The Application Research Of Swarm Intelligence Algorithm In Public Transportation Transfer Multiple Mode Routing

With the increasing number of private cars, urban traffic congestion problem has become more and more serious. In order to ease the traffic congestion problem, we have to develop the public transport. With the developing of urban public transport network, the characteristics of passenger flow network is gradually emerging, the traveler is no longer limited to the choice the single travel route. Therefore, passengers’ travel route choice behavior in the transportation network has changed greatly. How to choose a convenient, reasonable and meet to different needs of different groups route, is an important part of the urban intelligent transportation system(ITS) construction, and also an important issue for scholars.Based on the analysis and summary of the relevant research situation, this paper will focus on the multi mode transfer scheme and the related swarm intelligence algorithm of urban public transport, do some research as follows:Firstly, research the multi mode transfer of demand. Comprehensive consideration various common modes of public transport, analysis the structure of urban public transport network, establish a public transport network travel transfer model. Using the behavior characteristics of ant foraging path selection, the model of transfer frequency, travel time and travel cost model are constructed by the update mechanism of the intensity of the route, and the route choice of ant colony algorithm is studied.Secondly, improve the path selection algorithm. On the basis of analyzing the existing path selection algorithms, this paper will focus on the application of the heuristic algorithm in traffic routing. In order to overcome the shortcomings of the ant colony optimization(ACO), such as premature and low searching speed, a quantum ant colony algorithm(QACA) is introduced into the ant colony algorithm. In quantum computing theory and concept of quantum ant colony algorithm as a basis for quantum rotation gate technology is introduced in ant colony algorithm, increase the qubit heuristic factor, pheromone by encoding qubits, by quantum rotation gates, and update the pheromone, population size can be free to regulate, enhancement algorithm optimization characteristics, it can increase the effective population dispersion, enhanced ant colony algorithm with global searching ability and accelerate the convergence speed of the algorithm, due to the parallel is better, which has important application value and theoretical value.Tirdly, in order to verify the effectiveness of the algorithm, this paper will analyze and study the typical example. Through the comparison of the experimental results, we find that the use of quantum ant colony algorithm for traffic network transfer, and the typical shortest path routing algorithm- Dijkstra algorithm can increase the diversity of the search time; compared with the basic ant colony algorithm, can increase the search optimal path.Finally, a case study of the Songjiang District urban public transport line is carried out, and the transfer scheme between two sites is specified. The feasibility and validity of the research is further verified.The key point of this paper is to improve the ant colony algorithm. The quantum computing method is introduced into the ant colony algorithm, and the quantum ant colony algorithm is applied to the existing public transportation network to make the search more feasible and effective.