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Research on Destination Prediction for Urban Taxi based on GPS Trajectory

Volume 13, Number 4, July 2017 - Paper 20 - pp. 530-539
DOI: 10.23940/ijpe.17.04.p20.530539

Meng Zhanga, Yongjian Yanga, Liping Huanga, Xiaopeng Zhangb

aCollege of Computer Science and Technology, Jilin University, Changchun,China
bCollege of Software, Jilin University, Changchun, China

(Submitted on February 14, 2017; Revised on May 3, 2017; Accepted on June 15, 2017)


Researching on destination prediction has a particularly important influence on the location-based services' popularization. The traditional destination prediction algorithm is to retrieve the historical trajectory data to find the same trajectory sequences as the query trajectory and then derive the most likely location to be the predicted result. However, due to the limitation of the historical trajectory data, this method has low efficiency and accuracy. Thus, in this paper, we propose the Prediction algorithm based on time (PBT algorithm), which considers the influence of the factor of time on destination prediction. Experiments based on real data show that in terms of destination prediction, the PBT algorithm not only alleviates the limitation of the historical data in the traditional algorithm to make the results more realistic, but also is more effective.


References: 11

    1.    Nadembega, A., Taleb, T., Hafid, A.(2012) “A Destination Prediction Model based on historical data, contextual knowledge and spatial conceptual maps”, IEEE International Conference on Communications, pp.1416--1420.
    2.    Dash, M., Koo, K. K., Krishnaswamy, S. P., Jin, Y., Shi-Nash, A.(2016) “Visualize People's Mobility-Both individually and Collectively-Using Mobile Phone Cellular Data”, 17th IEEE International Conference on Mobile Data Management, Vol. 1, pp. 341--344.
    3.    Shinmura, T., Zhu, D., Ota, J., Fukazawa, Y.(2014) “Destination prediction considering both tweet contents and location transition hitstory”,  Seventh International Conference on Mobile Computing and Ubiquitous Networking, pp. 95--96.
    4.    Li, X., Li, M., Gong, Y. J., Zhang, X. L., Yin, J.(2016) “T-DesP: Destination Prediction Based on Big Trajectory Data”, IEEE Transactions on Intelligent Transportation Systems, Vol. 17, pp.2344--2354.
    5.    Jin, L., Han, M., Liu, G.,Feng, L.(2014) “Detecting Cruising Flagged Taxis' Passenger-Refusal Behaviors Using Traffic Data and Crowdsourcing”, 11th Intl Conf on Ubiquitous Intelligence and Computing and 11th Intl Conf on Autonomic and Trusted Computing, and 14th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UTC-ATC-ScalCom), pp. 18--25.
    6.    Singh, A. D., Wu, W., Xiang, S., Krishnaswamy, S.(2015) “Taxi trip time prediction using similar trips and road network dat”, 2015 IEEE International Conference on Big Data, pp. 2892--2894.
    7.    Chen, L., Lv, M., Chen, G.(2010) “A system for destination and future route prediction based on trajectory mining”,  Pervasive and Mobile Computing, Vol. 6, pp.657--676.
    8.    Xue, A. Y., Zhang, R., Zheng, Y., Xie, X., Huang, J., Xu, Z.(2013) “Destination prediction by sub-trajectory synthesis and privacy protection against such prediction”,  29th International Conference on Data Engineering (ICDE), pp.254-265.
    9.    Pang, J.(2015) “A new Markov model of reliability assurance and failure prediction using network technology”,  4th International Conference on Computer Science and Network Technology, Vol. 1, pp. 776--780.
    10.    Wen, L., Gao, Q.(2014) “Research on the Feasibility of the Markov Prediction Model on Energy Consumption”.  Journal of Information and Computational Science,  Vol. 11, pp.3149--3155.
    11.    Shi, Y., Wen, Y., Fan, Z., Miao, Y.(2013) “Predicting the next scenic spot a user will browse on a tourism website based on markov prediction model”, 25th International Conference on Tools with Artificial Intelligence, pp. 195--200.



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