A holistic approach for modeling and predicting bike demand. Issue 111 (January 2023)

• Record Type: Journal Article
• Authors: Tomaras, Dimitrios; Boutsis, Ioannis; Kalogeraki, Vana
• Is Part Of: Information systems. Issue 111(2023)
• Publication Date: 2023-01
• Subjects: Bike sharing systems -- Rebalancing -- Modeling -- Prediction; Database management -- Periodicals; Electronic data processing -- Periodicals; Bases de données -- Gestion -- Périodiques; Informatique -- Périodiques; Database management; Electronic data processing; Periodicals; 005.7
• DOI: 10.1016/j.is.2022.102129
• Languages: English
• ISSNs: 0306-4379

Abstract

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 various factors including topography, location of dockings, hours of service, safety and security, weather, rush hours or even during the occurrence of major events around the city. Such imbalances often result in shortage of bikes or bike parking racks. An unbalanced bike system indicates an unreliable form of transportation and disappointed users. Current studies 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 real-time rebalancing, during the event, to ensure seamless operation. In this work we solve the bike rebalancing problem while considering fluctuating demand that leads to an imbalance between supply and demand. We present "SmartBIKER", a holistic and cost-effective framework for bike sharing systems addressing both normal operation and operation during major city events. SmartBIKER models bike demand during both normal operation and major events, identifies bike stations with low or high demand using two different forecasting models and determines a relocation strategy that maximizes the utility of the stations while minimizing the relocation cost. Our experimental evaluation shows that our approach is practical, efficient and outperforms state-of-the-art relocation and prediction schemes. Highlights: Handling unexpected bike demand during large city situations. Modeling bike demand during casual and regular days. Prediction during regular days using Gradient Boosted Regression Trees. Prediction during large city situations incorporating Holtz's trend forecasting technique. A polynomial algorithm for efficient bike relocation.