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Subject: search.filters.subject.Intelligent Transportation Systems

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    Vehicle Travel Time Estimation in Transportation Network Using Random Forest and Neural Network
    (2025-01-01)
    Nakano, Shuya
    ;
    Panichpapiboon, Sooksan
    ;
    Kulla, Elis
    Several technologies in Intelligent Transportation Systems (ITS), such as automatic driving, electric vehicles, vehicular communications, are reshaping the way we travel and use the transportation system. Automatization of ITS mainly consists of traffic management, traffic light control, optimal route selection and so on. In these automatic applications, accurate estimation of vehicle travel time is essential to make efficient decisions. This study uses synthetic traffic data generated by Simulation of Urban MObility (SUMO) to evaluate machine learning models, like Random Forest and Neural Network, for travel time estimation. A variety of traffic-related features were collected, and three feature scenarios were tested. Results show that the Random Forest model outperforms both the neural network and the baseline method based on numerical estimation, highlighting the benefit of feature-rich approaches.
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    Network-wide on-line travel time estimation with inconsistent data from multiple sensor systems under network uncertainty
    (2018-01-02)
    Shao, Hu
    ;
    Lam, William H.K.
    ;
    Sumalee, Agachai
    ;
    Chen, Anthony
    This paper proposes a new modeling approach for network-wide on-line travel time estimation with inconsistent data from multiple sensor systems. It makes full use of both the available data from multiple sensor systems (on-line data) and historical data (off-line data). The first- and second-order statistical properties of the on-line data are investigated together with the data inconsistency issue to estimate network-wide travel times. The proposed model is formulated as a generalized least squares problem with non-linear constraints. A solution algorithm based on the penalty function method is adopted to solve the proposed model, whose application is illustrated by numerical examples using a local road network in Hong Kong.