A Multi-Agent Simulation Method for Air Taxi Combining Double-DQN Network

By combining Double-DQN networks and hierarchical finite state machines, an air taxi scheduling model is constructed, which solves the shortcomings of existing air taxi simulation methods, realizes detailed simulation and optimized scheduling of individual air taxi behavior, and improves the realism of simulation results and system efficiency.

CN122311985APending Publication Date: 2026-06-30ZHEJIANG UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
ZHEJIANG UNIV
Filing Date
2026-06-03
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Existing multi-agent simulation methods for air taxis neglect the low-altitude environment and the individual behavior of air taxis, making it difficult to adaptively learn and optimize scheduling strategies. They also cannot effectively cope with dynamic order allocation and multi-vehicle collaboration, resulting in significant deviations between simulation results and reality.

Method used

An air taxi scheduling model is constructed using a Double-DQN network. Combined with a hierarchical finite state machine and a composite urban air taxi traffic network model, the model simulates the full lifecycle behavior of air taxis and passenger behavior at stations in detail. The model is then trained and optimized using a multi-agent simulation system.

Benefits of technology

It significantly improves the realism and credibility of simulation results, can autonomously learn and optimize scheduling strategies, improve system operating efficiency, and provides multi-dimensional statistical indicators and visualization results to support decision-makers in analysis and planning.

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Abstract

This invention discloses a multi-agent simulation method for air taxis combining a Double-DQN network. The method includes constructing a low-to-high-level composite urban air taxi traffic network model based on urban geographic information data; then building a fixed-time-step multi-agent simulation system based on the constructed air taxi agent model and station passenger behavior model; constructing an air taxi scheduling model; training the air taxi scheduling model within the multi-agent simulation system; deploying the trained air taxi scheduling model into the multi-agent simulation system; running the simulation based on the low-to-high-level composite urban air taxi traffic network model; and outputting the simulation results of air taxi operation. This invention constructs a two-layer urban air traffic network, employs multiple air taxi agents to finely simulate the entire process behavior, and combines optimized scheduling strategies to achieve realistic simulation and efficiency improvement of air taxi operation.
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