TOP: Taxi Destination Prediction Based on Trajectory Knowledge Graph
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-7235-3_21
Reference26 articles.
1. Dan, P., Huang, M., Xing, Z.: Taxi origin and destination demand prediction based on deep learning: a review. Digital Trans. Saf. 2(3), 176–189 (2023)
2. Tang, J., Liang, J., Yu, T.: Trip destination prediction based on a deep integration network by fusing multiple features from taxi trajectories. IET Intel.Transp. Syst. 15, 1131–1141 (2021)
3. Liao, C., Chen, C., Xiang, C.: Taxi-passenger’s destination prediction via GPS embedding and attention-based BILSTM model. IEEE Trans. Intel. Trans. Syst. 23(5), 4460–4473 (2021)
4. Lv, J., Sun, Q., Li, Q.: Multi-scale and multi-scope convolutional neural networks for destination prediction of trajectories. IEEE Trans. Intell. Transp. Syst. 21(8), 3184–3195 (2020)
5. Zheng, J., Ni, L M.: Modeling heterogeneous routing decisions in trajectories for driving experience learning. In: Proceedings of the ACM Interactive Mobile Wearable Ubiquitous Technol, pp. 951–961 (2014)
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