PARKING OCCUPANCY PREDICTION AND PATTERN ANALYSIS

INFORMTION TACHNOLOGY

The management of parking and urban mobility now heavily relies on parking occupancy forecast and pattern analysis. In order to optimize parking resource allocation, lessen traffic congestion, and improve the overall parking experience in urban areas, this research investigates the application of data analytics, machine learning, and real-time information systems. Due to rising population and automobile ownership, managing parking resources in urban settings has become a crucial concern. Congested parking lots, protracted searches for open spaces, and the ensuing traffic congestion are becoming routine urban annoyances. In response, this study conducts a thorough review of parking occupancy prediction and pattern analysis, providing a thorough examination of their use, methods, and effects. The research starts by doing a thorough assessment of the prior literature, exploring the major approaches, conclusions, and trends in the field of pattern analysis and parking occupancy prediction. This evaluation of the literature identifies knowledge gaps and sets the basis for further investigation. The first step of the study is to analyze the prior research and pinpoint the major trends and knowledge gaps. After that, it concentrates on creating parking occupancy predicting models, using historical data and current updates to produce precise projections. These models are included into smart parking systems that provide drivers with up-to-the-minute parking availability data via mobile applications and digital signs. In order to promote the best parking turnover and income creation, dynamic pricing solutions are investigated. The study also explores how parking management affects decisions about urban design, traffic flow, and sustainable mobility programs. Along with suggestions for future study, challenges relating to data privacy, system integration, and adjusting to dynamic urban changes are highlighted. The results show how parking occupancy prediction and pattern analysis may be used to design smarter, more efficient, and sustainable urban settings that will benefit both locals and tourists as well as the environment.

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