Vehicle trajectory modelling with consideration of distant neighbouring dependencies for destination prediction |
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| Authors: | Chengyang Qian Ruqiao Jiang Yi Long Qi Zhang Muxian Li |
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| Affiliation: | 1. Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, China;2. State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing, China;3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China;4. Suzhou Industrial Park Surveying, Mapping and Geoinformation Co., Ltd., Suzhou, China |
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| Abstract: | Vehicle trajectory modelling is an essential foundation for urban intelligent services. In this paper, a novel method, Distant Neighbouring Dependencies (DND) model, has been proposed to transform vehicle trajectories into fixed-length vectors which are then applied to predict the final destination. This paper defines the problem of neighbouring and distant dependencies for the first time, and then puts forward a way to learn and memorize these two kinds of dependencies. Next, a destination prediction model is given based on the DND model. Finally, the proposed method is tested on real taxi trajectory datasets. Results show that our method can capture neighbouring and distant dependencies, and achieves a mean error of 1.08 km, which outperforms other existing models in destination prediction significantly. |
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| Keywords: | Neighbouring and distant dependencies final destination prediction trajectory modelling Trajectory Node Vector Trajectory Sequence Vector |
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