Predicting spatio-temporal traffic flow: a comprehensive end-to-end approach from surveillance cameras-Reference-Cited by-同舟云学术

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Predicting spatio-temporal traffic flow: a comprehensive end-to-end approach from surveillance cameras

Published:2024-07-22 Issue:1 Volume:12 Page:
ISSN:2168-0566
Container-title:Transportmetrica B: Transport Dynamics
language:en
Short-container-title:Transportmetrica B: Transport Dynamics

Author:

Feng Yuxiang¹,Zhao Yifan²,Zhang Xingchen³,Batista Sérgio F. A.¹,Demiris Yiannis³,Angeloudis Panagiotis¹

Affiliation:

1. Centre for Transport Engineering and Modelling, Department of Civil and Environmental Engineering, Imperial College London, London, UK

2. Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, People's Republic of China

3. Intelligent systems and networks, Department of Electrical and Electronic Engineering, Imperial College London, London, UK

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/21680566.2024.2380915

Reference75 articles.

1. Algiriyage Nilani Raj Prasanna Emma H. Doyle Kristin Stock David Johnston Minura Punchihewa and Santhoopa Jayawardhana. 2021. “Towards Real-Time Traffic Flow Estimation using YOLO and SORT from Surveillance Video Footage.” In Proceedings of the 18th International Conference on Information Systems for Crisis Response and Management July 40–48.

2. A vision-based real-time traffic flow monitoring system for road intersections

3. IEEE Transactions on Intelligent Transportation Systems

4. Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting;Bai Lei;Advances in Neural Information Processing Systems,2020

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