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Citation |
Thomas MM, Verma A, Mayakuntla SK, Chandra A. Simulat. Model. Pract. Theor. 2024; 131: e102871. |
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Copyright |
(Copyright © 2024, Elsevier Publishing) |
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DOI |
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PMID |
unavailable |
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Abstract |
The increased demand for a safe, reliable, cost-effective, clean, and social distancing-friendly mode of commuting in the post-COVID world has motivated the transportation research community to closely analyse and optimise the operations of the existing Bike-Sharing Systems (BSS). One of the key elements in BSS operation is the redistribution of bicycles to cope with the spatiotemporal asymmetry in the demands across the docking stations. Operators usually employ trucks to do the rebalancing at regular intervals. Alternatively, strategies encouraging user-based rebalancing that allow the continuous redistribution of bicycles can be implemented. Dynamic user-based rebalancing strategies are often considered a feasible approach to improve the reliance and performance of the BSS and have been gaining prominence. This paper presents a simulation-driven dynamic user-based redistribution framework using the novel Distance-Willingness-Reward (DWR) matrix method. The framework shows the capability to generate dynamic incentives for the users, motivating them to participate in the rebalancing process, leading to the continuous redistribution of bicycles at each station. The Distance-Willingness-Reward (DWR) matrix method generates the required dynamic incentives for each user, and the bike requirements at each docking station are evaluated using an improved suggestion measure, the Station Impact Index (SII). Simulation experiments are designed and run for 30 days of operation considering 30 stations. Language: en |
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Keywords |
Bike-sharing system; Bike-sharing system simulator; Dynamic user incentivization; Rebalancing approach; Simulation modelling; User-based Rebalancing |