Abstract
The ability to perform short-term traffic flow forecasting is a crucial component of intelligent transportation systems. However, accurate and reliable traffic flow forecasting is still a significant issue due to the complexity and variability of real traffic systems. To improve the accuracy of short-term traffic flow forecasting, this paper presents a novel hybrid prediction framework based on Support Vector Regression (SVR) that uses a Random Forest (RF) to select the most informative feature subset and an enhanced Genetic Algorithm (GA) with chaotic characteristics to identify the optimal forecasting model parameters. The framework is evaluated with real-world traffic data collected from eight sensors located near the 1-605 interstate highway in California. Results show that the proposed RF-CGASVR model achieves better performance than other methods.