关键词: 短时交通流量, 预测, 独立成分分析, 支持向量机, 遗传算法

Abstract: Traffic flow forecasting is one of the important issues for the research of Intelligent Transportation System (ITS). Through analyzing the characteristics of data collected by different observation place on the same road, the authors proposed a new prediction method of short-term traffic flow in road network based on Independent Component Analysis (ICA) and Support Vector Machine (SVM). First, the traffic flow data of every observation point on the same road was turned into independent source signal through ICA method. Second, SVM model was used to train and predict the source signal, and through Genetic Algorithm (GA) parameters were optimized. At last, the traffic flow forecasting data were obtained by an inverse transform. Real traffic data were applied to test the proposed prediction model. The experimental results show that this method not only is more accurate than the method which uses SVM directly to predict traffic flow, but also can get rid of the data interaction of every observation points on the same road.

Key words: short-term traffic flow, forecasting, Independent Component Analysis (ICA), Support Vector Machine (SVM), genetic algorithm