广西师范大学学报（自然科学版）2013，Vol.31Issue(1)：1-5,5.
基于特性和影响因素分析的短时交通流预测
Short-term Traffic Flow Forecasting Based on Analysis of Characteristics and Impact Factors
摘要
Abstract
Reliable short-term traffic flow forecasting is an important foundation for the intelligent transportation system. In order to improve the accuracy of the short-term traffic flow forecasting and increase its adaptability in different traffic states,a combination forecasting model based on the analysis of traffic flow characteristics and space-time two-dimensional impact factors is presented to reflect the characteristics and influencing factors. The model has three sub-models:time-series model,space-related model and combination forecasting model. The single forecast models includes single adaptive exponential smoothing model and RBF neural network model. The combination coefficient is obtained adaptively based on the smoothing percentage relative error of the two single forecast sub-modules as input by using the neural network as a learning algorithm. Finally,the traffic flow data are measured respectively in flat peak and peak hours to verify the validity and reliability of the model. The results show that the combination model can produce more precise forecasting than that of two individual models and adapt to different traffic states better.关键词
智能交通系统/交通流预测/指数平滑法/RBF神经网络Key words
intelligent transport system/traffic flow prediction/exponent smoothness method/RBF neural network分类
交通工程引用本文复制引用
许伦辉,游黄阳..基于特性和影响因素分析的短时交通流预测[J].广西师范大学学报（自然科学版）,2013,31(1):1-5,5.基金项目
国家自然科学基金资助项目(51268017,61263024) （51268017,61263024）
