Improving Service Level of Free-Floating Bike Sharing Systems

• Main Author: Pal, Aritra
• Format: Others
• Published: Scholar Commons 2017
• Subjects: Bike Sharing; Last Mile; Mobility Patterns; Rebalancing; Anomaly Detection; Operational Research; Statistics and Probability; Urban Studies and Planning
• Online Access: https://scholarcommons.usf.edu/etd/7433; https://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=8630&context=etd

Bike Sharing is a sustainable mode of urban mobility, not only for regular commuters but also for casual users and tourists. Free-floating bike sharing (FFBS) is an innovative bike sharing model, which saves on start-up cost, prevents bike theft, and offers significant opportunities for smart management by tracking bikes in real-time with built-in GPS. Efficient management of a FFBS requires: 1) analyzing its mobility patterns and spatio-temporal imbalance of supply and demand of bikes, 2) developing strategies to mitigate such imbalances, and 3) understanding the causes of a bike getting damaged and developing strategies to minimize them. All of these operational management problems are successfully addressed in this dissertation, using tools from Operations Research, Statistical and Machine Learning and using Share-A-Bull Bike FFBS and Divvy station-based bike sharing system as case studies.