Method for optimizing public traffic network

A technology of public transportation and optimization methods, applied in genetic rules, instruments, data processing applications, etc., can solve problems such as falling into local optimal solutions, achieve the effect of overcoming common shortcomings, improving convergence conditions, and avoiding falling into local optimal solutions

Active Publication Date: 2019-01-01
BEIJING JIAOTONG UNIV
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Problems solved by technology

[0008] The embodiment of the present invention provides a public transportation network optimization method, which considers the interests of operators and travelers in the multi-objective model at the same time, maximizes the interests of travelers on the basis of satisfying the interests of operators, and combines simulated annealing algorithm and Nested use of genetic algorithm, through the improvement of target selection and convergence, solve the multi-objective optimization model, improve the singleness in the multi-objective optimization problem, solve the local optimal solution problem in the artificial intelligence algorithm solution, and ensure the global search of the optimization process ability, and prevent the algorithm from falling into a local optimal solution, thereby ensuring the solution quality of the algorithm

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  • Method for optimizing public traffic network
  • Method for optimizing public traffic network

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no. 1 example

[0052] This embodiment provides a method for optimizing a public transportation network. The method optimizes the public transportation network on the basis of the original network, and has the characteristics of dual temperature control, reversible network, genetic nested simulated annealing, and the like. figure 1 Shown is a schematic flow chart of the public transportation network optimization method described in this embodiment. Such as figure 1 Shown, described public transport line network optimization method comprises the following steps:

[0053] Step S1, using the simulated annealing algorithm as the framework, under the framework to minimize the total operating cost of the operating company throughout the day to obtain the initial network;

[0054] Step S2, performing a disintegration operation on the initial wire mesh to form several wire mesh units;

[0055] Step S3, taking the dispersed network units as the input network, constructing a public transportation net...

no. 2 example

[0061] This embodiment provides a method for optimizing a public transport network, figure 2 Shown is a schematic flow chart of the method for optimizing the public transport network described in this embodiment. Such as figure 2 As shown, the public transport network optimization method described in this embodiment includes the following steps:

[0062] Step S21, parameter setting.

[0063] Further, in this step, the following parameters are set: initial temperature (T 0 ), termination temperature (T stop ), the maximum number of iterations (I 1 ), annealing control rate (D 1 ), control temperature (T c ), return temperature (T p ), iteration return rate (D 2 ) and cost difference control variable (C A ).

[0064] Preferably, set T in this embodiment 0 The value is 100, T stop The value is 0, I 1 The value is 500, D 1 The value is 0.97, D 2 The value of 0.94; C A It is 33.00% of the operating cost of the smallest line network that has been found.

[0065] I...

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Abstract

A method for optimizing public traffic network features that the simulated annealing algorithm is used as a frame, and the whole-day total operation cost of operation company is minimized as an objective to obtain initial line network under the frame. The initial line network is scattered to form the line network unit, which is used as the input network, and the genetic algorithm is embedded to optimize it. The public transportation line network optimization model is constructed to minimize the total travel time of all travelers, and the simplified new line network is formed. The change of operation cost is compared to determine whether the convergence condition is reached. The invention combines the simulated annealing algorithm with the genetic algorithm, which ensures the global searching ability of the optimization process and avoids the algorithm from falling into the local optimal solution, thereby improving the solution quality. At the same time, the design concept of 'element'is proposed to promote the combination of multi-objective optimization process, and the convergence condition of sub-heuristic algorithm is improved by two-temperature cooperative control iteration, thus overcoming the common shortcomings of sub-heuristic algorithm that the convergence condition is difficult to define.

Description

technical field [0001] The invention relates to the technical field of urban planning, in particular to a method for optimizing a public transportation network. Background technique [0002] Public transportation is the blood of a city. With the continuous development of the city, it is necessary to continuously optimize the city's public transportation network. Scholars at home and abroad have carried out research on the optimization of public transportation network earlier. From the perspective of optimization objectives, the public transport network involves different stakeholders, including operators and travelers; from the perspective of algorithm design, most studies on the optimization of public transport networks focus on using sub-heuristic algorithms to solve them. [0003] In terms of optimization objectives, the public transport network involves the processing of multi-objective problems. The research on multi-objective optimization problems generally adopts the...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/26G06N3/12
CPCG06N3/126G06Q10/047G06Q50/26
Inventor 冯雪松张路凯朱晓静
Owner BEIJING JIAOTONG UNIV
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