Estimating Traffic Density in Uncertain Environment: A Case Study of Danang, Vietnam

https://doi.org/10.31130/ud-jst.2023.045E
Tóm tắt: 42 | PDF: 72

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Author

  • Doan Phuoc Mien
    Tra Vinh university, Tra Vinh, Vietnam
    Tran The Vu
    The University of Danang - VN-UK Institute for Research and Executive Education, Vietnam
    Ngo Van Si
    Vietnam Research Institute of Electronics, Informatics and Automation Danang, Vietnam

Từ khóa:

estimation
density
the traffic flow
the uncertain environment

Tóm tắt

The road traffic monitoring system in Vietnam faces significant challenges due to the impact of uncertain factors. The problems of blurred lanes, many types of vehicles in traffic, the view from remote surveillance cameras, etc, directly affect the intelligent traffic management system. We propose an algorithm to estimate the traffic density in an uncertain environment to identify and solve the above problems. Through empirical evaluation of the data set from cameras collected on the website http://0511.vn, it has been proved that this proposed method has high performance in real-time and has an equivalent accuracy to other high-complexity methods.

Tài liệu tham khảo

    [1] K. Abdulrahim and R. A. Salam, “Traffic surveillance: A review of vision based vehicle detection, recognition and tracking,” International journal of applied engineering research, vol. 11, no. 1, pp. 713–726, 2016.
    [2] L. Muñoz, X. Sun, R. Horowitz, and L. Alvarez, “Traffic density estimation with the cell transmission model,” in Pro- ceedings of the 2003 American Control Conference, 2003., vol. 5. IEEE, 2003, pp. 3750–3755.
    [3] M. F. Chowdhury, M. R. A. Biplob, and J. Uddin, “Real time traffic density measurement using computer vision and dy- namic traffic control,” in 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd In- ternational Conference on Imaging, Vision & Pattern Recognition (icIVPR). IEEE, 2018, pp. 353–356.
    [4] P. Agarwal, P. Matta, and S. Sharma, “Analysis based traffic flow control decision using iot sensors,” Materials Today: Proceedings, vol. 46, pp. 10 707–10 711, 2021.
    [5] L. Q. P. Vu, M. L. Le, and N. M. T. Tran, “Uoc luong mat do giao thong dua vao hoc sau va dien tich,” The conference on information technology and its applications, pp. 164–171, 2019.
    [6] H. Lee, J. Lee, and Y. Chung, “Traffic density estimation using vehicle sensor data,” Journal of Intelligent Transportation Systems, vol. 26, no. 6, pp. 675–689, 2021.
    [7] D. Prasad, K. Kapadni, A. Gadpal, M. Visave, and K. Sultan- pure, “Hog, lbp and svm based traffic density estimation at intersection,” in 2019 IEEE Pune Section International Confer- ence (PuneCon). IEEE, 2019, pp. 1–5.
    [8] D. B. Sam, S. Surya, and R. V. Babu, “Switching convolutional neural network for crowd counting,” in 2017 IEEE Conference on Computer Vision.
    [9] V. A. Sindagi and V. M. Patel, “Generating high-quality crowd density maps using contextual pyramid cnns,” in Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 1861–1870.
    [10] Y. Zhang, D. Zhou, S. Chen, S. Gao, and Y. Ma,  “Single- image crowd counting via multi-column convolutional neural network,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 589–597.
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Đã Xuất bản

Jun 30, 2023
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Cách trích dẫn
Mien, D. P., T. T. Vu, và N. V. Si. “Estimating Traffic Density in Uncertain Environment: A Case Study of Danang, Vietnam”. Tạp Chí Khoa học Và Công nghệ - Đại học Đà Nẵng, vol 21, số p.h 6.2, Tháng Sáu 2023, tr 33-38, doi:10.31130/ud-jst.2023.045E.

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