Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{202414,
        author = {Alphanette Gemu},
        title = {Artificial Intelligence and Its Impact on On-Street Parking Management: A Case Study of Gweru City Parking, Zimbabwe},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {8864-8878},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202414},
        abstract = {Urban parking management systems worldwide face increasing pressure from rising vehicle ownership and limited infrastructure capacity, challenges that are particularly acute in developing African cities. This mixed-methods study investigated how Artificial Intelligence (AI) can impact and improve on-street parking management in Gweru City, Zimbabwe. Adopting the Technology Acceptance Model as a theoretical framework, the research employed a pragmatic paradigm to examine current parking performance, international AI applications, implementation barriers, and potential enhancement strategies. Data collection involved 217 participants across four stakeholder groups: parking management officials (n=9), on-street parking users (n=175), local business owners (n=27), and technology experts (n=6). Quantitative data captured parking utilization patterns, revenue collection efficiency, and traffic congestion metrics, while qualitative data explored stakeholder perceptions and organizational readiness through interviews, focus groups, and observational analysis. Findings revealed significant operational inefficiencies in Gweru's current parking system, with average occupancy rates of 87% during peak hours but only 34% during off-peak periods. Revenue collection efficiency stood at 62%, while 43% of vehicles spent 15-25 minutes searching for parking spaces during busy periods. User satisfaction surveys indicated widespread frustration with existing manual enforcement systems, while business owners reported customer avoidance due to parking difficulties. Technical feasibility assessment demonstrated moderate infrastructure readiness for AI implementation, with 45% of parking zones having adequate power supply and 67% possessing sufficient connectivity for sensor installation. Cost-benefit analysis projected a $2.3 million initial investment with a 4.5-year break-even period through improved revenue collection and reduced traffic congestion. Environmental modelling suggested a potential 23% reduction in carbon emissions from decreased vehicle search times. Implementation barriers included organizational capacity constraints, financial limitations, and employment security concerns among current parking attendants. However, 68% of users expressed enthusiasm for technological improvements, indicating strong acceptance potential. Expert interviews revealed mixed perspectives on technical capacity requirements and maintenance sustainability. The study concludes that AI-enhanced parking management offers substantial potential for addressing Gweru's parking challenges, with favourable user acceptance and moderate technical feasibility. However, successful implementation requires phased deployment strategies, comprehensive staff training, and inclusive access mechanisms to address digital divide concerns. Recommendations include establishing pilot projects in high-traffic zones, developing employment transition programs for existing staff, and creating stakeholder engagement platforms to ensure system responsiveness to local needs. This research contributes to the limited literature on AI parking implementation in Sub-Saharan African contexts and provides practical guidance for similar developing city initiatives.},
        keywords = {Artificial Intelligence, on-street parking, Gweru City Parking, Smart City, Zimbabwe, Urban Mobility},
        month = {May},
        }