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Original Article
AI-Driven Road Expansion Recommendation System Based on Vehicle Traffic Patterns
P Nithish Kumar Reddy1
Narala Jahnavi Reddy2
Dr. Ramya G Franklin3
1 2 3 Department of Computer Science Engineering, Sathyabama Institute of Science and Technology Chennai, Tamilnadu, India.
Published Online: March-April 2026
Pages: 66-73
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20260602010
References
1. Y. Lv, Y. Duan, W. Kang, Z. Li, and F.-Y. Wang, "Traffic Flow Prediction with Big Data: Deep Learning Approach," IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 2, pp. 865-873, 2015. DOI: 10.1109/TITS.2014.2345663.
2. J. Zhang, Y. Zheng, and D. Qi, "Deep Spatio-Temporal Residual Networks of Citywide Crowd Flows Prediction," AAAI Conference on Artificial Intelligence, vol. 31, no. 1, pp. 1655-1661, 2017. DOI: 10.1609/aaai.v31i1.10735.
3. A. Farhadi, R. Redmon, and J. Joseph, "YOLOv3: A New Improvement," arXiv preprint, 2018. DOI: 10.48550/arXiv.1804.02767.
4. A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, "About 'YOLOv4: Optimal Speed and Accuracy of Object Detection'," arXiv preprint, 2020. DOI: 10.48550/arXiv.2004.10934.
5. C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, "YOLOv7: Bag-of-Freebies Trainable Sets New State-of-the-Art," arXiv preprint, 2022. DOI: 10.48550/arXiv.2207.02696.
6. X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning of Image Recognition," in Proceedings of CVPR, 2016, pp. 770-778. DOI: 10.1109/CVPR.2016.90.
7. A. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft, "Simple Online and Real- Time Tracking," in IEEE International Conference on Image Processing, 2016, pp. 3464-3468. DOI: 10.1109/ICIP.2016.7533003.
8. L. Zheng, H. Zhang, S. Sun, et al., "DeepSORT: Multi-Object Tracking in City Traffic by Deep Learning," Sensors, vol. 21, no. 1, p. 302, 2021. DOI: 10.3390/s21010302.
9. X. Ma, Z. Tao, Y. Wang, H. Yu, and Y. Guo, "Long Short-Term Memory Neural Network of Traffic Speed Prediction," Transportation Research Part C, vol. 54, pp. 187-197, 2015. DOI: 10.1016/j.trc.2015.03.014.
10. D.-H. Kim, B. Kim, and S. Kang, "Prediction of Short-Term Traffic Flow with Deep Neural Networks," KSCE Journal of Civil Engineering, vol. 22, no. 4, pp. 1227-1233, 2018. DOI: 10.1007/s12205-017-2000-3.
11. T. Tian and L. Pan, "Predicting Short-Term Traffic Flow by Long Short- Term Memory Recurrent Neural Network," in IEEE International Smart Cities Conference (SmartCity), 2015, pp. 153-158. DOI: 10.1109/SMARTCITIES.2015.10002.
12. B. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft, "Simple Online and Real-Time Tracking," in IEEE International Conference on Image Processing, 2016, pp. 3464-3468. DOI: 10.1109/ICIP.2016.7533003.
13. A. Farhadi, R. Redmon, and J. Joseph, "YOLOv3: A New Improvement," arXiv preprint, 2018. DOI: 10.48550/arXiv.1804.02767.
14. H. Qu, Q. Zheng, M. Msahli, G. Memmi, M. Qiu, and J. Lu, "Topological Graph Convolutional Network-Based Urban Traffic Flow and Density Prediction," IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 7, pp. 4560-4569, 2021. DOI: 10.1109/TITS.2020.2974939.
15. S. Zhang, Y. Guo, P. Zhao, C. Zheng, and X. Chen, "Graph-Based Temporal Attention Framework to Multi-Sensor Traffic Flow Forecasting," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 7743-7758, 2021. DOI: 10.1109/TITS.2021.3072109.
2. J. Zhang, Y. Zheng, and D. Qi, "Deep Spatio-Temporal Residual Networks of Citywide Crowd Flows Prediction," AAAI Conference on Artificial Intelligence, vol. 31, no. 1, pp. 1655-1661, 2017. DOI: 10.1609/aaai.v31i1.10735.
3. A. Farhadi, R. Redmon, and J. Joseph, "YOLOv3: A New Improvement," arXiv preprint, 2018. DOI: 10.48550/arXiv.1804.02767.
4. A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, "About 'YOLOv4: Optimal Speed and Accuracy of Object Detection'," arXiv preprint, 2020. DOI: 10.48550/arXiv.2004.10934.
5. C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, "YOLOv7: Bag-of-Freebies Trainable Sets New State-of-the-Art," arXiv preprint, 2022. DOI: 10.48550/arXiv.2207.02696.
6. X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning of Image Recognition," in Proceedings of CVPR, 2016, pp. 770-778. DOI: 10.1109/CVPR.2016.90.
7. A. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft, "Simple Online and Real- Time Tracking," in IEEE International Conference on Image Processing, 2016, pp. 3464-3468. DOI: 10.1109/ICIP.2016.7533003.
8. L. Zheng, H. Zhang, S. Sun, et al., "DeepSORT: Multi-Object Tracking in City Traffic by Deep Learning," Sensors, vol. 21, no. 1, p. 302, 2021. DOI: 10.3390/s21010302.
9. X. Ma, Z. Tao, Y. Wang, H. Yu, and Y. Guo, "Long Short-Term Memory Neural Network of Traffic Speed Prediction," Transportation Research Part C, vol. 54, pp. 187-197, 2015. DOI: 10.1016/j.trc.2015.03.014.
10. D.-H. Kim, B. Kim, and S. Kang, "Prediction of Short-Term Traffic Flow with Deep Neural Networks," KSCE Journal of Civil Engineering, vol. 22, no. 4, pp. 1227-1233, 2018. DOI: 10.1007/s12205-017-2000-3.
11. T. Tian and L. Pan, "Predicting Short-Term Traffic Flow by Long Short- Term Memory Recurrent Neural Network," in IEEE International Smart Cities Conference (SmartCity), 2015, pp. 153-158. DOI: 10.1109/SMARTCITIES.2015.10002.
12. B. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft, "Simple Online and Real-Time Tracking," in IEEE International Conference on Image Processing, 2016, pp. 3464-3468. DOI: 10.1109/ICIP.2016.7533003.
13. A. Farhadi, R. Redmon, and J. Joseph, "YOLOv3: A New Improvement," arXiv preprint, 2018. DOI: 10.48550/arXiv.1804.02767.
14. H. Qu, Q. Zheng, M. Msahli, G. Memmi, M. Qiu, and J. Lu, "Topological Graph Convolutional Network-Based Urban Traffic Flow and Density Prediction," IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 7, pp. 4560-4569, 2021. DOI: 10.1109/TITS.2020.2974939.
15. S. Zhang, Y. Guo, P. Zhao, C. Zheng, and X. Chen, "Graph-Based Temporal Attention Framework to Multi-Sensor Traffic Flow Forecasting," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 7743-7758, 2021. DOI: 10.1109/TITS.2021.3072109.
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