To save more for taxi drivers, in this chapter, we present a two-phase fuel-efficient path-planning framework called GreenPlanner. In the first phase, we build a personalized fuel consumption model (PFCM) for each driver, based on the individual driving behaviors embedded in the GPS trajectory data and the physical features (e.g., traffic lights, stop signs, road network topology) along the routes provided by road network data. Furthermore, we build a general PFCM which only needs some basic information about the driver, including the category of overall fuel consumption performance in history and the car mode. In the second phase, with the real-time traffic information collected via the mobile crowdsensing manner, we are able to estimate and compare the fuel cost among different routes for a given driver, and recommend him/her with the most fuel-efficient one. We evaluate the two-phase framework using the real-world datasets, and results demonstrate that, compared to the baseline models, the proposed model achieves the best accuracy. Moreover, users could save about 20% fuel consumption on average if driving along the suggested routes in our case studies.


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    Titel :

    GreenPlanner: Planning Fuel-Efficient Driving Routes


    Beteiligte:
    Chen, Chao (Autor:in) / Zhang, Daqing (Autor:in) / Wang, Yasha (Autor:in) / Huang, Hongyu (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2021-04-02


    Format / Umfang :

    27 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch