Research On City Bus Intelligent Dispatching Optimization

In recent years, urban residents'travel volume is increasing fleetly with the acceleration development of urbanization and urban population growth, and so that the road traffic capacity is becoming saturation with each passing day and traffic problem is bad increasingly. Local governments and traffic management department gradually realized that urban public transportation must be developed hugely to solve the urban transportation problem, namely bus priority strategy and technology of intelligent transportation system are applied. This paper researches the public transportation scheduling problem based on the comprehensive improved genetic algorithm under the background of ITS.The paper researches on the bus timetable scheduling problem under one bus line with and without transfer problem. The bus scheduling models are established, the objective is minimize the passengers'waiting time and maximize the bus enterprise operating profit and the decision variable is the bus departure time, and also the constraints of departure interval and the bus capacity are considered.The paper researches the genetic algorithm and uses the adaptive operation and optimal reserved strategy to improve the algorithm, and so the improved GA is got. In the parameters of genetic algorithm, crossover and mutation probability influence the behavior and performance of genetic algorithm directly, and adaptive genetic algorithm can make crossover and mutation probability changed with the fitness value. Optimal reserved strategy is used to select the superior and eliminate the inferior in the improved GA, namely in the current population the optimal individual doesn't participate in crossover and mutation operations and is used to replace the worst individual of the current generation population which are after the crossover and mutation genetic operation.Basing on the above researches, this paper combines a bus line survey data of economic technical development zone of Qingdao area and constructs bus scheduling model. Then the genetic algorithm and the comprehensive improved algorithm are designed and used to compute the bus scheduling model in which Visual Basic programme language is used to programming. After simulation we get the non-uniform bus timetable in the whole operation period, and the results show that the improved GA is better than standard GA in the performance and is an available method for bus timetable scheduling problem. Also, Comparing between the results and the survey data shows that the scheduling model established has some practical value.