Research on intelligent city parking guidance method based on ant colony algorithm. (7th March 2022)

• Record Type: Journal Article
• Authors: Gao, Feng; Chen, Aifeng
• Is Part Of: International journal of innovative computing and applications. Volume 13:Number 1(2022)
• Page Start: 1
• Page End: 10
• Publication Date: 2022-03-07
• Subjects: ant colony algorithm -- ACO -- simulation -- parking -- intelligent guidance; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

In order to get the most satisfactory parking space at the fastest speed, an intelligent urban parking guidance method based on ant colony algorithm is proposed. The main factors affecting the selection of parking spaces in parking lots are analysed, including walking distance, driving distance, walking time, driving time and so on. Each factor is set as multiple attributes of berth, and the optimal berth selection model of smart city is established. Ant colony algorithm is used to solve the model, obtain the optimal parking space, and realise intelligent guidance of intelligent city parking. The simulation results show that the proposed method is feasible and effective.