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Research on the Coordination Model of Passenger Transportation Mode in the Intercity Comprehensive Transportation Corridor
Author(s) -
Yong Liao
Publication year - 2022
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2022/6528089
Subject(s) - crossover , mathematical optimization , genetic algorithm , transportation planning , pareto principle , transportation theory , mode (computer interface) , function (biology) , operations research , computer science , linear programming , order (exchange) , transport engineering , engineering , economics , mathematics , artificial intelligence , evolutionary biology , biology , operating system , finance
At present, the study of comprehensive transportation corridors primarily focuses on the planning and construction of transportation corridors. There are few studies on the coordinated operation of all modes of transportation after the construction of the transportation corridor is completed. Comprehensive transportation corridor takes on the characteristics that the volume of transport demand and supply is large, the number of transportation modes is not only one, and competition among different transportation modes is fierce. In order to prevent unhealthy competition among different transportation modes from disrupting the transportation market, this paper takes the passenger transportation modes within the intercity comprehensive transportation corridor as the studied object and establishes a coordination model for them. The model introduces the disaggregate model as the research theory, takes the quantizable attributes of transportation modes as the decision variables, takes the reasonable distribution of passenger flow among different transportation modes as the objective function, and takes the supply capacity of different transportation modes and the reasonable value range of decision variables as the constraints. This model is a multiobjective nonlinear programming problem. The multiobjective genetic algorithm is designed to solve the model. The real number encoding method is adopted to encode the decision variables; the penalty function method is used to eliminate the solutions that do not meet the nonlinear constraints after crossover and mutation; the repairment algorithm is used to convert the solutions that do not satisfy the linear constraints and bound constraints after crossover and mutation into the feasible region. Pareto optimal solution set is obtained through continuous selection, crossover, and mutation. Finally, a numeric example is made to demonstrate that the method proposed in this paper is effective and feasible.

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