Abstract
Destination prediction is an important issue for location based services (LBSs) and intelligent urban traffic planning. Existing destination methods either ignore spatial correlations or temporal features of the trajectories. We propose four spatial ontologies and four relations to construct the trajectory knowledge graph, such that the spatial explicit and implicit features are captured. Then, we propose a new destination prediction method TOP w.r.t the trajectory knowledge graph and the embedded trajectories. Specifically, the relational graph convolutional network is used on the trajectory knowledge graph to obtain the embedding representation of the spatial entities; trajectories are transformed into embedding trajectories utilizing the embedding representation of the spatial entities; GRU is employed to learn the temporal features on the embedded trajectories; finally, external metadata, such as workdays, holidays, and departure time are fused together to predict the destination. We also conduct a series of experiments on a real dataset. The experimental results proved the effectiveness of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
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)
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)
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)
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)
Liu, S., Ni, L.M., Krishnan, R.: Fraud detection from taxis’ driving behaviors. IEEE Trans. Veh. Technol. 63(1), 464–472 (2014)
Besse, P.C., Guillouet, B., Loubes, J.M.: Destination prediction by trajectory distribution-based model. IEEE Trans. Intell. Transp. Syst. 19(8), 2470–2481 (2018)
Can, Y., Győző, G.: Fast map matching, an algorithm integrating hidden Markov model with precomputation. Int. J. Geogr. Inf. Sci. 32(3), 547–570 (2018)
Xue, A.Y., Zhang, R., Zheng, Y., Xie, X.: Destination prediction by sub-trajectory synthesis and privacy protection against such prediction. ICDE, pp. 254–265 (2012)
Li, X., Li, M., Gong, Y.J.: T-DesP: destination pre-diction based on big trajectory data. IEEE Trans. Intell. Transp. Syst. 17(8), 2344–2354 (2016)
Mathew, W., Raposo, R., Martins, B.: Predicting future locations with hidden Markov models. In: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, pp. 911–918 (2012)
Alvarez-Garcia, J.A., et al.: Trip destination prediction based on past GPS log using a Hidden Markov Model. Expert Syst. Appl. 37(12), 8166–8171 (2010)
Ebel, P., IE Göl., Lingenfelder, C.: Destination prediction based on partial trajectory data. IEEE Intelligent Vehicles Symposium, pp.1149−1155 (2020)
Liao, C., Chen, C., Xiang, C.: Taxi-passenger’s destination prediction via GPS embedding and attention-based BiLSTM Model. IEEE Trans. Intell. Transp. Syst. 1, 1–14 (2021)
Yuki, E., Hiroyuki, T., Kyosuke, N., Akihisa K.: Deep feature extraction from trajectories for transportation mode estimation. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 54–66 (2016)
Zhang, X., Zhao, Z., Zheng, Y.: Prediction of taxi destinations using a novel data embedding method and ensemble learning. IEEE Trans. Intell. Transp. Syst. 21(1), 68–78 (2019)
Yang, Z., et al.: An efficient destination prediction approach based on future trajectory prediction and transition matrix optimization. IEEE Trans. Knowl. Data Eng. 32(2), 203−217 (2020)
Ryo, I., Kota, T., Tatsuya, K., Masamichi, S.: Early destination prediction with spatio-temporal user behavior patterns. Proc. ACM Interact. Mob. Wearable Ubiquit. Technol. 1(4), 1–19 (2018)
Schlichtkrull, M., Kipf, T.N., Bloem, P.: Modeling relational data with graph convolutional networks, In: 15th ESWC (2018)
Cho, K., Van Merrienboer, B., Bahdanau, D.: On the properties of neural machine translation: Encoder-decoder approaches. arXiv:1409.1259. https://arxiv.org/abs/1409.1259 (2014)
Rossi, A., Barlacchi, G., Bianchini, M., Lepri, B.: Modelling taxi drivers’ behaviour for the next destination prediction. IEEE Trans. Intel. Transp.Syst. 21(7), 2980–2989 (2019)
Endo, Y., Toda, H., Nishida, K.: Classifying spatial trajectories using representation learning. Int. J. Data Sci. Analytics. 2(3–4), 107–117 (2016). https://doi.org/10.1007/s41060-016-0014-1
Zhao, J., et al.: On prediction of user destination by sub-trajectory understanding: a deep learning based approach. In: ACM International Conference on Information and Knowledge Management, pp. 1413–1422 (2018)
Xu, J., et al.: Predicting destinations by a deep learning based approach. IEEE Trans. Knowl. Data Eng. 33(2), 651−661 (2019)
Paul, N., John, K.: Hidden Markov map matching through noise and sparseness. In: ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 336–343 (2009)
https://sigspatial2021.sigspatial.org/, https://sigspatial2021.sigspatial.org/sigspatial-cup/. Accessed 11 Nov 2022
Acknowledgements
This work was partially supported by the grant from the Natural Science Foundation of Hebei Province (F2021210005), the Outstanding Youth Foundation of Hebei Education Department (BJ2021085).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Pan, X., Jiang, M., Wang, S., Li, N., Sun, J., Wang, Z. (2024). TOP: Taxi Destination Prediction Based on Trajectory Knowledge Graph. In: Zhang, W., Tung, A., Zheng, Z., Yang, Z., Wang, X., Guo, H. (eds) Web and Big Data. APWeb-WAIM 2024. Lecture Notes in Computer Science, vol 14962. Springer, Singapore. https://doi.org/10.1007/978-981-97-7235-3_21
Download citation
DOI: https://doi.org/10.1007/978-981-97-7235-3_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-7234-6
Online ISBN: 978-981-97-7235-3
eBook Packages: Computer ScienceComputer Science (R0)