Citation

Li Y, Xu J, Li Y, Xue Y, Yao Z. Transportmetrica B: Transp. Dyn. 2024; 12(1): e2297143.

Copyright

(Copyright © 2024, Hong Kong Society for Transportation Studies, Publisher Informa - Taylor and Francis Group)

DOI

10.1080/21680566.2023.2297143

PMID

unavailable

Abstract

Effective estimation and prediction of freeway congestion propagation is the basis for formulating a traffic management strategy. A new estimation and prediction model for congestion propagation based on coordinated vehicle positioning data collected from fixed detectors is proposed. A shockwave-based method is established to model the propagation trajectory of congestion in time and space using data filtered by the Savitzy-Golay. Bayesian ridge regression is applied to determine the probability range of the propagation path of the congestion. The proposed method was tested with Wi-Fi positioning data from a section of the Beijing-Kunming Freeway in China. The results of our method were compared with filed traffic conditions estimated by loop data. The results show a difference of approximately 200s between the start time of the estimated congestion and the observed time on the selected road section. The predicted congestion start time differs from the observed results by around 100s.

Language: en

Keywords

Bayesian ridge regression; congestion prediction; shockwave estimation; tagged vehicle positioning data; Traffic congestion propagation