Advancements in Traffic Flow Prediction and Traffic State Discrimination: A Comprehensive Review

This chapter presents a concise review of the current research status in traffic flow data collection, estimation, prediction, and state estimation. It summarizes the main research trends, and achievements, and identifies the associated issues and limitations. Regarding data collection methods, vehicle-mounted GPS, microwave detection, video detection, and highway toll station data are discussed. GPS data has limitations due to its reliance on vehicle coverage and interruptions caused by signal quality, limiting its representation of overall travel information. Hence, emphasizing the practicality and importance of using limited traffic flow data for road segment flow prediction. Flow prediction commonly employs parametric statistical models and non-parametric machine learning models, with machine learning models exhibiting better estimation accuracy. Further exploration is needed for traffic flow prediction using GPS data. In terms of traffic flow prediction, recent research focuses on utilizing historical data combined with non-parametric machine learning or deep learning models. Exploring traffic flow prediction based on distinct operational features is crucial for understanding traffic on road segments. The analysis of state estimation methods encompasses traffic feature indicators, estimation models, complexity, and classification. Existing studies primarily concentrate on single vehicle types, with limited exploration of parameters for passenger cars and trucks. Additionally, there are differences in determination criteria and levels among government agencies. To predict traffic state accurately with limited parameters, it is essential to explore the parameter ranges specific to different vehicle types.