A Deep Learning Based Traffic State Estimation Method for Mixed Traffic Flow Environment

Abstract

Traffic state estimation plays a fundamental role in traffic control and management. In the connected vehicles (CVs) environment, more traffic-related data perceived and interacted by CVs can be used to estimate traffic state. However, when there is a low penetration rate of CVs, the data collected from CVs would be inadequate. Meanwhile, the representativeness of the collected data is positively correlated with the penetration rate. This article presents a traffic state estimation method based on a deep learning algorithm under a low and dynamic CVs penetration rate environment. Specifically, we design a K-Nearest Neighbor (KNN) data filling model integrating acceleration data to solve the problem of insufficient data. This method can fuse the time feature of speed by acceleration modification and mine the distribution features of speed by KNN. In addition, to reduce the estimation error caused by penetration rate, we design a Long Short-Term Memory (LSTM) model, which uses penetration rate estimated by Macroscopic Fundamental Diagram (MFD) as one of the input factors. Finally, we use the concept of operational efficiency for reference, dividing traffic state into three categories according to the estimated speed: free flow, optimal flow, and congestion. SUMO is used to simulate traffic cases under different penetration rates to evaluate our scheme. The results suggest that our data filling model can significantly improve filling accuracy under a low penetration rate; there is also a better performance of our estimation model than that of other comparison models in both low and dynamic penetration rates.