Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (7): 1611-1622.DOI: 10.3778/j.issn.1673-9418.2012039

• A.pngicial Intelligence • Previous Articles     Next Articles

Dynamic Pickup-Point Recommendation Based on Spatiotemporal Trajectory and Hybrid Gain Evaluation

GUO Yuhan+,(), LIU Qiuyue   

  1. School of Software, Liaoning Technical University, Huludao, Liaoning 125105, China
  • Received:2020-12-10 Revised:2021-02-07 Online:2022-07-01 Published:2021-02-26
  • Supported by:
    the National Natural Science Foundation of China(61404069);the Natural Science Foundation of Liaoning Province(2019-ZD-0048);the Basic Research Project of Department of Education of Liaoning Province(LJ2019JL012)


郭羽含+,(), 刘秋月   

  1. 辽宁工程技术大学 软件学院,辽宁 葫芦岛 125105
  • 作者简介:郭羽含(1983—),男,黑龙江哈尔滨人,博士,副教授,硕士生导师,CCF会员,主要研究方向为智能搜索算法、车辆调度问题、供应链优化问题。
    GUO Yuhan, born in 1983, Ph.D., associate professor, M.S. supervisor, member of CCF. His research interests include intelligent search algorithm, vehicle scheduling problem and supply chain optimization problem.
    LIU Qiuyue, born in 1995, M.S. candidate. Her research interests include intelligent search algorithm and vehicle scheduling problem.
  • 基金资助:


With the rapid development of Internet technology, online car-hailing has become an important way of travel. Recommending boarding points for users through intelligent means can not only effectively achieve traffic diversion and alleviate congestion, but also reduce the communication cost between passengers and drivers, improve the service efficiency of drivers and reduce the waiting time of passengers, so as to improve the travel experience of both drivers and passengers. However, the existing recommendation methods are normally based on a single criterion, which do not achieve a good balance between passenger convenience and driver income, and can not guarantee the safety and accessibility of the recommended boarding point. By summarizing and analyzing the big data of spatiotemporal trajectory, this paper extracts the potential boarding points to ensure accessibility, avoids the bias caused by considering a single criterion, and comprehensively considers the key factors such as passenger walking income, driver driving income, road condition, and peripheral safety. It establishes a composite income evaluation of boarding points, and constructs the dynamic recommendation model of boarding points to control the recommended quantity of the same boarding point at the same time with constraints, effectively solving the unnecessary waiting and waste of resources caused by the accumulation of orders at a single boarding point, and alleviating the traffic pressure to a certain extent. Experiments based on real online car-hailing data show that the proposed model and recommendation method can achieve an effective dynamic allocation of boarding points, and have better comprehensive benefits and time advantages than the single criterion method. From the perspective of both drivers and passengers, on the basis of reducing the total travel time, the proposed method improves the overall driver pick-up efficiency and reduces the waiting time of passengers, and the comprehensive evaluation shows the obtained results are better than those provided by the existing recommendation methods.

Key words: intelligent transportation, pickup-point recommendation, hybrid gain, spatiotemporal trajectory data, heuristic algorithm



关键词: 智能交通, 上车点推荐, 复合收益, 时空轨迹数据, 启发式算法

CLC Number: