Competitive spatial pricing for urban parking systems: Network structures and asymmetric information

Abstract

Inspired by new technologies to monitor parking occupancy and process market signals, we aim to expand the application of demand-responsive pricing in the parking industry. Based on a graphical Hotelling model wherein each garage has information for its incoming parking demand, we consider a general competitive spatial pricing in parking systems under an asymmetric information structure. We focus on the impact of urban network structure on the incentive of information sharing. Our analyses suggest that the garages are always better off in a circular-networked city, while they could be worse off in the suburbs of a star-networked city. Nevertheless, the overall revenue for garages is improved and the aggregate congestion is reduced under information sharing. Our results also suggest that information sharing helps garages further exploit the customers who in turn become worse-off. Therefore, policy-makers should carefully evaluate their transportation data policy since impacts on the service-providers and the customers are typically conflicting. Using the SFpark data, we empirically confirmed the value of information sharing. In particular, garages with higher price-demand elasticity and lower demand variance tend to enjoy larger benefits via information sharing. These insights support the joint design of parking rates structure and information systems.

Keywords:

• Smart parking
• information systems design
• Bertrand competition
• game with incomplete information

Acknowledgments

The authors gratefully acknowledge the valuable comments from the editors and three anonymous reviewers that helped improve the paper.

Additional information

Funding

This research is partially supported by the U.S. National Science Foundation through Grants CNS# 1637772, National Natural Science Foundation of China through Grants 72071101, Hong Kong Research Grants Council (RGC) for General Research Fund 11215119, and Shenzhen Natural Science Fund (Grant No. GXWD20201230110313001, Program Contract No. 20200925160442005).

Notes on contributors

Yuguang Wu

Yuguang Wu is a Ph.D. candidate in the Department of Industrial and Systems Engineering, University of Wisconsin-Madison (UW-Madison). He obtained the M.S. degree in Computer Science at UW-Madison (2020) and the B.S. degree in mathematics and physics at Tsinghua University, China (2016).

Qiao-Chu He

Qiao-Chu He received a B.E. degree from Tsinghua University, Beijing, in 2011, and a Ph.D. degree in Operations Research from the University of California, Berkeley, in 2016. He worked as an Assistant Professor at the University of North Carolina at Charlotte from 2016 to 2019 and was Visiting Assistant Professor at Hong Kong University of Science and Technology. He joined the Southern University of Science and Technology in 2019, where he is currently an Associate Professor. His research interests include operations research in smart-cities applications.

Xin Wang

Xin Wang received a Ph.D. degree in Civil and Environmental Engineering from the University of Illinois at Urbana-Champaign (UIUC) in 2015. He also obtained the M.S. degree in Applied Math (2012) and Civil and Environmental Engineering in UIUC (2014), the B.S. degree in Automation Engineering in Tsinghua University, China (2010). He is currently an assistant professor in the Department of Industrial and System Engineering, University of Wisconsin-Madison, and also affiliated with the Department of Civil and Environmental Engineering, Grainger Institute for Engineering.