Dynamic Modeling for Parking Allocation Problem

Abstract

Parking problems have become one of the most pressing issues in urban transportation planning and management. In many cities throughout the world, they are a well-known cause of traffic congestion and significant economic and environmental losses. This thesis investigates the solution techniques from an operational research viewpoint and provides practical solutions using mathematical programming and heuristics in an effort to mitigate the problem’s consequences. We initially presented and studied a number of time-dependent models for assigning parking spaces to vehicles, with the aim of reducing total traveling time. In addition, we provided an equivalent network-based formulation for the problem and a greedy heuristic algorithm that allocates cars according to their respective arrival timings. Extensive experiments using randomly generated instances were carried out to evaluate the performance and effectiveness of models under various conditions. In addition, we proposed a flexible framework that captures the dynamic character of the problem, accounts for cars’ duration of stay, track vehicle requests, and adjusts the availability of parking spaces for future allocation appropriately. Our method has been validated by simulation on top of real-world parking data.

Date of Award	Dec 2022
Original language	American English
Supervisor	Andrei Sleptchenko (Supervisor)