Calculation Method for Optimal Routing in Traffic Network Big Data

Abstract

In allusion to the influence of traffic flow change on vehicle routing selection, the article aims at finding the method for planning the trip route under the condition of knowing traffic flow change. The method combining particle swarm algorithm and dynamic planning is used for routing optimization in order to obtain the excellent route for various vehicles influenced by traffic flow. The road network structure and the traffic data in actual environment are adopted for simulation and the result shows that this method can truly and dynamically optimize routing, and meanwhile the road traf- fic flow and the travel time of vehicles can also significantly influence the routing selection of vehicles. 1. INTRODUCTON The vehicle routing planning based on traffic flow makes road network diversified and true. Routing optimization not only aims at optimizing the service sequence among client points, but also aims at optimizing the routing among the client points, with the optimization objective converted from shortest distance to shortest time. In modern society, time gradually becomes an important asset in people's daily life and accordingly time use efficiency becomes the essential condition for the benefit maximization in various industries. For logistics industry, along with the increased logistics cost, how to timely and efficiently complete transportation tasks becomes logistics enterprises' problem that shall be urgently solved. With the features of less individuals, simple opera- tion, fast convergence rate, easy realization, etc., particle swarm algorithm has been widely applied by the scholars in the field of computer science and management science and meanwhile has obtained a lot of research achievements. Wu Kaijun, et al., have adopted the binary coded particle swarm algorithm for vehicle routing optimization (1-4). Zhang Li- yan and Zhang Wenjing both have proposed the application of particle swarm algorithm in vehicle routing problem (5, 6). Self-adaptively weighted particle swarm algorithm has been adopted in the article to initially optimize client points and vehicles and then optimize the routing among the client points by stages according to road traffic flow change and finally obtain the optimal distribution routes of various vehi- cles.