Data-Driven Spatial Zoning and Differential Pricing for Large Commercial Complex Parking

This study presents a data-driven framework for optimizing parking space allocation and pricing in large commercial complexes, addressing persistent spatial imbalances in occupancy between high- and low-demand zones. A mixed Logit (ML) model with interaction terms is estimated from stated preference survey data to capture heterogeneous user preferences across trip purposes. A dual clustering algorithm is then applied to generate spatially coherent pricing zones, integrating geometric, functional, and occupancy-based attributes. Two differential pricing strategies are formulated: an administered model with regulatory price bounds and a market-based model without such constraints. Both pricing models are solved using an improved multi-objective Particle Swarm Optimization–Grey Wolf Optimizer (PSO–GWO) algorithm that jointly optimizes spatial zoning and zone–time pricing schedules. Using data from the Kingmo Complex in Nanjing, China, the results show that both strategies significantly reduce spatio-temporal occupancy variance and improve utilization balance. The administered strategy reduces variance by up to 67% on weekdays, with only a 1% increase in revenue, making it suitable for contexts prioritizing regulatory compliance and price stability. In contrast, the market-based strategy reduces variance by over 40% while generating substantially higher revenue, particularly during periods of high and uneven demand. The proposed framework demonstrates the potential of integrating behavioral modeling, spatial clustering, and multi-objective optimization to improve parking efficiency. The findings provide practical guidance for operators and policymakers seeking to implement adaptive pricing strategies in large-scale parking facilities.