Neural-Network-Based Dynamic Distribution Model of Parking Space Under Sharing and Non-Sharing Modes

Abstract

In recent years, with the rapid development of China’s automobile industry, the number of vehicles in China has been increasing steadily. Vehicles represent a convenient mode of travel, but the growth rate of the number of urban motor vehicles far exceeds the construction rate of parking facilities. The continuous improvement of parking allocation methods has always been key for ensuring sustainable city management. Thus, developing an efficient and dynamic parking distribution algorithm will be an important breakthrough to alleviate the urban parking shortage problem. However, the existing parking distribution models do not adequately consider the influence of real-time changes in parking demand and supply on parking space assignment. Therefore, this study proposed a method for dynamic parking allocation using parking demand predictions and a predictive control method. A neural-network-based dynamic parking distribution model was developed considering seven influencing factors: driving duration, walking distance, parking fee, traffic congestion, possibility of finding a parking space in the target parking lot and adjacent parking lot, and parking satisfaction degree. Considering whether the parking spaces in the targeted parking lots are shared or not, two allocation modes—sharing mode and non-sharing mode—were proposed and embedded into the model. At the experimental stage, a simulation case and a real-time case were performed to evaluate the developed models. The experimental results show that the dynamic parking distribution model based on neural networks can not only allocate parking spaces in real time but also improve the utilisation rate of different types of parking spaces. The performance score of the dynamic parking distribution model for a time interval of 2–20 min was maintained above 80%. In addition, the distribution performance of the sharing mode was better than that of the non-sharing mode and contributed to a better overall effectiveness. This model can effectively improve the utilisation rate of resources and the uniformity of distribution and can reduce the failure rate of parking; thus, it significantly contributes to more smart and sustainable urban parking management.

Optimization of On-Street Parking Charges Based on Price Elasticity of the Expected Perceived Parking Cost

“…However, those methods have failed to distinguish the different types of users [9], which cause the failure of meeting the main objective of providing the on-street parking space, namely, providing service for the short-term parking users. According to the previous researches, parking time, walking distance and satisfaction degree of parking behavior of travelers have significant guidance for the formulation of parking price [23], and it is argued that the pricing should be charged according to the sensitivity differences in various parking time. In order to achieve the purpose of providing parking spaces to short-term parking, many cities have adopted a progressive charging mechanism: the parking time is divided into several stages with considerable price rising for longer parking period.…”