Estimating Traffic Density In Real-Time Using Enhanced Deep Learning Model

  • Kirti Singh
  • Dr. Deepak Sharma
Keywords: Intelligent Transportation System, YOLO, Traffic Density, Deep Learning, Vehicle recognition.

Abstract

In the field of transportation where cars are becoming more and more common metropolitan areas are experiencing an increase in traffic congestion. Using advanced technologies in Intelligent Transport Systems (ITS) has emerged as a viable solution to address this. ITS essentials like vehicle detection and counting help with traffic control and urban planning. Using current developments in Deep Learning (DL), especially with regard to the You Only Look Once (YOLO) model. By utilising object identification capabilities, the model precisely counts cars inside designated locations helping to identify congested zones and peak traffic hours. This work provides a real-time traffic density estimation technique. The work optimises the YOLOv8 model for better performance and uses the COCO benchmark dataset for vehicle detection. Through comprehensive performance evaluation including learning curve analysis, confusion matrix assessment and performance metrics evaluation the model demonstrates high accuracy and generalization capability. Inference and generalisation on test data demonstrate real-world applicability and demonstrate the model's efficacy in real-world circumstances. Inference and generalisation on test data demonstrate real-world applicability and demonstrate the model's efficacy in real-world circumstances.

Author Biographies

Kirti Singh

Research Scholar 

Dr. Deepak Sharma

Professor, Head Of Department, MONAD University, Hapur U.P.  

References

1. Huu-Huy, N. (2023). Vehicle-detection-based traffic density estimation at road intersections. International Journal of Open Information Technologies, 11(7), 39-46.
2. Ghosh, B., & Dauwels, J. (2022). Comparison of different Bayesian methods for estimating error bars with incident duration prediction. Journal of Intelligent Transportation Systems, 26(4), 420-431.
3. Xiang, X., Zhai, M., Lv, N., & El Saddik, A. (2018). Vehicle counting based on vehicle detection and tracking from aerial videos. Sensors, 18(8), 2560.
4. Guerrieri, M., & Parla, G. (2021). Deep learning and yolov3 systems for automatic traffic data measurement by moving car observer technique. Infrastructures, 6(9), 134.
5. Ma, R., Zhang, Z., Dong, Y., & Pan, Y. (2020). Deep learning based vehicle detection and classification methodology using strain sensors under bridge deck. Sensors, 20(18), 5051.
6. Berg, A., Ahlberg, J., & Felsberg, M. (2015, August). A thermal object tracking benchmark. In 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (pp. 1-6). IEEE.
7. Mittal, U., Srivastava, S., & Chawla, P. (2019). Object detection and classification from thermal images using region based convolutional neural network. Journal of Computer Science, 15(7), 961-971.
8. Zhu, J., Li, X., Jin, P., Xu, Q., Sun, Z., & Song, X. (2020). Mme-yolo: Multi-sensor multi-level enhanced yolo for robust vehicle detection in traffic surveillance. Sensors, 21(1), 27.
9. Hu, X., Wei, Z., & Zhou, W. (2021, March). A video streaming vehicle detection algorithm based on YOLOv4. In 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) (Vol. 5, pp. 2081-2086). IEEE.
10. Pan, Y. A., Guo, J., Chen, Y., Li, S., & Li, W. (2022). Incorporating traffic flow model into a deep learning method for traffic state estimation: A hybrid stepwise modeling framework. Journal of Advanced Transportation, 2022.
11. Kannan, K. S., & Parimyndhan, V. (2023, March). Modelling and Estimating of VaR Through the GARCH Model. In International Conference on Advanced Engineering, Technology and Applications (pp. 324-334). Cham: Springer Nature Switzerland.
12. Ma, B., Hua, Z., Wen, Y., Deng, H., Zhao, Y., Pu, L., & Song, H. (2024). Using an improved lightweight YOLOv8 model for real-time detection of multi-stage apple fruit in complex orchard environments. Artificial Intelligence in Agriculture.
13. Kashyap, A. A., Raviraj, S., Devarakonda, A., Nayak K, S. R., KV, S., & Bhat, S. J. (2022). Traffic flow prediction models–A review of deep learning techniques. Cogent Engineering, 9(1), 2010510.
14. Kutlimuratov, A., Khamzaev, J., Kuchkorov, T., Anwar, M. S., & Choi, A. (2023). Applying enhanced real-time monitoring and counting method for effective traffic management in tashkent. Sensors, 23(11), 5007.
15. Sayed, S. A., Abdel-Hamid, Y., & Hefny, H. A. (2023). Artificial intelligence-based traffic flow prediction: a comprehensive review. Journal of Electrical Systems and Information Technology, 10(1), 13.
16. Wassie, G., Ding, J., & Wondie, Y. (2023). Traffic prediction in SDN for explainable QoS using deep learning approach. Scientific Reports, 13(1), 20607.
Published
2024-01-25
How to Cite
Kirti Singh, & Dr. Deepak Sharma. (2024). Estimating Traffic Density In Real-Time Using Enhanced Deep Learning Model. Revista Electronica De Veterinaria, 25(1), 767-774. https://doi.org/10.69980/redvet.v25i1.705
Section
Articles