Data-driven prioritization strategies for inventory rebalancing in bike-sharing systems

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The popularity of bike-sharing systems has constantly increased throughout the last years. Most of such success can be attributed to their multiple benefits, such as user convenience, low usage costs, health benefits and their contribution to environmental relief. However, satisfying all user demands remains a challenge, given that the inventories of bike-sharing stations tend to be unbalanced over time. Bike-sharing system operators must therefore intervene to rebalance station inventories to provide both available bikes and empty docks to the commuters. Due to limited rebalancing resources, the number of stations to be rebalanced often exceeds the system's rebalancing capacity, especially close to peak hours. As a consequence, operators are forced to manually select stations and determine the appropriate quantities of bikes for rebalancing. In practice, such manual planning is likely to result in suboptimal system performance. In this paper, we propose four strategies to select the stations that should be prioritized for rebalancing, using features such as the predicted trip demand, as well as the inventory levels at the stations themselves and their surrounding stations. We evaluate the performance of these prioritization strategies by simulating real-world trips using data from 2019 and 2020, each of which exhibits distinct travel patterns given the restrictive measures implemented in 2020 to prevent the spread of COVID-19. One of these strategies significantly improves the system's performance by reducing the lost demand by up to 65%, while another strategy reduces the number of required rebalancing operations by up to 33%, when compared to the prioritization scheme currently used within our bike-sharing system use case. Finally, one prioritization strategy encourages the selection of stations that are geographically clustered, which may facilitate rebalancing operations afterwards.

, 20 pages

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