Transportation is an important factor that affects energy consumption, and driving behavior is one of the main factors affecting vehicle fuel consumption. The purpose of this paper is to improve fuel consumption monitoring databases based on mobile phone data. Based on the mobile phone terminals and on-board diagnostic system (OBD) installed in taxis, driving behavior data and fuel consumption data are extracted, respectively. By matching the driving behavior data collected by a mobile phone with the fuel consumption data collected by OBD, the correlation between driving behavior and fuel consumption is explored, so that vehicle fuel consumption could be predicted based on mobile phone data. The fuel consumption prediction models are built using back propagation (BP) neural network, support vector regression (SVR), and random forests. The results show that the average speed, average speed except for idle (ASEI), average acceleration, average deceleration, acceleration time percentage, deceleration time percentage, and cruising time percentage are important indicators for fuel consumption evaluation. All three models could predict fuel consumption accurately, with an absolute relative error less than 10%. The random forest model is proved to have the highest accuracy and runs faster, making it suitable for wide application. This method lays a foundation for monitoring database improvement and fine management of urban transportation fuel consumption.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Vehicle Fuel Consumption Prediction Method Based on Driving Behavior Data Collected from Smartphones


    Beteiligte:
    Ying Yao (Autor:in) / Xiaohua Zhao (Autor:in) / Chang Liu (Autor:in) / Jian Rong (Autor:in) / Yunlong Zhang (Autor:in) / Zhenning Dong (Autor:in) / Yuelong Su (Autor:in)


    Erscheinungsdatum :

    2020




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Instantaneous Fuel Consumption Estimation Using Smartphones

    Shaw, Samuel / Hou, Yunfei / Zhong, Weida et al. | IEEE | 2019


    Fuel consumption-based driving behavior scoring

    ZENG XIANGRUI / MOHANTY AMIT | Europäisches Patentamt | 2020

    Freier Zugriff

    How GPS route data collected from smartphones can benefit bicycle planning

    Meyer, Joel L. / Duthie, Jennifer C. | TIBKAT | 2018


    Vehicle remaining route fuel consumption prediction method and system based on driving style

    PENG ZHAOHUI / SONG QIAO / HOU XUAN et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Fuel cell vehicle driving range prediction method based on historical real-time energy consumption

    JIANG JUNZHAO / YANG WENHAO / PENG BIN et al. | Europäisches Patentamt | 2023

    Freier Zugriff