Smart Parking and Urban Space Optimization in Mixed-Use Estates: Design, Implementation, Evaluation

Thesis Abstract

In rapidly urbanizing cities, mixed-use estates face acute parking shortages, inefficient vehicle circulation, and underutilized street frontage, which together erode liveability, increase congestion, and elevate emissions. This study addresses the problem by designing, implementing, and evaluating an integrated smart parking and urban space optimization framework that leverages sensor-enabled parking guidance, dynamic pricing, and modular street furniture to reclaim underutilized curb spaces for pedestrians and micro-activities. The aim is to demonstrate that a data-driven, context-aware system can reduce parking search times, improve turnover rates of parking bays, and enhance the perceived quality of public space within mixed-use estates. Specific objectives include (1) to map current parking demand-supply imbalances and spatial utilization patterns; (2) to design a technology-enabled parking management system incorporating IoT sensors, computer vision, and a decision-support dashboard aligned with local zoning and safety regulations; (3) to implement a pilot in a representative mixed-use estate comprising 1,200 residential units, 180,000 m2 of commercial space, and 45,000 daily vehicle trips; (4) to evaluate impacts on parking occupancy, average search time, and pedestrian space usability through pre- and post-implementation measurements; and (5) to develop guidelines for scalable deployment and policy considerations. The study adopts a pragmatist research design drawing on action research and design science paradigms, guided by the ecological validity of mixed-use environments and informed by the Theory of Planned Behavior to interpret user acceptance and the Technology-Organization-Environment (TOE) framework to assess adoption drivers. The population includes estate residents, tenants, visitors, facility managers, and municipal inspectors. A stratified random sample of 400 residents, 60 business tenants, and 20 facility managers will be surveyed, complemented by 120 in-depth interviews with key informants and 200 hours of behavioral observation. Data collection instruments comprise (i) a redesigned parking occupancy sensor network and mobile app analytics; (ii) structured questionnaires capturing attitudes toward smart parking, perceived usability, and behavioral intentions; (iii) semi-structured interview protocols; and (iv) observational checklists for curb-space usage and pedestrian flow. Validity and reliability will be ensured through pilot testing, content validity by expert review, and Cronbach’s alpha assessment for multi-item scales. Quantitative data will be analyzed using regression models to identify determinants of parking search time and occupancy rates, interrupted time-series analysis to detect changes across pre- and post-implementation phases, and ANOVA to compare spatial zones. Spatial analytics will employ GIS-based hotspot and space-usage metrics, while multivariate clustering will classify micro-areas by performance. Qualitative data will be analyzed thematically with a coding framework anchored in the Theory of Planned Behavior and the socio-technical perspective, with triangulation across data sources. A conceptual model integrating IoT-driven sensing, dynamic pricing, user behavior, and urban space revitalization will be iteratively refined through design science cycles and validated by stakeholder workshops. Expected findings include a statistically significant reduction in average parking search time by 35–50%, increased occupancy turnover by 15–25%, and measurable improvements in pedestrian space utilization and perceived safety, particularly in peak hours. The study anticipates differential effects across land-use zones, with greater benefits where curb space is flexible and existing pedestrian infrastructure is robust. The contribution to knowledge lies in delivering a transferable, empirically validated framework for smart parking and space optimization in mixed-use estates, bridging design theory with implementation realities, and providing a decision-support toolkit for urban managers. The research will inform policy prescriptions on curb-space governance, dynamic pricing, data governance, and retrofit guidelines for existing estates, as well as guidelines for scalable replication in comparable urban contexts. The main conclusion is that an integrated, data-driven system that aligns technical components with human factors and regulatory constraints can substantially improve parking efficiency and urban space usability in mixed-use environments, while maintaining safety and inclusivity. Recommendations include adopting phased rollout with continuous monitoring, establishing privacy-by-design data protocols, integrating multimodal transport incentives, and prioritizing flexible curb-space designs that accommodate evolving urban activities.