), Electric vehicles -- Batteries, Automated vehicles, Intelligent agents (Computer software) -- Mathematical models, Artificial intelligence -- Engineering applications | |||||||||
| Journal or Publication Title: | Mobile Information Systems | |||||||||
| Publisher: | Hindawi Limited | |||||||||
| ISSN: | 1574-017X | |||||||||
| Official Date: | 15 October 2022 | |||||||||
| Dates: |
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| Volume: | 2022 | |||||||||
| Number of Pages: | 21 | |||||||||
| Article Number: | 3623881 | |||||||||
| DOI: | 10.1155/2022/3623881 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||
| Date of first compliant deposit: | 27 September 2022 | |||||||||
| Date of first compliant Open Access: | 27 September 2022 | |||||||||
| RIOXX Funder/Project Grant: |
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