The growing popularity of shared transportation services relies heavily on their accessibility to users. Achieving a balanced supply and demand for dockless shared electric bicycles (e-bikes) is crucial for the widespread adoption of such services. In this study, we introduce a fusion model that incorporates a hierarchical structure integrating various features. Our model sequentially processes the spatial dependencies of demand using convolutional neural networks (CNN), followed by the temporal dependencies using long short term memory (LSTM). Additionally, we employ CNN to extract the spatial dependencies of points of interest (POI) and introduce additional layers to handle external features capturing their variability. Notably, compared to other transportation modes, shared e-bike trips typically involve shorter distances and require finer spatial grids, which pose a challenge that needs to be addressed effectively. Our proposed model demonstrates high accuracy and generalization capabilities, even when dealing with fine-grained gridding and sparse data generated at the finer granularity. The results indicate that POI and timestamp are crucial for demand forecasting, while weather variables are less significant.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Fusion Deep Learning Network for Shared e-Bike Demand Prediction with Spatiotemporal Dependencies


    Contributors:
    Yin, Ailing (author) / Chen, Xiaohong (author) / Zou, Guojian (author)


    Publication date :

    2023-09-24


    Size :

    788506 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Spatiotemporal Deep-Learning Networks for Shared-Parking Demand Prediction

    Liu, Yonghong / Liu, Chunyu / Luo, Xia | ASCE | 2021




    Station-Level Hourly Bike Demand Prediction for Dynamic Repositioning in Bike Sharing Systems

    Wu, Xinhua / Lyu, Cheng / Wang, Zewen et al. | Springer Verlag | 2019


    Station-level Demand Prediction for Bike-Sharing System

    Ramesh, Arthi Akilandesvari / Nagisetti, Sai Pavani / Sridhar, Nikhil et al. | IEEE | 2021