Parking space optimization using monte carlo simulation: case study at the University of Moratuwa

Abstract

With over 8.3 million automobiles in Sri Lanka as of 2022, the dominance of private vehicles in urban transportation has led to a marked increase in parking demand, frequently surpassing the available supply. This challenge is particularly pronounced within university settings, where the influx of students, lecturers, and staff often overwhelms the existing parking infrastructure. The University of Moratuwa is a representative case for studying parking optimization strategies, making it an ideal site for this research. This study utilizes Monte Carlo simulations to identify the optimal parking angle along a narrow, one-way road within the campus. By systematically evaluating various parking angles while accounting for constraints such as road width, vehicle dimensions, and necessary driving space, the research identifies parallel parking at 0 degrees as the most efficient configuration, accommodating the maximum number of vehicles. The findings provide a robust, data-driven approach to enhancing parking efficiency, with broader implications for urban traffic management and space utilization in constrained environments. Additionally, the study highlights the potential for integrating advanced simulation techniques into more complex parking scenarios, offering innovative and inspired solutions to the challenges of urban parking.