For station-based bike-sharing systems, the balance between user demand and bike allocation is critical for the operation. As a basic operational index, the short-term prediction of bike numbers (flow) plays an important role in demand forecasting and rebalancing resources of bike-sharing networks. Many different methods have been proposed for bike forecast in recent years, and the deep learning (DL)-based models have dominated this area because of their competitive performance. However, there still exist challenges in such approaches including: (i) how to appropriately select the training input for the DL model, and (ii) how to effectively utilize both the temporal and spatial features in the data for prediction. In particular, the arbitrary input may limit the model optimization, and the separate consideration of temporal and spatial features could change the original data representation. This paper uses a simple autocorrelation function to select the best input candidates and develops a three-dimensional (3 D) residual neural network to learn spatiotemporal features simultaneously. The proposed DL model is trained and validated using two separate bike-sharing datasets from New York and Suzhou cities. The learned features by two-dimensional (2 D) and 3 D CNN kernels under different input methods are compared. Results show that 3 D CNN outperforms other models and that the proposed input selection method yields better learning results for both datasets. The proposed methods help with a comprehensive DL model workflow and better forecasting accuracy.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Short-term traffic flow prediction in bike-sharing networks


    Contributors:
    Wang, Bo (author) / Vu, Hai L. (author) / Kim, Inhi (author) / Cai, Chen (author)

    Published in:

    Publication date :

    2022-06-15


    Size :

    15 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown





    Short-term Prediction of Bike-sharing Usage Considering Public Transport: A LSTM Approach

    Zhang, Cheng / Zhang, Linan / Liu, Yangdong et al. | IEEE | 2018


    Long-term & short-term bike sharing demand predictions using contextual data

    Tabandeh, Mirfarnam / Antoniou, Constantinos / Cantelmo, Guido | IEEE | 2023


    SHARING BIKE AND SHARING BIKE SYSTEM

    YUE WEI | European Patent Office | 2019

    Free access