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)

时空轨迹和复合收益的动态上车点推荐

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.
    刘秋月(1995—),女,黑龙江黑河人,硕士研究生,主要研究方向为智能搜索算法、车辆调度问题。
    LIU Qiuyue, born in 1995, M.S. candidate. Her research interests include intelligent search algorithm and vehicle scheduling problem.
  • 基金资助:
    国家自然科学基金(61404069);辽宁省自然科学基金(2019-ZD-0048);辽宁省教育厅基础研究项目(LJ2019JL012)

Abstract:

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

摘要:

随着互联网技术的快速发展,网约车已成为出行的重要方式。通过智能手段为用户推荐上车点不仅可有效实现分流缓解道路拥堵,也可减少乘客与司机的沟通成本,提升司机的服务效率并降低乘客的等待时间,从而提高司乘双方的出行体验感。但现存推荐方法所依据的指标较为单一,未在乘客便利性与司机收益之间取得较好平衡,且无法保证所推荐上车点的安全性与可达性。通过对时空轨迹大数据的归纳与分析,提取保证可达性的潜在上车点,避免依据单一指标推荐上车点所导致的偏袒性问题,综合考虑乘客步行收益、司机驾驶收益、上车点路况指标、周边安全性等关键因素,建立上车点的复合收益评价,构建上车点的动态推荐模型。以约束控制同时段同上车点的推荐量,有效解决由于单上车点订单堆积而造成的非必要等待和资源浪费,在一定程度上缓解交通压力。基于真实网约车数据的实验表明,该模型和推荐方法可实现上车点的有效动态分配,较单一指标上车点推荐方法有较好的综合收益与时间优势。从司机与乘客双方角度出发,在降低行程总时间的基础上,提升全局司机接驾效率并降低乘客等待时间,且推荐结果的综合评价值优于现存推荐方法。

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

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