Conventional travel behavior research relies on questionnaires to understand individual travel patterns. However, these studies consume considerable manpower and resources. In a connected vehicle environment, on-board diagnostic (OBD) devices can record engine status, trajectories, acceleration, and fuel consumption of a vehicle to capture long-term variability in driver behaviors. In this study, the heterogeneous travel patterns of drivers are modeled using 2-month OBD data. An algorithm called clustering by fast search and find of density peaks is employed to classify drivers into long-distance and occasional, high-frequency, and regular travelers. The average travel distance, travel days, and first and last departure time records are considered in the procedure. A multi-dimensional discrete hidden Markov model is used to predict the category of any driver based on their historical travel behavior. This study provides useful data sources for activity-based modeling and also demonstrates the potential of vehicle OBD data for developing targeted online services.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Understanding Drivers’ Travel Behaviors through Vehicle Onboard Diagnostic Data Using Multi-Dimensional Discrete Hidden Markov Model


    Contributors:
    Wang, Yunpeng (author) / Yin, Guohao (author) / Ma, Xiaolei (author)

    Conference:

    17th COTA International Conference of Transportation Professionals ; 2017 ; Shanghai, China


    Published in:

    CICTP 2017 ; 98-107


    Publication date :

    2018-01-18




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English







    Modeling Pipeline Driving Behaviors: Hidden Markov Model Approach

    Zou, Xi / Levinson, David | Transportation Research Record | 2006


    Modeling Pipeline Driving Behaviors: Hidden Markov Model Approach

    Zou, X. / Levinson, D. M. / National Research Council (U.S.) | British Library Conference Proceedings | 2006