中南大学学报（自然科学版）2016，Vol.47Issue(12)：4276-4281,6.DOI:10.11817/j.issn.1672-7207.2016.12.041
基于粒子群优化投影寻踪回归模型的短时交通流预测
Short-term traffic flow prediction method based on particle swarm optimization projection pursuit regression model
摘要
Abstract
Considering the highly complexity, randomness and non-stability characteristics of short-time traffic flow data, a short–term traffic flow prediction method based on particle swarm optimization projection pursuit regression model was put forward. Traffic flow forecasting impact factors were determined by grey relational analysis. Then the projection pursuit nonparametric regression traffic flow forecasting model was constructed using particle swarm optimization algorithm. Finally, validation and comparative analyses were carried out using inductive loop data measured from the north-south viaduct in Shanghai. The results indicate that the proposed PSO-PPR model achieves better prediction performance than comparison methods. The average prediction accuracy of proposed method is 37.8%and 27.2%higher than ARIMA model and BPNN model, respectively.关键词
智能交通系统/短时交通流预测/投影寻踪回归模型/粒子群优化/灰色关联度分析Key words
intelligent transportation systems/short-term traffic flow prediction/projection pursuit regression model/particle swarm optimization/grey relational analysis分类
交通工程引用本文复制引用
邴其春,龚勃文,林赐云,杨兆升,曲鑫..基于粒子群优化投影寻踪回归模型的短时交通流预测[J].中南大学学报（自然科学版）,2016,47(12):4276-4281,6.基金项目
国家高技术研究发展计划项目(2012AA112307)；国家科技支撑计划项目(2014BAG03B03)；国家自然科学基金资助项目(51308248,51408257)；吉林省科技发展计划青年科研基金资助项目(20140520134JH)(Project(2012AA112307) supported by the National High Technology Research and Development Program of China （2012AA112307）
Project(2014BAG03B03) supported by the National Science and Technology Pillar Program （2014BAG03B03）
Projects(51308248,51408257) supported by the National Natural Science Foundation of China （51308248,51408257）
Project(20140520134JH) supported by Jilin Province Science and Technology Development Plan of Youth Research Fund) （20140520134JH）
