Research On Application Of Intelligence Schedule Of Pubic Traffic Vehicles Based On Genetic Algorithm

Intelligent Public Transport System is a main direction which Intelligent Transportation Systems (ITS) studies, has some functions which localization tracking, auxiliary navigation, dispatch control, dynamic issue of information of public transport and inquiry optimal path for journey It was created to maximize the utilization of vehicles and road, to improve service quality of bus, thereby creating a huge social and economic benefits, so the study of intelligent pubic transportation systems has far-reaching significance.However, start schedule of the bus is the core of urban public transport scheduling and the fundamental basis which dispatcher, driver and conductor of a public vehicle to work and buses to run normally. Laying down start schedule need to establish a corresponding optimization model and to select and design effective algorithms to solve. At present, most of the research literatures are based on a statistical time period (e.g.1 hour) as the basic object of a model, obtained in the time period of the uniform grid spacing. Obviously, this ignores the entire period of time the data changes. On the basis of the characteristics of public transportation vehicles scheduling and genetic algorithm, established a model which takes into account the interests of passengers and bus companies of public transport smart scheduling problem based on improved genetic algorithm in order to solve the traffic schedule. The model aims at the minimum of passengers' waiting time and the maximum of bus company' income, and subjects to the maximum and minimum dispatching interval, the difference value of adjacent interval and the full-load rate. Then the improved genetic algorithm is developed to solve the problem, and throughout the scheduling period of no uniform timetable is obtained by programming with simulation. Results show that the improved Genetic Algorithm can find the approximate best result in the huge search space of optimization, while greatly increased the computational efficiency. Finally the scheduling timetable has been arranged to the line, which will not appear the phenomenon of "the smallest gap" or "the biggest gap", reduced time which passenger waiting for a train, and improved service levels of the vehicle. It achieves intellectualization requirements of the public transportation system.