信息观和粒子群算法在城市交通拥堵中的研究

Information View and Particle Swarm Optimization Algorithm in Urban Traffic Congestion

交通拥堵是近年来在全国凸现的“城市病”,寻找拥堵关键因素是当前急需解决的问题。首先汇总影响交通拥堵的关键因素,再利用信息观理论和粒子群优化算法相结合的约简算法,对关键因素进行特征选择,筛选出影响交通拥堵的关键因素。通过仿真实验结果表明:该算法具有有效性和准确性,为解决城市交通拥堵问题提供有效解决策略。

Traffic congestion is an“urban disease”in China in recent years.It is urgent to find out the key factors of traffic congestion.Firstly,the key factors affecting traffic congestion were summarized.And then,the reduction algorithm combined with information view theory and particle swarm optimization algorithm was used to select the characteristics of key factors,and the key factors affecting traffic congestion were screened out.The simulation test results show that the proposed algorithm is effective and accurate,which provides an effective solution strategy for solving the problem of urban traffic congestion.