Comprehensive Review of Smart Parking Occupancy Prediction Models in Nairobi City: Strengths, Weaknesses, and Research Gaps

An in-depth analysis of smart parking occupancy prediction models in contemporary cities is presented in this research. The paper identifies key research gaps while methodically analyzing the strengths, flaws, and resilience of existing models. Priority was given to the models’ precision and efficacy in addressing the city's growing parking issues. An extensive analysis of these models demonstrated that they help to manage to park effectively because of their properties including real-time data integration and great forecast accuracy. On the other hand, several restrictions and flaws have been found, including issues with data accessibility, a lack of generalizability, and the complexity of certain advanced models. These findings emphasized the value of creative and situation-specific responses. The findings demonstrated the urgent need for further study, notably in the fields of data integration, scalability, interpretability, cost-effectiveness, and user-centered methods for smart parking models. These flaws are now being addressed to build a comprehensive smart parking system that is tailored to the particular urban dynamics of Nairobi. They also provide a framework for future study. Our project's ultimate goal is to significantly improve Nairobi's urban mobility and parking management, and machine learning will be a key instrument in this transformation process.