Abstract
Most of the existing destination prediction methods are based on historical travel trajectory data. There is rarely method to predict users’ travel destination only depending on departure time and the coordinates of departure point. In this paper, we use a real-world travel dataset, which only contains time and the coordinate of users’ travel location, and no trajectories, we propose a new destination prediction algorithm, which is composed of three modules, including candidate destinations supplement, feature extraction and classifier training. For some users who have rarely travel records, according to a supplement rule, we choose tens of candidate destinations from millions of data. We extract statistical feature, temporal feature, spatial neighbor feature and graph feature from the perspective of the user group, time and geographical location. Finally, the performance of our proposed algorithm in terms of score and running time is demonstrated by experiments.
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References
Dai, J., Yang, B., Guo, C., Ding, Z.: Personalized route recommendation using big trajectory data. In: ICDE2015, pp. 543–554 (2015)
Pan, B., Zheng, Y., Wilkie, D., Shahab, C.: Crowd sensing of traffic anomalies based on human mobility and social media. SIGSPATIAL GIS 2013, 334–343 (2013)
Liu, J., Han, K., Chen, X., Ong, G.P.: Spatial-temporal inference of urban traffic emissions based on taxi trajectories and multi-source urban data. Trans. Research Part C Emerg. Technol. 106, 145–165 (2019)
Zhang, L., Hu, T., Min, Y., Wu, G.: A taxi order dispatch model based on combinatorial optimization. In: ACM SIGKDD2017, pp. 2151–2159 (2017)
Ziebart, B.D., Maas, A.L., Dey, A.K., et al.: Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior. In: UbiCom2008, pp, 322–331 (2008)
Gogate, V., Dechter, R., Bidyuk, B., et al.: Modeling transportation routines using hybrid dynamic mixed networks (2012). arXiv preprint arXiv:1207.1384
Marmasse, N., Schmandt, C.: A user-centered location model. Pers. Ubiquit. Comput. 6(5–6), 318–321 (2002)
Patterson, D.J., Liao, L., Fox, D., et al.: Inferring high-level behavior from low-level sensors. In: UbiCom, 2003, pp. 73–89 (2003)
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)
Tiesyte, D., Jensen, C.S.: Similarity-based prediction of travel times for vehicles traveling on known routes. In: ACM SIGSPATIAL GIS2008, vol. 14 (2008)
Chen, L., Lv, M., Chen, G.: A system for destination and future route prediction based on trajectory mining. Pervasive Mob. Comput. 6(6), 657–676 (2010)
Xue, A., Zhang, R., Zheng, Y., et al. Destination prediction by sub-trajectory synthesis and privacy protection against such prediction. In: ICDE2013, pp. 254–265 (2013)
Besse, P.C., Guillouet, B., Loubes, J.M., et al.: Destination prediction by trajectory distribution-based model. IEEE Trans. Int. Transp. Syst. 19(8), 2470–2481 (2017)
Neto, F.D.N., Baptista, C., Campelo, C.E.C.: Combining Markov model and prediction by partial matching compression technique for route and destination prediction. Knowl.-Based Syst. 154, 81–92 (2018)
Zong, F., Tian, Y., He, J., Tang, J., Lv, J.: Trip destination prediction based on multi-day GPS data. Phys. A Stat. Mech. Appl. 55, 258–269 (2019)
Yang, Z., Sun, H., Huang, J., Sun, H., Qiao, S.: An efficient destination prediction approach based on future trajectory prediction and transition matrix optimization. IEEE Trans. Knowl. Data Eng. 32(2), 203–217 (2020)
De Brébisson, A., Simon, É., Auvolat, A., et al.: Artificial neural networks applied to taxi destination prediction (2015). arXiv preprint arXiv:1508.00021
Endo, Y., Nishida, K., Toda, H., et al.: Predicting destinations from partial trajectories using recurrent neural network. PAKDD 2017, 160–172 (2017)
Lv, J., Li, Q., Sun, Q., et al.: T-CONV: a convolutional neural network for multi-scale taxi trajectory prediction. In: BigComp 2018, pp. 82–89 (2018)
Wu, H., Chen, Z., Sun, W., Zheng, B., Wang, W.: Modeling trajectory with recurrent neural networks. In: IJCAI2017, pp. 3083–3090 (2017)
Zhao, J., Xu, J., Zhou, R., Zhao, P., Liu, C., Zhu, F.: On prediction of user destination by sub-trajectory understanding: a deep learning based approach. In: CIKM, pp. 1413–1422 (2018)
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W.: LightGBM: a highly efficient gradient boosting decision tree. NIPS 2017, 3149–3157 (2017)
Chen, T., Carlos, G.: XGBoost: a scalable tree boosting system. In: ACM SIGKDD2016, pp. 785–794 (2016)
Acknowledgments
This study was supported by the National Natural Science Foundation of China (61602518, 71872180) and the Fundamental Research Funds for the Central Universities, Zhongnan University of Economics and Law (2722019JCG0 74, 2722019JCT035).
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Liu, S., Zhang, L., Chen, X. (2021). Travel Destination Prediction Based on Origin-Destination Data. In: Barolli, L., Poniszewska-Maranda, A., Enokido, T. (eds) Complex, Intelligent and Software Intensive Systems. CISIS 2020. Advances in Intelligent Systems and Computing, vol 1194. Springer, Cham. https://doi.org/10.1007/978-3-030-50454-0_30
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DOI: https://doi.org/10.1007/978-3-030-50454-0_30
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