A Deep Learning Approach for Estimating Inventory Rebalancing Demand in Bicycle Sharing Systems

Abstract

Meeting user demand is one of the most challenging problems arising in public bicycle sharing systems. Various factors, such as daily commuting patterns or topographical conditions, can lead to an unbalanced state where the numbers of rented and returned bicycles differ significantly among the stations. This can cause spatial imbalance of the bicycle inventory which becomes critical when stations run completely empty or full, and thus prevent users from renting or returning bicycles. To prevent such service disruptions, we propose to forecast user demand in terms of expected number of bicycle rentals and returns and accordingly to estimate number of bicycles that need to be manually redistributed among the stations by maintenance vehicles. As opposed to traditional solutions to this problem, which rely on short-term demand forecasts, we aim to maximise the time within which the stations remain balanced by forecasting user demand multiple steps ahead of time. We propose a multi-input multi-output deep learning model based on Long Short-Term Memory networks to forecast user demand over long future horizons. Conducted experimental study over real-world dataset confirms the efficiency and accuracy of our approach.

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