Examples of Validating an Adaptive Kalman Filter Model for Short-Term Traffic Flow Prediction

摘要

The paper validates an improved adaptive Kalman filter model (AKFM) for short-term traffic flow prediction on intersections in Shanghai, China. In this field, much research has been conducted in developed countries. However, less work has been done in China, a typical developing country, particularly dealing with the realtime prediction method with mixed traffic flow characteristics. This paper studies the adaptive mechanism method of time-window and the state transition parameters for improved model in detail, and carries out the characteristic and predictability analysis of the traffic flow volume using the detector data of an intersection in Shanghai, China. In addition, this paper has implemented the improved adaptive model in C++ and simulation results show it is effective, stable and self-adaptive. The findings of this study provide some useful insights into the short-term traffic flow prediction in urban intersections or other similar intersections in China.