On The Urban Bus Network Optimizaiton Under The Construction Of Urban Rail Transit

With the acceleration of China’s urbanization, traffic problems in city become more and more serious. Many cities are interested in building urban rail transit to increase the transport capacity of the urban transport system. When the urban rails are put into use the public transport system will be greatly changed. It is necessary to optimize the urban bus routes in order to mostly take advantage of the urban rail transit and increase the capacity of the whole public transport system. This optimization should make the bus transit and the rail transit be coordinated and create a new public transport system. This paper researched on the bus network optimization problem from the following aspects.First, this paper summarized the necessity of bus network optimization when the urban rails are put into use. Then the bus network optimization method was proposed based on the analysis of citizens’travel actions, urban transit corridor’s characteristics and the urban rail transit’s influence on the urban bus transit in different periods.Second, this paper located the position of bus transit and rail transit in the whole urban public transport system. The method to forecast the bus transit the bus demand was proposed after the influence mode and scale of the rail transit were identified. Then this paper described the OD matrix adjustment method and pointed out the disadvantages of this method. After all this paper proposed a new method which based on the Nested Logit model to forecast the bus demand.Third, this paper searched for the means to solve the bus network optimization problem. It make clear the purpose of the bus network optimization on the basis of the bus demand forecast and then provided two kinds of methods to optimize the bus network, the evaluation method and the mathematical method. This paper described the procedure of the evaluation method and builds an evaluation index system. On the other side, this paper compared the three kinds of mathematical methods and then uses genetic algorithm to build an optimization model. At last this paper designs an example to test the practicability of the optimization model.