A Data-Driven Method for Congestion Identification and Classification-Reference-Cited by-同舟云学术

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A Data-Driven Method for Congestion Identification and Classification

Published:2022-04 Issue:4 Volume:148 Page:
ISSN:2473-2907
Container-title:Journal of Transportation Engineering, Part A: Systems
language:en
Short-container-title:J. Transp. Eng., Part A: Systems

Author:

Zarindast Atousa¹,Poddar Subhadipto²,Sharma Anuj³

Affiliation:

1. Ph.D. Student, Dept. of Civil and Environment Engineering, Iowa State Univ., Ames, IA 50010 (corresponding author).

2. Dept. of Civil and Environment Engineering, Iowa State Univ., Ames, IA 50010.

3. Associate Professor, Dept. of Civil and Environment Engineering, Iowa State Univ., Ames, IA.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Transportation,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/JTEPBS.0000654

Reference56 articles.

1. Aboah A. 2021. “A vision-based system for traffic anomaly detection using deep learning and decision trees.” In Proc. IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR) Workshops 4207–4212. Piscataway NJ: IEEE.

2. Adams R. P. and D. J. MacKay. 2007. “Bayesian online change point detection.” Preprint submitted October 19 2007. http://arxiv.org/abs/0710.3742.

3. Alomari, E., R. Mehmood, and I. Katib. 2020. “Sentiment analysis of Arabic tweets for road traffic congestion and event detection.” In Smart infrastructure and applications, 37–54. Berlin: Springer.

4. Spatio-temporal clustering for non-recurrent traffic congestion detection on urban road networks;Anbaroglu B.;Transp. Res. Part C Emerging Technol.,2014

5. Non-recurrent traffic congestion detection on heterogeneous urban road networks

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