Short-term traffic parameters prediction method based on vector error correction model

被引：0
作者：
Bing, Qi-Chun [1 ]
Yang, Zhao-Sheng [1 ,2 ,3 ]
Zhou, Xi-Yang [1 ]
Ma, Ming-Hui [1 ]
机构：
[1] College of Transportation, Jilin University, Changchun
[2] State Key Laboratory of Automobile Simulation and Control, Jilin University, Changchun
[3] Jilin Province Key Laboratory of Road Traffic, Jilin University, Changchun
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2015年 / 45卷 / 04期
关键词：
Engineering of communication and transportation system; Short-term prediction; Traffic parameters; Vector error correction model;
D O I：
10.13229/j.cnki.jdxbgxb201504008
中图分类号：
学科分类号：
摘要：
In order to further improve the prediction accuracy of short-term traffic parameters, according to the inherent correlation between traffic parameters, on the basis of stationarity test and cointegration test for traffic parameters time series, a multivariate time series model for short-term traffic parameters forecasting is proposed, which is called vector error correction model. The stationarity of the vector error correction model was tested. Validation and comparative analysis were carried out using inductive loop data measured from the north-south viaduct in Shanghai, China. The experiment results indicate that the proposed vector error correction model has good prediction performance and can further reduce the prediction errors of short-term traffic parameters. ©, 2015, Editorial Board of Jilin University. All right reserved.
引用
收藏
页码：1076 / 1081
页数：5
相关论文
共 11 条
  • [1] Williams B.M., Multivariate vehicular traffic flow prediction: An evaluation of ARIMAX modeling, The Transportation Research Board Annual Meeting, (2001)
  • [2] Yang Z.-S., Wang Y., Guan Q., Short-time traffic prediction method based on SVM, Journal of Jilin University (Engineering and Technology Edition), 36, 6, pp. 881-884, (2006)
  • [3] Zhou X.-P., Feng Q., Sun L.-J., Short-term traffic flow forecasting based on nearest neighbor algorithm, Journal of Tongji University(Nature Science), 34, 10, pp. 1494-1498, (2006)
  • [4] Gong B.-W., Lin C.-Y., Li J., Et al., Short-time traffic flow prediction based on KSOM-BP neural network, Journal of Jilin University (Engineering and Technology Edition), 41, 4, pp. 938-942, (2011)
  • [5] Fan N., Zhao X.-M., Dai M., Et al., Short-term traffic flow prediction model, Journal of Traffic and Transportation Engineering, 12, 4, pp. 114-119, (2012)
  • [6] Chang G., Zhang Y., Yao D.-Y., Short-time traffic flow forecasting model for regional road network based on spatial-temporal dependency, Journal of Tsinghua University (Science and Technology), 53, 2, pp. 215-221, (2013)
  • [7] Gao W., Lu B.-C., Yun T.-L., Et al., Short-term traffic flow forecasting based on spatiotemporal characteristics of traffic flow and RBF neural network, Journal of Transportation Information and Safety, 29, 1, pp. 16-24, (2011)
  • [8] Mina W., Wynterb L., Real road traffic prediction with spatio-temporal correlation, Transportation Research Part C, 19, 4, pp. 606-616, (2011)
  • [9] Stathopoulos A., Karlaftis M.G., A multivariate state space approach for urban traffic flow modeling and prediction, Transportation Research Part C, 11, 2, pp. 121-135, (2003)
  • [10] Wan L.-M., The study on efficiency of Chinese fuel oil futures market, (2010)
12