Immediate traffic flow monitoring and management based on multimodal data in cloud computing

Weike Ding; Hao Tan; Huiming Zhou; Zihan Li; Chao Fan

doi:10.54254/2755-2721/67/2024MA0052

Article Information

Received: 24 April 2024

Published: 31 May 2024

DOI: https://doi.org/10.54254/2755-2721/67/2024MA0052

Article Type: Research Article

Cite this article

Ding,W.;Tan,H.;Zhou,H.;Li,Z.;Fan,C. (2024). Immediate traffic flow monitoring and management based on multimodal data in cloud computing. Applied and Computational Engineering,67,267-272.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About this Volume

Volume Title: ACE Vol.67

Part of Series: Applied and Computational Engineering

ISSN: 2755-2721 (Print) / 2755-273X (Online)

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[1]Kashyap, Anirudh Ameya, et al. "Traffic flow prediction models–A review of deep learning techniques." Cogent Engineering 9.1 (2022): 2010510.

[2]YinX. et al.Multi-stage attention spatial-temporal graph networks for traffic prediction,Neurocomputing,(2021)

[3]Treiber, Martin, and Arne Kesting. "Traffic flow dynamics." Traffic Flow Dynamics: Data, Models and Simulation, Springer-Verlag Berlin Heidelberg (2013): 983-1000.

[4]Varga, Norbert, et al. "An architecture proposal for V2X communication-centric traffic light controller systems." 2017 15th International Conference on ITS Telecommunications (ITST). IEEE, 2017Razali, Noor Afiza Mat, et al. "Gap, techniques and evaluation: traffic flow prediction using machine learning and deep learning." Journal of Big Data 8.1 (2021): 1-25.

[5]Navarro-Espinoza, A., López-Bonilla, O. R., García-Guerrero, E. E., Tlelo-Cuautle, E., López-Mancilla, D., Hernández-Mejía, C., & Inzunza-González, E. (2022).

[6]Zheng, Jiajian, et al. "The Random Forest Model for Analyzing and Forecasting the US Stock Market in the Context of Smart Finance." arXiv preprint arXiv:2402.17194 (2024).

[7]Yang, Le, et al. "AI-Driven Anonymization: Protecting Personal Data Privacy While Leveraging Machine Learning." arXiv preprint arXiv:2402.17191 (2024).

[8]Cheng, Qishuo, et al. "Optimizing Portfolio Management and Risk Assessment in Digital Assets Using Deep Learning for Predictive Analysis." arXiv preprint arXiv:2402.15994 (2024).

[9]Zhu, Mengran, et al. "Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data." arXiv preprint arXiv:2402.09830 (2024).

[10]K. Xu, X. Wang, Z. Hu and Z. Zhang, "3D Face Recognition Based on Twin Neural Network Combining Deep Map and Texture," 2019 IEEE 19th International Conference on Communication Technology (ICCT), Xi'an, China, 2019, pp. 1665-1668, doi: 10.1109/ICCT46805.2019.8947113.