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

Yuguang Wu, Qiao-Chu He and Xin Wang

IISE Transactions, 2021, vol. 54, issue 2, 186-197

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.

Date: 2021
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DOI: 10.1080/24725854.2021.1937755

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