With the rapid growth in the number of vehicles, energy consumption and environmental pollution in urban transportation have become a worldwide problem. Efforts to reduce urban congestion and provide green intelligent transport become a hot field of research. In this paper, a multimetric ant colony optimization algorithm is presented to achieve real-time dynamic path planning in complicated urban transportation. Firstly, four attributes are extracted from real urban traffic environment as the pheromone values of ant colony optimization algorithm, which could achieve real-time path planning. Then Technique for Order Preference by Similarity to Ideal Solution methods is adopted in forks to select the optimal road. Finally, a vehicular simulation network is set up and many experiments were taken. The results show that the proposed method can achieve the real-time planning path more accurately and quickly in vehicular networks with traffic congestion. At the same time it could effectively avoid local optimum compared with the traditional algorithms.
A Multimetric Ant Colony Optimization Algorithm for Dynamic Path Planning in Vehicular Networks
