Near stationary traffic states are of great significance for the calibration of the fundamental diagram and the quantification of capacity variation. In this paper, based on wavelet transform and robust functional pruning optimal partitioning (RFPOP) changepoint detection, a robust and efficient method for automatic identification of the near stationary traffic states is proposed. This method first removes the noise influence of traffic flow series, divides the series automatically into multiple candidate intervals that may be close to stationary states according to the RFPOP changepoint detection method, and calculates the candidate interval characteristics. The near stationary states are then identified based on the modified Cassidy’s criterion. A case study is provided for the proposed method, and its robustness is proved in a simulation experiment. Finally, it is shown that the method of automatic identification of near stationary traffic states proposed in this paper is robust and effective.
Automatic Identification of Near Stationary Traffic States Using Changepoint Detection Method
Transportation Research Record: Journal of the Transportation Research Board
Transportation Research Record: Journal of the Transportation Research Board ; 2676 , 9 ; 187-201
2022-04-04
Article (Journal)
Electronic Resource
English
PLISS: labeling places using online changepoint detection
British Library Online Contents | 2012
|