Optimized Public Parking Location Modelling for Green Intelligent Transportation System Using Genetic Algorithms

• Main Authors: Tong Shen, Kun Hua, Jiaping Liu
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Parking allocation model; congestion management; green intelligent transportation system; genetic algorithm; optimization
• Online Access: https://ieeexplore.ieee.org/document/8924626/

This paper proposes an optimal parking site selection scheme to alleviate CO₂ emissions of the traffic flows for green urban road networks. Through the creative dynamic traffic zone programming, a constrained optimization model is set up to assess the impact of potential public parking locations on road traffic emissions. In each scenario, Thiessen Polygon based zoning method is applied to investigate the distributions of road traffics. The main contribution of this study is as follows. Firstly, this proposed model takes the CO₂ emission of the whole traffic network of sustainable city development as the optimization goal, instead of the traditionally discussed travel distance or cost efficiency. Secondly, a Thiessen polygon based public parking zoning method is developed and implemented realistically. This zoning method provides a precise approach to traffic distribution and parking demand estimation. Rather than the quadrilateral or radial zoning, this method pays more attention to the parking supply demand and its impact on parking congestion. Thirdly, the genetic algorithm (GA) is used to find the optimal public parking location (PPL) sets. GA has a great application value in speeding up stochastic search for global optimization. It is especially suitable to simulate complex and large capacity problems concerning the realistic solutions. By implementing the dynamic zoning and modelling method into intelligent transportation system (ITS), the efficiency of parking induction and dynamic optimization of traffic distribution could be ensured for the future smart mobility. Therefore, this model not only serves as a novel method for public parking allocations, but hold potential to support intelligent parking guidance, as a part of the intelligent traffic system for smart city development.