Mathematical modeling and heuristic approaches to optimize shared parking resources: A case study of Beijing, China

• Main Authors: Pengfei Zhao, Hongzhi Guan, Heng Wei, Shixu Liu
• Format: Article
• Language: English
• Published: Elsevier 2021-03-01
• Series: Transportation Research Interdisciplinary Perspectives
• Subjects: Resource optimization; Heuristic algorithm; Shared parking space allocation; Dual bin-packing
• Online Access: http://www.sciencedirect.com/science/article/pii/S2590198221000245

Sharing unused private parking spaces with public travelers can improve parking resource utilization. However, a challenge lies in the approach to maximize the utilization of such parking resources without time colliding with the needs of these resources between public travelers and owners over time. To address this challenge, this study presents a methodology aiming at efficiently obtaining the optimum matches between parking supply and demand with time-window constraints. First, a framework for shared parking is proposed to guide the development of the computation algorithms. Based on this framework, a mixed-integer nonlinear programming model and its solving algorithms are then developed to maximize the spatial-temporal utilization. Further, the feasibility and validity of the proposed model and algorithms are tested by empirical data collected in our real-world. The results indicate that the non-increased demand durations and non-descending supply durations (NI-ND) heuristic performs the best under both small- and large-scale supply and demand settings with extremely high solution efficiency. Moreover, the solutions based on the NI-ND algorithm are very close to the optimal objective, implying that the proposed shared parking allocation method based on the dual bin-packing principle is an effective way to address the realistic supply–demand relationship of parking resources under time-window constraints. Finally, the potential of shared parking is estimated based on the statistics of the number of private parking spaces and parking behavior data in Beijing.