交通信息与安全2011，Vol.29Issue(1)：16-19,24,5.DOI:10.3963/j.ISSN1674-4861.2011.01.005
基于时空特性和RBF神经网络的短时交通流预测
Short-term Traffic Flow Forecasting Based on Spatiotemporal Characteristics of Traffic Flow and RBF Neural Network
高为 1陆百川 1贠天鹂 1谭伟1
作者信息
- 1. 重庆交通大学交通运输学院,重庆,400074
- 折叠
摘要
Abstract
As traffic flow changes dynamically with week-similarity and relevance, this paper presents a short-term traffic flow forecasting method based on spatial and temporal changes in traffic flow characteristics and RBF neural network. The method takes full advantage of the week-similarity and relevance of the traffic flow time series, considering the adjacent section of the traffic flow of interacting factors, with combination of self-learning, self-organizing, and adaptive function of the RBF neural network, plus a wide range of data integration characteristics in short-term traffic flow forecasting. Finally, examples are simulated and analyzed. The results show that the method can improve the traffic flow prediction accuracy.关键词
时空特性/RBF神经网络/交通流预测/仿真分类
交通工程引用本文复制引用
高为,陆百川,贠天鹂,谭伟..基于时空特性和RBF神经网络的短时交通流预测[J].交通信息与安全,2011,29(1):16-19,24,5.
