Modeling and Predicting Bike Demand in Large City Situations

Abstract

Bike-sharing systems have enjoyed tremendous success in many major cities around the world today as a new means of urban public transportation offering a green and facile solution for daily commuters and tourists. One common problem featured in these systems is that the distribution of bikes among stations can be quite uneven, due to topography, rush hours or during the occurrence of major events around the city. This often results in shortage of bikes or bike parking racks. An unbalanced bike system means an unreliable form of transportation and disappointed users. Existing works in the literature are limited as they are not designed to handle fluctuating, high or unpredictable demand during large city events that typically affect multiple stations and require rebalancing in real-time, during the event, to ensure seamless operation. In this work, we present “SmartBIKER”, a cost-effective framework for bike sharing systems focusing on major city events. SmartBIKER models bike demand trends during major events, identifies bike stations with low or high demand using a trend forecasting model and determines a relocation strategy that minimizes the relocation cost while maximizing the utility of the stations. Our experimental evaluation shows that our approach is practical, efficient and outperforms state-of-the-art relocation and prediction schemes.