Utilize Improved Particle Swarm to Predict Traffic Flow

摘要：

　　Presented an improved particle swarm optimization algorithm,introduced a crossover operation for the particle location,interfered the particles speed,made inert particles escape the local optimum points,enhanced PSO algorithms ability to break away from local extreme point.Utilized improved algorithms to train the RBF neural network models,predict short-time traffic flow of a region intelligent traffic control.Simulation and test results showed that,the improved algorithm can effetely forecast short-time traffic flow of the regional intelligent transportation control,forecasting effects is better can be effectively applied to actual traffic control.

关键词： Improved particle swarm RBF neural network Traffic flow prediction

作者: Hongying LIU

作者单位: Dept.of Computer Science and Engineering Guangzhou Vocational & Technical Institute of Industry & Commerce Guangzhou, China

会议类型: 国际会议

会议名称: 2012 2nd International Conference on Computer and Information Applications(ICCIA2012)(2012第二届计算机和信息应用国际会议)

会议地点: 太原

会议语种:英文

页码: 1381-1384

在线出版日期: 2012-12-08（万方平台首次上网日期，不代表论文的发表时间）