Prediction of bus arrival time is an important part of intelligent transportation systems. Accurate prediction can help passengers make travel plans and improve travel efficiency. Given the nonlinearity, randomness, and complexity of bus arrival time, this paper proposes the use of a wavelet neural network (WNN) model with an improved particle swarm optimization algorithm (IPSO) that replaces the gradient descent method. The proposed IPSO-WNN model overcomes the limitations of the gradient-based WNN which can easily produce local optimum solutions and stop the training process and thus improves prediction accuracy. Application of the model is illustrated using operational data of an actual bus line. The results show that the proposed model is capable of accurately predicting bus arrival time, where the root-mean square error and the maximum relative error were reduced by 42% and 49%, respectively.


    Access

    Download


    Export, share and cite



    Title :

    Bus Arrival Time Prediction Using Wavelet Neural Network Trained by Improved Particle Swarm Optimization


    Contributors:
    Yuanwen Lai (author) / Said Easa (author) / Dazu Sun (author) / Yian Wei (author)


    Publication date :

    2020




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown







    A Traffic Forecasting Model Using Adaptive Particle Swarm Optimization Trained Neural Network

    Xu, Rong / Zhou, Dong / Jiang, Shizheng et al. | British Library Online Contents | 2015


    A Novel Wavelet Neural Network Based on Improved Particle Swarm Optimization Algorithm

    Xu, X.-p. / Qian, F.-c. / Liu, D. et al. | British Library Online Contents | 2008