Estimating the origin-destination travel time is a fundamental problem in many location-based services for vehicles, e.g., ride-hailing, vehicle dispatching, and route planning. Recent work has made significant progress to accuracy, but they largely rely on GPS trajectories which are too coarse to model many personalized driving behaviors, e.g., differentiating novice and veteran drivers. In this paper, we propose Customized Travel Time Estimation (CTTE) that fuses GPS trajectories, smartphone inertial data, and road network within a deep recurrent neural network. It constructs a road link traffic database with topology representation, speed statistics, and query distribution. It also calibrates inertial readings, estimates the arbitrary phone’s pose in car, and detects multiple aggressive driving events (e.g., bump judders, sharp turns, sharp slopes, frequent lane shifts, overspeeds, and sudden brakes). Finally, we demonstrate our solution on two typical transportation problems, i.e., predicting traffic speed at holistic level and estimating customized travel time at personal level, within a multi-task learning structure. Experiments on two large-scale real-world traffic datasets from DiDi platform show our effectiveness compared with the state-of-the-art.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    CTTE: Customized Travel Time Estimation via Mobile Crowdsensing


    Contributors:
    Gao, Ruipeng (author) / Sun, Fuyong (author) / Xing, Weiwei (author) / Tao, Dan (author) / Fang, Jun (author) / Chai, Hua (author)

    Published in:

    Publication date :

    2022-10-01


    Size :

    2783082 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Mobile crowdsensing with mobile agents

    Leppänen, T. (Teemu) / Lacasia, J. Á. (José Álvarez) / Tobe, Y. (Yoshito) et al. | BASE | 2017

    Free access

    INFERRING LEFT-TURN INFORMATION FROM MOBILE CROWDSENSING

    SHIN KANG G / CHEN DONGYAO | European Patent Office | 2021

    Free access

    INFERRING LEFT-TURN INFORMATION FROM MOBILE CROWDSENSING

    SHIN KANG G / CHEN DONGYAO | European Patent Office | 2023

    Free access

    Location-Dependent Task Bundling for Mobile Crowdsensing

    Zhen, Yan / Wang, Yunfei / He, Peng et al. | IEEE | 2022


    Estimating Travel Speed of a Road Section Through Sparse Crowdsensing Data

    Wang, Cheng / Xie, Zhiyang / Shao, Lu et al. | IEEE | 2019