Leveraging Data for Better Bike Sharing: A Methodology for Terminal Availability Prediction

In urban environments, bicycle-sharing emerges as an eco-friendly solution, yet inherent imbalances in bicycle rents and returns necessitate systematic rebalancing, posing a global challenge. This underscores the crucial role of forecasting in optimizing bicycle allocation across diverse docks. Despite the commendable goals of reducing carbon emissions and promoting public health, efficient rebalancing remains elusive, emphasizing the need for forecasting to enhance overall system efficiency. User demand in public bicycle-sharing systems presents a primary challenge, influenced by commuting patterns and topographical conditions, leading to critical spatial incongruities. Integration of robust demand prediction mechanisms becomes essential, strategically overcoming challenges and ensuring seamless bicycle-sharing system operation. Our solution proactively addresses disruptions by forecasting user demand and employing manual redistribution based on Long Short-Term Memory (LSTM) models. Empirical validation attests to its efficiency and accuracy, showcasing versatility. The system seamlessly integrates frameworks for forecast delivery to applications, ensuring robustness and high availability through meticulous dataset consumption.