Intelligent taxi scheduling method and system based on double attention mechanism

By introducing a dual attention mechanism into the taxi scheduling system, dynamically predicting the traffic environment status and optimizing the scheduling strategy, the existing system's insufficient information fusion and imperfect collaboration mechanism in complex traffic environments are solved, and more efficient scheduling and lower air driving rates are achieved.

CN119671208AInactive Publication Date: 2025-03-21EAST CHINA JIAOTONG UNIVERSITY
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Patent Information

Application Number
CN202510166173.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-14
Publication Date
2025-03-21
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The existing taxi dispatching system is difficult to adapt to the dynamic and complex urban traffic environment, with insufficient information integration, imperfect optimization of the collaboration mechanism and poor adaptability, resulting in mismatch between taxi supply and demand and high air driving rate.

Method used

An intelligent taxi scheduling method based on the dual attention mechanism is proposed. The urban traffic network is divided into hexagonal grid units through the agent, and the traffic environment state vector is dynamically generated. The dual attention mechanism is used to predict, and the local supply and demand state and global supply and demand state are obtained. The best scheduling strategy is obtained by combining the critic network and the environment simulator for training.

Benefits of technology

It significantly improves the scheduling efficiency in urban traffic, reduces the air driving rate, can dynamically capture changes in demand patterns in cities, adjusts scheduling strategies in real time, and improves the system's adaptability and optimization capabilities.

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Abstract

The invention provides an intelligent taxi scheduling method and system based on a double attention mechanism, and the method comprises the steps: dividing an urban traffic network into hexagonal grid units through an intelligent agent, and combining the local state of each grid unit with the state of an adjacent grid through an adjacent matrix, dynamically generating traffic environment state vectors according to the traffic environment data; obtaining a local supply and demand state and a global supply and demand state based on the traffic environment state vector; the model is trained through the global supply and demand state, and an optimal scheduling strategy is obtained; an execution action is generated through the optimal scheduling strategy; and updating the strategy by executing the action. The method shows remarkable superiority in a dynamic complex traffic environment, and an efficient and feasible solution is provided for scheduling and repositioning optimization of an urban intelligent traffic system.
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