Understanding the Sustainability of Bike Sharing Systems: A Tale of Eight Cities
Zhaoyu Kou
Purdue University
I am a PhD student in the School of Industrial Engineering at Purdue University, specializing in the field of Operations Research. My current research interest focuses on the optimization of sustainable multi-modal urban transportation systems and the understanding of urban travel patterns. I received a Master of Science degree in Earth and Environmental Engineering from Columbia University in 2016 and a Bachelor of Science degree in Environmental Engineering from Tsinghua University (Beijing, China) in 2014.
Abstract
Bike sharing is an emerging mode of transportation in urban systems. It provides convenient mobility to short-distance travelers both for sightseeing purposes and for making connections between other transportation modes. As... [ view full abstract ]
Bike sharing is an emerging mode of transportation in urban systems. It provides convenient mobility to short-distance travelers both for sightseeing purposes and for making connections between other transportation modes. As one type of “active travel”, biking not only contributes to the sustainability of urban transportation by reducing traffic congestion and emissions, but also help improve human health. Many cities are rolling out bike sharing programs. However, few studies have evaluated how bike sharing systems are used and their sustainability implications. Understanding the travel patterns of bike trips using bike sharing programs not only can improve the efficiency of existing system and inform development of future programs but also help better integrate bike sharing with public transportation infrastructure to build a multi-modal transportation system.
This study aims to analyze and compare real-world trip data collected from the bike sharing systems in eight cities in the United States, including New York, Chicago, Boston, Philadelphia, Washington D.C., Los Angeles, San Francisco, and Seattle. We apply data mining methods such as clustering, linear regression, network analysis, and geo-visualization to evaluate the spatiotemporal characteristics of bike sharing use. The key factors include trip distance distribution, travel time, bike sharing use behaviors over time, distance between bike docking stations and other public transport stations, trip footprint, and trip demands in different areas. Bike sharing use patterns across different cities are also compared in consideration of urban structure and demographic distribution. To the best of our knowledge, this is the first study comparing bike sharing use in multiple cities using real-world trip data.
Authors
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Zhaoyu Kou
(Purdue University)
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Hua Cai
(Purdue University)
Topic Areas
• Open source data, big data, data mining and industrial ecology , • Sustainable urban systems
Session
ThS-20 » International comparisons of sustainable systems (13:45 - Thursday, 29th June, Room E)
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