ISSN: 2226-3624
Open access
Rapid urban growth and rising vehicle ownership have worsened parking inefficiencies in smart city environments, highlighting the importance of effective parking management for urban mobility, operational sustainability, and service quality. While adoption of smart parking technology increases, many companies still face fragmented systems, manual processes, limited real-time data, and weak predictive capabilities. This research examines how phased integration of cloud computing and AI can improve operational efficiency in a real organization. Conducted through action research at Shenzhen Ai Ke Technology, a smart parking provider transitioning from traditional to digital management, the diagnosis identified outdated infrastructure and manual workflows as major inefficiencies. Two intervention cycles were implemented: Cycle 1 introduced cloud solutions for data centralization, better reporting, and enhanced real-time visibility; Cycle 2 incorporated AI to improve demand forecasting, reduce errors, and support decision-making. Data collected from observation, interviews, surveys, and open responses from 11 staff members across departments showed positive feedback for both interventions. Results indicated significant improvements in operational reliability, accuracy, resource utilization, response time, and overall efficiency. Qualitative insights highlighted benefits such as real-time monitoring, streamlined operations, and increased competitiveness, while also revealing the need for ongoing improvements in usability, training, staffing, and advanced AI features. This research advances knowledge in smart parking and digital transformation by demonstrating that cloud and AI deliver maximum value when implemented gradually and in a complementary manner. It offers practical guidance for parking managers and smart city stakeholders seeking to enhance operations through scalable, intelligent system integration.
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