Short-term Traffic Flow Prediction Based on ANFIS

摘要：

Accurate short-term traffic flow prediction has become a critical problem in intelligent transportation systems (ITS). In the paper, a kind of adaptive prediction method for short-term traffic flow based on ANFIS (adaptive-network-based fuzzy interference system) model was presented. ANFIS is a fuzzy interference tool implemented in the framework of adaptive network. It combines the comprehensibility of fuzzy rules and the adaptability and self-learning algorithms of neural networks. The traffic flow prediction model with 104 changeable parameters will be established through the training process, the goal of which is reduce the prediction errors between real predicting output the ANFIS model and the desired output. The result of simulation research demonstrates that this method has the advantage of high precision and good adaptability. This scheme is novel and advanced in the domain of the road traffic flow prediction. The application of the scheme will remarkably improve the response efficiency and precision degree of the road traffic inducement and control system in our country.

关键词： Short-term traffic flow prediction ANFIS model simulation research

作者: CHEN Bao-ping MA Zeng-qiang

作者单位: Structural Health Monitoring and Control Institute Shijiazhuang Railway Institute Shijiazbuang, Chin Department of Electrical & Electronic Engineering Shijiazhuang Railway Institute Shijiazhuang, China

会议类型: 国际会议

会议名称: The International Conference on Communication Software and Networks(2009 IEEE通信软件与网络国际会议 ICCSN 2009)

会议地点: 成都

会议语种:英文

页码: 791-793

在线出版日期: 2009-02-20（万方平台首次上网日期，不代表论文的发表时间）