This study aims at modelling unassisted drivers’ speed at the yellow onset to enhance Connected and Autonomous Vehicles applications at signalised intersections and maximise drivers’ comfort. For this purpose, a total of 2442 real-life vehicle trajectories were analysed to extract driver behavioural measures (i.e. speed, acceleration, and distance to intersection) at different times before the yellow onset. These behavioural measures were used to integrate drivers’ perceptual ability into modelling drivers’ speed at the yellow onset. To develop these models, three machine learning techniques; namely, linear regression, Support Vector Machine, and Neural Networks have been adopted. The best model was a neural network model and was selected based on the goodness-of-fit of the test dataset which has an R-squared value of 0.97. The results indicate that the speed at the yellow onset can be estimated based on behavioural measures while accounting for drivers’ perceptual ability. Also, the model can contribute to a V2I application by assisting the driver in a partially autonomous vehicle to avoid trapping in the dilemma zone and stop safely at signalised intersections. Also, the model can be used to recommend a comfortable riding speed, from a rider’s perspective to a fully autonomous vehicle.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Perceptual-based driver behaviour modelling at the yellow onset of signalised intersections


    Contributors:

    Published in:

    Publication date :

    2022-03-04


    Size :

    26 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Signalised intersections and driver performance

    Triggs, Thomas J. | TIBKAT | 1981


    Some aspects of driver behaviour in signalised intersections

    Mussone, L. / Reitani, G. | British Library Conference Proceedings | 1996


    Capacity and driver behaviour in Indonesian signalised intersections

    Baang, K.-L. / Palgunadi / Transportation Research Board; Committee on Highway Capacity and Quality of Service et al. | British Library Conference Proceedings | 1994


    Modelling Driver Behaviour at Urban Signalised Intersections Using Logistic Regression and Machine Learning

    Ahmad H. Alomari / Bara’ W. AL-Mistarehi / Al-Jammal A. Al-Jammal et al. | DOAJ | 2023

    Free access

    Delay and emissions modelling for signalised intersections

    Zhu, Feng / Lo, Hong K. / Lin, Hong-Zhi | Taylor & Francis Verlag | 2013