A method for optimizing the location and path of urban air taxis

By establishing a hybrid integer planning model, considering the battery capacity and charging situation of air taxi, optimizing the location and path planning of air taxi, solving the problem of local optimal solutions in the existing technology, and achieving more efficient operation and charging solutions.

CN113935543BActive Publication Date: 2025-03-28BEIHANG UNIV
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Patent Information

Application Number
CN202111289896.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Priority Date
2021-10-28
Filing Date
2021-11-02
Publication Date
2025-03-28
Estimated Expiration
2041-11-02

AI Technical Summary

Technical Problem

In the prior art, site selection and operational path planning of air taxi facilities are usually considered separately, resulting in the formation of a local optimal solution, and the failure to fully consider the battery capacity, state of charge and charging needs of air taxi.

Method used

A method of site selection-path optimization for urban air taxi is proposed. By establishing a hybrid integer planning model, considering the battery capacity of air taxi, the state of charge during transporting passengers, the charging situation and the service time window of passengers, the air taxi site selection-path model with time window and charging is constructed.

Benefits of technology

This method can more effectively optimize the location selection and path planning of air taxis, avoid local optimal solutions, improve operational efficiency, meet passenger service time window requirements, and optimize charging solutions.

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Abstract

The present invention discloses a method for optimizing the location and path of an urban air taxi, and relates to the field of urban traffic optimization. First, a directed network including nodes and edges is constructed, and a mixed integer programming model is established. With the goal of minimizing the total cost, an air taxi location and path model with a time window and considering charging is established. Then, an initial solution is selected, and the value of a penalty-bearing value function is calculated using the objective function of the model. One of the three neighborhood structures under the perturbation program is randomly selected to act on the current initial solution to obtain an updated solution S1, and S1 is used as the initial solution to obtain S2 by using a taboo search algorithm for updating. A simulated annealing method is applied to determine whether the new solution S2 is accepted as the current optimal solution. When the number of iterations is greater than the number of feasibility iterations, the feasibility stage is executed for the current optimal solution until the number of iterations reaches the maximum number of iterations, and the current best solution after feasibility is output. The present invention avoids the algorithm from falling into a local optimal solution and improves efficiency.
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