Abstract
The issue of final destination prediction has recently attracted extensive attentions from academia and industry. However, the road segments in the traffic network are affected by the surrounding points of interest(POI) and functional regions, resulting in the low accuracy of destination prediction. Furthermore, the sparsity of the trajectory data also affects the accuracy. Therefore, it is a big challenge to predict the destination more accurately. In order to address this issue, we propose a traffic knowledge graph based destination prediction model. In this model, a three-layer knowledge graph consisting of road network layer, trajectory layer and function layer is constructed to model the spatial correlations of road segments, POI and functional regions. Then, we exploit relative spatial information by the Traffic Graph-bert algorithm which learns the features of nodes in the subgraph and the whole graph through Transformer. Finally, the model applies self-attention mechanism to incorporating the time information and functional region of starting point to improve the prediction accuracy. The extensive data-driven experiments based on the Didi dataset are conducted to prove the effectiveness of the proposed model.
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
References
Xue, A.Y., Zhang, R., Zheng, Y., et al.: Destination prediction by sub-trajectory synthesis and privacy protection against such prediction. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE), pp. 254–265. IEEE (2013)
Zhang, X., Zhao, Z., Zheng, Y., et al.: Prediction of taxi destinations using a novel data embedding method and ensemble learning. IEEE Trans. Intell. Transp. Syst. 21(1), 68–78 (2019)
Wang, L., Wang, M., Ku, T., et al.: A hybrid model towards moving route prediction under data sparsity. In: 2017 20th International Conference on Information Fusion (Fusion), pp. 1–8. IEEE (2017)
Endo, Y., Nishida, K., Toda, H., Sawada, H.: Predicting destinations from partial trajectories using recurrent neural network. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10234, pp. 160–172. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57454-7_13
Xu, M., Wang, D., Li, J.: DESTPRE: a data-driven approach to destination prediction for taxi rides. In: Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing, vol. 2016, pp. 729–739 (2016)
Xue, A.Y., Qi, J., Xie, X., et al.: Solving the data sparsity problem in destination prediction. VLDB J. 24(2), 219–243 (2015)
Yang, D., Fankhauser, B., Rosso, P., et al.: Location prediction over sparse user mobility traces using RNNs: flashback in hidden states! In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 2184–2190 (2020)
Zhao, K., Zhang, Y., Yin, H., et al.: Discovering subsequence patterns for next POI recommendation. In: IJCAI, pp. 3216–3222 (2020)
Wang, Q., Yin, H., Chen, T., et al.: Next point-of-interest recommendation on resource-constrained mobile devices. In: Proceedings of the Web Conference 2020, pp. 906–916 (2020)
Sun, K., Qian, T., Chen, T., et al.: Where to go next: modeling long-and short-term user preferences for point-of-interest recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 01, pp. 214–221 (2020)
Liu, Q., Wu, S., Wang, L., et al.: Predicting the next location: a recurrent model with spatial and temporal contexts. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)
Rossi, A., Barlacchi, G., Bianchini, M., et al.: Modelling taxi drivers’ behaviour for the next destination prediction. IEEE Trans. Intell. Transp. Syst. 21(7), 2980–2989 (2019)
Xie, M., Yin, H., Wang, H., et al.: Learning graph-based poi embedding for location-based recommendation. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 15–24 (2016)
Christoforidis, G., Kefalas, P., Papadopoulos, A., et al.: Recommendation of points-of-interest using graph embeddings. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 31–40. IEEE (2018)
Zhang, L., Sun, Z., Zhang, J., et al.: Modeling hierarchical category transition for next POI recommendation with uncertain check-ins. Inf. Sci. 515, 169–190 (2020)
Liu, B., Qian, T., Liu, B., et al.: Learning spatiotemporal-aware representation for POI recommendation. arXiv preprint arXiv:1704.08853 (2017)
Zhang, L., Zhang, G., Liang, Z., et al.: Multi-features taxi destination prediction with frequency domain processing. PLoS ONE 13(3), e0194629 (2018)
Tang, J., Zhang, J., Yao, L., et al.: ArnetMiner: extraction and mining of academic social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 990–998 (2008)
Zhang, J., Zhang, H., Xia, C., et al.: Graph-BERT: only attention is needed for learning graph representations. arXiv preprint arXiv:2001.05140 (2020)
Page, L., Brin, S., Motwani, R., et al.: The PageRank citation ranking: bringing order to the web. Stanford InfoLab (1999)
Shervashidze, N., Schweitzer, P., Van Leeuwen, E.J., et al.: Weisfeiler-Lehman graph kernels. J. Mach. Learn. Res. 12(9), 2539–2561 (2011)
Ebel, P., Göl, I.E., Lingenfelder, C., et al.: Destination prediction based on partial trajectory data. In: 2020 IEEE Intelligent Vehicles Symposium (IV), pp. 1149–1155. IEEE (2020)
De Brébisson, A., Simon, É., Auvolat, A., et al.: Artificial neural networks applied to taxi destination prediction. arXiv preprint arXiv:1508.00021 (2015)
Alvarez-Garcia, J.A., Ortega, J.A., Gonzalez-Abril, L., et al.: Trip destination prediction based on past GPS log using a Hidden Markov Model. Expert Syst. Appl. 37(12), 8166–8171 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhuang, Z., Wang, L., Hao, Z., Qi, H., Shen, Y., Yin, B. (2022). Traffic Knowledge Graph Based Trajectory Destination Prediction. In: Liao, X., et al. Big Data. BigData 2022. Communications in Computer and Information Science, vol 1496. Springer, Singapore. https://doi.org/10.1007/978-981-16-9709-8_14
Download citation
DOI: https://doi.org/10.1007/978-981-16-9709-8_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9708-1
Online ISBN: 978-981-16-9709-8
eBook Packages: Computer ScienceComputer Science (R0)
