Rebalance Modern Bike Sharing System: Spatio-Temporal Data Prediction And Path Planning For Multiple Carriers

Modern bike sharing system, in which bikes can be parked freely, extends the flexibility of traditional bike sharing system and thus has greatly facilitated urban transportation. However, the balance of such system is often broken by the user behaviors. And how to manage a large number of bikes which parked randomly in a city is a difficult problem. To tackle this problem, we propose a two-step solution. First, we deal with the bike trajectory data and design the Spatial-Temporal Bike Flow Prediction (ST-BFP) model, which is a convolutional network based on residual framework with history external factors to predict the bike flows. Second, to make the system return to balance state as soon as possible, we propose an Improved Local Search Algorithm (ILSA) for path planning with multiple carriers based on forecast result, which schedules multiple carriers in real time to complete the rebalance task collaboratively. Finally, we validate our model and algorithm via realdata based experiment. Experimental results demonstrate that our method can balance the entire system efficiently.