Hybrid GA-SVR: An Effective Way to Predict Short-Term Traffic Flow

Guanru Tan, Shiqiang Zheng, Boyu Huang, Zhihan Cui, Haowen Dou, Xi Yang, Teng Zhou

Abstract: Establishing an accurate short-term traffic flow prediction model is an important part of the intelligent transportation system (ITS). However, due to the nonlinear and stochastic dynamics of the traffic flow, building an effective predictive model remains a challenge. Support vector regression (SVR), a model that is widely used to solve non-linear regression problems, has good predictive performance for time series data such as traffic flow. But the hyperparameters of support vector machines affect their predictive performance. This paper presents a prediction model using a genetic algorithm (GA) to determine the combination of hyperparameters for the SVR model, called a hybrid GA-SVR model. Experiments on real-world traffic flow data have shown that the hybrid GA-SVR model has superior predictive performance than several state-of-the-art prediction algorithms.