In this paper, we aim to provide an optimal passenger matching solution by recommending ridesharing groups of passengers from GPS trajectories. Existing algorithms for rider grouping usually rely on matching pre-selected origin-destination coordinates. Unfortunately, the semantics in the spatial layout (e.g., social interactions and properties of the locations) are ignored, leading to inaccuracies in discovering the ridesharing groups. Meanwhile, the destinations manually entered by users impact the accuracy of matching, as these addresses are usually not available in a road network or are not optimal for passenger pickup. This is particularly true when a passenger travels in a less familiar place. Given a set of passengers and the distribution of their destination, our approach is to compute the ridesharing matching between passengers. The raw GPS trajectories can be characterized by a combination of time constraints, traffic environments, and social activities. We first developed a PrefixSpan-prediction using a partial matching (P-PPM) destination-prediction algorithm to mine the frequent movement patterns from the trajectory data and determine the confidence of the movement rules. Our method uses the total travel time as the matching objective. Our approach is superior to the baseline methods in terms of accuracy (increased from 46% to 80%). We have also achieved significant improvements on other metrics, such as users’ saved travel distance. We demonstrated that using our proposed method, a group of passengers could save over 19% of total travel miles, which shows that the ridesharing scheme could be effective.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Recommendation for Ridesharing Groups Through Destination Prediction on Trajectory Data


    Beteiligte:
    Tang, Lei (Autor:in) / Duan, Zongtao (Autor:in) / Zhu, Yishui (Autor:in) / Ma, Junchi (Autor:in) / Liu, Zihang (Autor:in)


    Erscheinungsdatum :

    01.02.2021


    Format / Umfang :

    3188106 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    DESTINATION PREDICTION BASED ON PARTIAL TRAJECTORY DATA

    Ebel, Patrick / Göl, Ibrahim Emre / Lingenfelder, Christoph et al. | British Library Conference Proceedings | 2020


    Destination Prediction Based on Partial Trajectory Data

    Ebel, Patrick / Gol, Ibrahim Emre / Lingenfelder, Christoph et al. | IEEE | 2020


    Vehicle Destination Prediction Based on Trajectory Data

    Guo, Wenming / Jia, Ruiqi / Zong, Che et al. | IEEE | 2024


    RIDESHARING SUPPORT SYSTEM, RIDESHARING SUPPORT METHOD, AND RIDESHARING SUPPORT DEVICE

    MORIOKA WATARU | Europäisches Patentamt | 2019

    Freier Zugriff

    Ridesharing management device, ridesharing management method, and program

    FUJIMOTO NAOTOSHI / ITO YO / IWAMOTO SUSUMU et al. | Europäisches Patentamt | 2024

    Freier Zugriff