Multi-agent simulation analysis method for operation optimization of urban parking system

Through multi-agent simulation analysis methods and Binomial Logit models, combined with simulated annealing algorithms, a parking lot micro-simulation platform was constructed, which solved the problem of insufficient research on driver behavior in traditional methods and improved the operating efficiency of parking lots and the scientific nature of resource allocation.

CN120633895APending Publication Date: 2025-09-12TONGJI UNIV
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Patent Information

Application Number
CN202510333228.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-03-20
Publication Date
2025-09-12

AI Technical Summary

Technical Problem

Existing technologies lack in-depth research on individual drivers' behavior in parking lot design and management, resulting in inefficient resource utilization, poor user experience, and traffic congestion. Traditional methods cannot accurately reflect the driver's dynamic decision-making process and lack efficient management strategy evaluation tools.

Method used

Through multi-agent simulation analysis method, combined with Binomial Logit discrete choice model and simulated annealing algorithm, a parking lot micro-simulation platform is constructed to analyze the influencing factors of drivers' parking choice behavior and optimize parking lot operation management.

Benefits of technology

It effectively reflects the dynamic decision-making process of individual drivers' behavior, quantifies influencing factors, improves parking lot operation efficiency and scientific resource allocation, and provides innovative solutions for parking lot planning, design and management.

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Abstract

The invention provides a multi-agent simulation analysis method for operation optimization of an urban parking system, and the method comprises the following steps: S1, obtaining influence factors which remarkably influence the parking selection behavior of a driver through questionnaire design and experiment based on will survey; s2, through a Binomial Logit discrete selection model, carrying out quantitative analysis on the influence factors of the parking selection behavior of the driver; s3, building a multi-agent parking lot microscopic simulation platform in combination with a Netlogo development environment; and S4, constructing a double-layer model for parking lot operation management optimization, and solving by adopting a simulated annealing algorithm. On the basis of analyzing the parking selection behavior of the driver, the operation efficiency of the parking lot is effectively improved through multi-agent simulation, and a technical tool is provided for operation organization and management strategy evaluation of the parking lot.
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