Highlights We present a Smart Predict-then-Optimize method for dynamic green bike relocation in the free-floating system. A novel multi-task spatio-temporal neural network is proposed to predict bike-sharing inflow and outflow demands. Potential unusable shared bikes are discovered from the users’ travel behavior and the usage characteristics of shared bikes. An iterative decomposition algorithm is implemented for relocation routes and vehicle speed optimization.

    Abstract Cities are facing numerous challenges such as road traffic congestion and environmental pollution. Bike-sharing, as an emission-free travel mode, aligns with the principle of green and environmental protection and serves mainly for short-distance trips in urban areas. Shared bikes pose significant issues to operators due to supply–demand imbalances across different time and space. This study proposes a Smart Predict-then-Optimize method for dynamic green bike relocation in the free-floating system, which aims to minimize the cost of fuel and carbon emissions from repositioning vehicles and the total unmet demand during the operating period. A multi-task deep neural network model is designed to predict regional inflow and outflow demand, where targeted modules are embedded to extract the spatio-temporal characteristics. Potential unusable shared bikes are discovered from the users’ travel behavior and the usage characteristics of shared bikes. Then, we build a data-driven optimization model for bike-sharing relocation and design an iterative decomposition algorithm that incorporates an adaptive large neighborhood search for relocation routes and vehicle speed optimization. The proposed method is tested on real-world bike-sharing trips in Shenzhen, China, and results show that the relocation distance and carbon emission cost can be reduced by 11.69% and 14.09% by relocating operational bikes and unusable bikes simultaneously. Additionally, route decision-making with speed optimization can decrease the total fuel and emission, where considering the collection of unusable bikes help improve the service level of the system.


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    Titel :

    A Smart Predict-then-Optimize method for dynamic green bike relocation in the free-floating system


    Beteiligte:
    Chang, Ximing (Autor:in) / Wu, Jianjun (Autor:in) / Sun, Huijun (Autor:in) / Yan, Xuedong (Autor:in)


    Erscheinungsdatum :

    2023-06-13




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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