Highlights Proposing a novel framework to classify driving styles based on large-scale online car-hailing data. Dividing the driving tasks into three categories (defined as cruising, ride requests, and drop-off) according to the tasks of professional drivers of online-hailed vehicles. Analyzing driving style based on maneuver detection (defined as turning, acceleration, deceleration). Analyzing variations in driving style during turning, acceleration, and deceleration maneuvers among the three driving tasks. The proposed framework is evaluated by a real case in Nanjing, China.

    Abstract As a product of the shared economy, online car-hailing platforms can be used effectively to help maximize resources and alleviate traffic congestion. The driver’s behavior is characterized by his or her driving style and plays an important role in traffic safety. This paper proposes a novel framework to classify driving styles (defined as aggressive, normal, and cautious) based on online car-hailing data to investigate the distinct characteristics of drivers when performing various driving tasks (defined as cruising, ride requests, and drop-off) and undergoing certain maneuvers (defined as turning, acceleration, and deceleration). The proposed model is constructed based on the detection and classification of driving maneuvers using a threshold-based endpoint detection approach, principal component analysis, and k-means clustering. The driving styles that the driver exhibits for the different driving tasks are compared and analyzed based on the classified maneuvers. The empirical results for Nanjing, China demonstrate that the proposed framework can detect driving maneuvers and classify driving styles accurately. Moreover, according to this framework, driving tasks lead to variations in driving style, and the variations in driving style during the different driving tasks differ significantly for turning, acceleration, and deceleration maneuvers.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Driving style recognition and comparisons among driving tasks based on driver behavior in the online car-hailing industry


    Beteiligte:
    Ma, Yongfeng (Autor:in) / Li, Wenlu (Autor:in) / Tang, Kun (Autor:in) / Zhang, Ziyu (Autor:in) / Chen, Shuyan (Autor:in)


    Erscheinungsdatum :

    2021-03-16




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Online car-hailing distracted driving behavior identification method based on multi-source data

    MA YONGFENG / WANG FAN / XING GUANYANG et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    Online driving style recognition using fuzzy logic

    Dorr, Dominik / Grabengiesser, David / Gauterin, Frank | IEEE | 2014


    Driver driving distraction behavior recognition method based on driving behavior data

    YUAN WEI / ZHANG KANGKANG / CAO LONG et al. | Europäisches Patentamt | 2021

    Freier Zugriff

    DRIVER CLASSIFICATION AND DRIVING STYLE RECOGNITION USING INERTIAL SENSORS

    Ly, M. / Martin, S. / Trivedi, M. et al. | British Library Conference Proceedings | 2013


    Automatic driving online car-hailing operation method, device and equipment

    ZHAO YIMING / XU JIANYONG / WU QIXUAN et al. | Europäisches Patentamt | 2022

    Freier Zugriff