Model And Algorithm Of Public Transit Network Planning

Urban public transit is a major livelihood project, which is the social welfare undertaking of meeting people’s basic travelling, and an important part of transportation services, to be closely linked people’s daily life, inseparable with the operation and the economic development of cities. Promote the healthy development of urban public transportation, ensure the operation of urban public transit smoothly and orderly, which is utmost importance for promoting the socioeconomic sustainable development, improving the urban living environment, promoting urban civilization, and guaranteeing the basic travel rights of the masses.The purpose of public transit network planning is to provide safe, convenient, comfortable, economic and rapid service, enhance the operating efficiency of public transit system, and promote the development of urban public transit.Public transit network planning includes trip assignment of passenger transferring and evaluation of transit network planning and so on, which is a complex combinational optimization problem. This paper applied the rail passenger transfer network to describe the bus transfer network of the peak period, on which equilibrium analysis of passenger travelling choice behavior was based; Considering the interests of passengers and the bus companies, a bi-level programming model of public transit network planning was established through combining with the evaluation indicators of the length of routes, the nonlinear coefficient of bus routes, the duplication coefficient of bus routes and the average times of transferring; Simulated annealing algorithm was designed to solve the model, through seven searching methods:deleting routes, generating routes, substituting routes, extending routes, curtailing routes, combining routes and dismantling routes to search the neighborhood solutions. Finally, have an analysis to the Sioux Falls network which has a simple treatment, the example showed that the public transit network planning generated by the model and algorithm had the good service and economic indicators.