Algorithm And Research On Bus Rapid Transit

With the development of China's urbanization,traffic problems of urbans have become increasingly serious and widespread,which has affected the city's production and our daily life. How to solve traffic problems has become the focus of commen concern.Under this circumstance,the Intelligent Transport Systems(ITS) will become an important way to solve these problems. With the rapid development of China's ITS technology,especially,a number of domestic cities have constructed and implemented some demonstration projects in advanced public transportation system in recent years. The technology of information collection and publishing, which support the decision-making of bus scheduling is mature.And the environment of priority-operating of public transport is well improved.Advanced Public Transportation System (APTS) is one of the major subsystems of ITS. In general,APTS include:the public transport scheduling management system,the bus information of operation system,bus rapid transit system (BRT),the advanced security of public transport vehicles. BRT is an important part of APTS. Since 2005,a lot of BRT projects have been built and are building in Beijing,Hangzhou,Kunming,Hefei,Jinan, Shenzhen,Dalian,Xiamen and Changzhou,which greatly reduce the cities'traffic pressure.Bus schedule is one of the main aspects,which affect the operating efficiency of public transportation system and service level,and become the core content of intelligent transportation system. This paper is on the basis of ITS,with conscientiously study the basic knowledge of ant colony optimization algorithms and genetic algorithm,using mixed ant colony optimization genetic algorithm to solve the schedule problem of BRT. Scheduling grid is the basic mode of public transport. The goal is to make the time passengers waiting for buses minimum and the income of public transport enterprises maximum,as well as considering the longest and the shortest grid spacing,the grid spacing of two adjacent buses and the load factor. Using the start time as variables to establish mathematical model.In this paper,we mixed-use genetic algorithms and ant colony optimization algorithm. After each cross-operator,we choose the optimal chromosomes in current group as the optimum solution,then using ants to find their gene mutation which is better. According to probability function,each ant choose the paths, then produce a new chromosome. If the new chromosome is better than the original chromosome according to fitness,then retain the contrary;otherwise adandon it. If the number of better chromosome groups is up to their population, we stop looking for it. In this way, using ant colony optimization algorithm as the guidance of variance in genetic algorithms, we make the variation more intelligent. Using Bus Rapid Transit in Dalian as an example to seek for start times during the entire scheduling period.If we use the results obtained in bus scheduling, the number of grid and cost can be greatly reduced but not affect passengers'travel.