Space-time demand cube for spatial-temporal coverage optimization model of shared bicycle system: A study using big bike GPS data. (October 2020)

• Record Type: Journal Article
• Authors: Yang, Lin; Zhang, Fayong; Kwan, Mei-Po; Wang, Ke; Zuo, Zejun; Xia, Shaotian; Zhang, Zhiyong; Zhao, Xinpei
• Is Part Of: Journal of transport geography. Volume 88(2020)
• Publication Date: 2020-10
• Subjects: Bike-sharing systems -- Location optimization -- Spatiotemporal dynamics -- Space-time demand cube -- Spatial-temporal coverage -- Genetic algorithms; Transportation -- Periodicals; Telecommunication -- Periodicals; Transport -- Périodiques; Télécommunications -- Périodiques; Telecommunication; Transportation; Periodicals; 388
• DOI: 10.1016/j.jtrangeo.2020.102861
• Languages: English
• ISSNs: 0966-6923

Abstract

Abstract: As a sustainable transport mode, bicycle sharing is increasingly popular and the number of bike-sharing services has grown significantly worldwide in recent years. The locational configuration of bike-sharing stations is a basic issue and an accurate assessment of demand for service is a fundamental element in location modeling. However, demand in conventional location-based models is often treated as temporally invariant or originated from spatially fixed population centers. The neglect of the temporal and spatial dynamics in current demand representations may lead to considerable discrepancies between actual and modeled demand, which may in turn lead to solutions that are far from optimal. Bike demand distribution varies in space and time in a highly complex manner due to the complexity of urban travel. To generate better results, this study proposed a space-time demand cube framework to represent and capture the fine-grained spatiotemporal variations in bike demand using a large shared bicycle GPS dataset in the "China Optics Valley" in Wuhan, China. Then, a more spatially and temporally accurate coverage model that maximizes the space-time demand coverage and minimizes the distance between riders and bike stations is built for facilitating bike stations location optimization. The results show that the space-time demand cube framework can finely represent the spatiotemporal dynamics of user demand. Compared with conventional models, the proposed model can better cover the dynamic needs of users and yields 'better' configuration in meeting real-world bike riding needs.