Research On Optimal Path Solving Algorithm For Dynamic Road Network Based On Multi Factor Analysis

Nowadays, transportation, as the main carrier in social and economic activities, play a very important role in various industries. And with the continuous development of society and economic, following a sharp increase in the number of motor vehicles, which result in the increasing load of road, causing road congestion, traffic frequently, as well as serious environmental pollution problems. At present, intelligent transportation system is the most effective way to solve or alleviate the traffic problem, and one of important part is the dynamic guidance system. The system is combined with the actual dynamic traffic information, using intelligent algorithm to find out a highly efficient and reasonable optimal path. With this way, not only it can save the traveler’s travel time, but also it can alleviate the traffic load and improve the energy utilization rate, as well as reduce the pollution of vehicle exhaust to the environment. It play a very important role in people’s daily life and social development.Based on the factors of influencing road traffic, the in-depth of the various path optimization of intelligent algorithm was investigated. We proposed the hybrid ant colony particle swarm algorithm, and analyzes the multi factor dynamic road network optimum path through the algorithm. The specific work are as follows:1) In this paper, the optimal path was obtained by analyzing the factors influenced the dynamic road network. According to the needs of travelers, we established different models to represent sections of the weights, and make an in-depth study of the optimal path algorithm and focusing on the analysis of the ant colony algorithm and particle swarm algorithm.2) Comparing and analyzing the advantages and disadvantages of these two algorithms, a hybrid ant colony particle swarm optimization algorithm was proposed. This algorithm was mainly uses the particle swarm algorithm to initialize the parameters of the ant colony algorithm, and by mixing the two kinds of algorithms, the ants could also have the characteristics of particles. Through the att48 problem, the hybrid ant colony algorithm is better than the ant colony algorithm and particle swarm optimization algorithm in solving accuracy or convergence speed.3) Finally, the hybrid ant colony particle swarm algorithm is used to analysis the multi-factor dynamic road network optimum path, draw the following conclusion: I. It is proved that the hybrid ant colony particle swarm algorithm in urban road network is used to solve the optimal path is effective through the adjustment; II. A traveler optimal target is not the same, the optimal path selection may also is not the same; III, even if a walker give the same requirements as the optimal target, optimal path selection may also differ in different time.