Title: Research on intelligent city parking guidance method based on ant colony algorithm

Authors: Feng Gao; Aifeng Chen

Addresses: School of Artificial Intelligence, Chongqing University of Arts and Sciences, Chongqing 402160, China; Lyceum of the Philippines University, Batangas Campus, Batangas City, Philippines ' College of Computer Science, Sichuan University, Chengdu 610065, China

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.

Keywords: ant colony algorithm; ACO; simulation; parking; intelligent guidance.

DOI: 10.1504/IJICA.2022.121383

International Journal of Innovative Computing and Applications, 2022 Vol.13 No.1, pp.1 - 10

Received: 20 Dec 2019
Accepted: 21 Apr 2020
Published online: 10 Mar 2022 *