How could the station-based bike sharing system and the free-floating bike sharing system be coordinated? |
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| Institution: | 1. Department of Industrial and Management Systems Engineering, University of South Florida, United States;2. Department of Civil and Environmental Engineering, University of South Florida, United States;3. College of Transportation Engineering, Tongji University, China;1. Zhou Enlai School of Government, Nankai University, 300350, Tianjin, China;2. The Bartlett Centre for Advanced Spatial Analysis, University College London, 90 Tottenham Court Road, W1T 4TJ, London, UK;3. Experimental Teaching Centre of Applied Social Science, Nankai University, 300350, Tianjin, China;4. Chair of Cartography, Technical University of Munich, Arcisstraße 21, 80333, Munich, Germany;5. The Bartlett School of Construction and Project Management, University College London, WC1E 7HB, London, UK;1. Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai, China;2. Urban Planning Group, Eindhoven University of Technology, Eindhoven, the Netherlands;1. Jiangsu Key Laboratory of Urban ITS, Southeast University, Dongnandaxue Road #2, Nanjing, 211189, China;2. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Dongnandaxue Road #2, Nanjing, 211189, China;3. School of Transportation, Southeast University, Dongnandaxue Road #2, Nanjing, 211189, China;4. Department of Geography, Ghent University, Krijgslaan 281 S8, Ghent, 9000, Belgium |
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| Abstract: | The station-based bike sharing system (SBBSS) and the free-floating bike sharing system (FFBSS) have been adopted on a large scale in China. However, the overlap between the services provided by these two systems often makes bike sharing inefficient. By comparing the factors that affect the usage of the two systems, this paper aims to propose appropriate strategies to promote their coordinated development. Using data collected in Nanjing, a predictive model is built to determine which system is more suitable at a given location. The influences of infrastructure, demand distribution, and land use attributes at the station level are examined via the support vector machine (SVM) approach. Our results show that the SBBSS tends to be favored in areas where there is a high concentration of travel demand, and close proximity to metro stations and commercial properties, whereas locations with a higher density of major roads and residential properties are associated with more frequent use of the FFBSS. With regard to the methods used, a comparison of several machine learning approaches shows that the SVM has the best predictive performance. Our findings could be used to help policy makers and transportation planners to optimize the deployment and redistribution of docked and dockless bikes. |
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