DYNAMIC VEHICLE PARKING PRICING, PREDICTION OF DEMAND AND OPTIMAL SELECTION OF PARKING LOTS

Abstract

Urban traffic congestion is becoming a major issue, resulting in prolonged travel times, air pollution, fuel consumption, and driver resentment, which is frequently created by vehicles searching for vacant parking spaces. In the development of a parking manage ment system, the optimization of parking pricing, demand, and supply throughout the course of a day is a significant issue. Dynamic vehicle parking pricing has emerged as a promising strategy to manage parking demand, minimize cruising time, and maxi mize revenue and enhancing traffic flow. This study employs competitive game theory among parking agents to formulate a new dynamic parking pricing model. Bi-level op timization problem is developed to regulate traffic flow and maximize profit for parking agents. Evolutionary algorithm is used to solve the problem. Extensive numerical sim ulations are executed using randomly generated data to evaluate the optimal pricing strategies and maximization of profit for parking agents. Predicting parking demand is also the focus of this study. Time varying discrete non-homogeneous Markov chain model is used to predict demand. An adaptive learning algorithm is proposed to enable the non-homogeneous Markov chain to respond effectively to changes in the dynamic demand environment. Case study has been carried out based on the proposed al gorithm. The result derived from data predictions, along with the integration of an adaptive strategy, is presented to enable the system to learn from new changes. Sensi tivity analysis is performed to assess the impact of learning parameters on prediction accuracy. Optimal parking lot choice strategy is also further investigated in this study. The total travel time and the expected cost of parking lots are considered as conflict ing objectives for the decision-maker. Bi-criteria optimization is used to formulate the parking lot choice problem. The Pareto optimal Parking lot is identified by applying ideal point method. We conduct numerical simulations and apparently generate data to show the feasibility of the proposed algorithm.