Journal of Transportation Systems Engineering and Information Technology ›› 2017, Vol. 17 ›› Issue (6): 40-47.

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A BPNN-based Speed Prediction Method with GA-PSO Optimization Algorithm

LIU Ji-chao a, b, c, CHEN Yang-zhou a, b, c   

  1. a. College of Metropolitan Transportation; b. Beijing Key Laboratory of Transportation Engineering; c. Beijing Collaborative Innovation Center for Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China
  • Received:2017-05-22 Revised:2017-08-24 Online:2017-12-25 Published:2017-12-25

基于GA-PSO 混合优化的BPNN 车速预测方法

刘吉超a, b, c,陈阳舟*a, b, c   

  1. 北京工业大学a. 城市交通学院;b. 北京市交通工程重点实验室;c. 北京城市交通协同创新中心,北京100124
  • 作者简介:刘吉超(1986-),男,江苏东海人,博士生.
  • 基金资助:

    国家自然科学基金/National Natural Science Foundation of China(61573030).

Abstract:

The BP neural network (BPNN) is used to research on the speed prediction. For the problem that the different initial weights and thresholds of the BPNN can influence the speed prediction accuracy, a speed prediction method based on the BPNN with GA- PSO optimization algorithm is proposed. A route from Beigongdaximen to Baigeqiao is selected as the research path; then the speed prediction model based on the BPNN is established. Based on the optimization process between the genetic algorithm (GA) and particle swarm optimization (PSO), the speed prediction method based on the BPNN with GA- PSO optimization algorithm is designed through the method that the optimal weights and thresholds of the BPNN are determined by using the iteratively optimal method. Finally, based on the selected route, the GA- BPNNbased, the PSO-BPNN-based and the proposed speed prediction methods are used to achieve speed prediction, respectively. The result indicates that, compared with the other two optimal methods on the BPNN, the average speed errors of the proposed method are reduced by 37.1% and 24.1%, respectively. The proposed method can effectively improve the accuracy of the speed prediction.

Key words: urban traffic, speed prediction, BP neural network, genetic algorithm, particle swarm optimization

摘要:

BP神经网络(BPNN)已经用于车速预测方面的研究.针对BPNN不同的初始权值和阈值会影响车速预测精度的问题,提出一种基于GA-PSO混合优化的BPNN车速预测方法.以北工大西门到百葛桥为研究路径,构建基于BPNN的车速预测模型;将遗传算法(GA)和粒子群算法(PSO)的寻优过程进行融合,通过逐次迭代取最优的方式确定 BPNN的最优初始权值和阈值,以此设计基于GA-PSO混合优化的BPNN车速预测方法. 最后,以所选路径为对象,利用基于GA-BPNN的预测法、基于PSO-BPNN的预测法,以及提出的方法对车速进行了实验预测.结果表明,相较于前两种车速预测改进方法,本文方法的平均车速预测误差分别降低了37.1%和24.1%,有效地提高了车速的预测精度.

关键词: 城市交通, 车速预测, BP神经网络, 遗传算法, 粒子群算法

CLC Number: