Development of a parking system based on learning automata

Rapid growth of urban population and unplanned urbanization are reducing the number of urban parking spaces and increase traffic congestion. In China, due to the tripling of the number of motor vehicles in the last decade, the problem of finding a parking space has become very pressing. Increased difficulties are observed with short-term parking near public urban locations such as cinemas, shopping centers, universities and hospitals. In the telecommunications industry and other related fields, learning automata have been widely employed as adaptable devices for making decisions in unknowable settings. Their use in choosing a parking spot for a little time in cities hasn't been properly researched, nevertheless. The usage of learning automata in the created LAPS (Learning Automata Parking System) parking algorithm is discussed in this study in contrast to other algorithms. More specifically, the study will gain insight into the impact of factors such as vehicle count, space between vehicle arrivals in the parking lot, time of day, and the parking area that can be used for reservations on the average waiting time for a parking space and the likelihood of finding one. The paper presents the research findings determining the parking system's effectiveness based on learning automata in the urban areas of China. The experimental part of the study was performed using Arena simulation software on the parking lot model, with the maximum parking time under simulation conditions not exceeding 4 h. The paper evaluated such parking efficiency metrics as the average parking space waiting time and the probability of getting one, measuring the influence on these indicators on such factors as the number of motor vehicles, interval between the arrival of vehicles in the parking lot, time of day, as well as the parking area that can be used for reservations. The research findings suggested that the LAPS algorithm can be recommended for development of smart packing systems software in urban areas in China and other countries.