ARCHIVES
Original Article
Hybrid Adaptive Pressure-Based Dynamic Traffic Signal Control at Urban Road Junctions
Mukta Ranjan Singha1
Pranjal Das2
1 2 Department of Computer Applications, Girijananda Chowdhury University, Assam Azara, Guwahati, India.
Published Online: January-April 2026
Pages: 539-546
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260501061References
1. M. Beckmann, C. B. McGuire, and C. B. Winsten, Studies in the Economics of Transportation. New Haven, CT, USA: Yale Univ. Press,
1956.
2. Y. Sheffi, Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods. Englewood Cliffs, NJ, USA:
Prentice-Hall, 1985.
3. R. K. Ahuja, T. L. Magnanti, and J. B. Orlin, Network Flows: Theory, Algorithms, and Applications. Upper Saddle River, NJ, USA: Prentice-
Hall, 1993.
4. C. F. Daganzo, “Queue spillovers in transportation networks with a route choice,” Transportation Science, vol. 32, no. 1, pp. 3–11, Feb.
1998.5. H. S. Mahmassani, “Dynamic network traffic assignment and simulation methodology for advanced system management applications,”
Networks and Spatial Economics, vol. 1, no. 3–4, pp. 267–292, 2001.
6. F.-Y. Wang, “Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications,” IEEE
Intelligent Systems, vol. 25, no. 4, pp. 52–60, July–Aug. 2010.
7. S. Porta, P. Crucitti, and V. Latora, “The network analysis of urban streets: A primal approach,” Environment and Planning B: Planning
and Design, vol. 33, no. 5, pp. 705–725, 2006.
8. M. Batty, “The size, scale, and shape of cities,” Science, vol. 319, no. 5864, pp. 769–771, Feb. 2008.
9. M. E. J. Newman, Networks: An Introduction. Oxford, U.K.: Oxford Univ. Press, 2010.
10. T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolution networks,” in Proc. Int. Conf. Learning Representations
(ICLR), 2017.
11. Y. Wang, Z. Li, and L. Li, “Traffic flow prediction with graph convolution networks,” IEEE Transactions on Intelligent Transportation
Systems, vol. 20, no. 10, pp. 1–12, 2019.
12. R. Cohen and S. Havlin, Complex Networks: Structure, Robustness and Function. Cambridge, U.K.: Cambridge Univ. Press, 2010.
13. M. R. Singha, B. Kalita, “ Mapping Mobile Phone Network onto Urban Traffic Network “, Proceeding of International Multi Conference
of Computer Engineers and Scientists 2013“, Vol I, ISBN: 978-988-19251-8-3, 13-15 March 2013, Hongkong.
14. M. R. Singha, B. Kalita, “Using Mobile Phone Network for Urban Traffic Management” International Journal of Computer Applications,
(0975-8887), Volume 65-No.2, March 2013, Pp 12-17.
15. Tom Thomas, Wendy Weijermars, Eric Van Berkum, “ Prediction of Urban Volumes in Single Time Series”, IEEE Transactions on
Intelligent Transportation Systems, Vol 11 No. 1 March 2010.
16. Asad Salkham, Raymond Cunninggham, Anurag Garg, Vinny Cahil, “ A Collaborative Reinforcement Learning Approach to Urban Traffic
Control Optimisation”, IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology”, 2008.
17. S. O. Fadare , B.B, Ayantoyinbo, “ A Study of the effects of Road Traffic Congestion on Freight Movement in Laos Metropolos”, European
Journal of Social Sciences, Vol 16 no. 3 , 2010.
18. Adekunte J Adermo, Tolu I. Atomode, “Traffic Congestion at Road Intersections in Ilorin, Nigeria”, Austrian Journal of Basic and Applied
Sciences, 5(9), 1439-1448, 2011.
19. Kenedy Aliila Greyson, “Anticipated Traffic Jam Locations Using Inlet and Outlet Factors Analysis”, Int. J. Emerg Sci. 2(2), 193-203,
June 2012. ISSN 2222-4254.
20. Guillaume Leduc, “Road Traffic Data: Collection Methods and Applications” , JRC 47967 – 2008.
21. Hu Chunchun, Luo Nianxue, Yan Xiaohong, and Shi Wenzhong, “Traffic Flow Data Mining and Evaluation Based on Fuzzy
ClusteringTechniques”, International Journal of Fuzzy Systems, Vol. 13, No. 4, December 2011
22. Ryota Ayaki, Hideki Shimada, Kenya Sato, “A Proposal of Sensor Data Collection System Using Mobile Relay Nodes”, Wireless Sensor
Network, 2012, 4, 1-7
23. Xielin Liu, Feng-Shang Wu, and Wen-Lin Chu, “Diffusion of Mobile Telephony in China: Drivers and Forecasts”, IEEE Transactions
on Engineering Management, VOL. 59, NO. 2, MAY 2012
24. Bhaskara Tejaswi E , Ashish Verma, “Public Transport System in Guwahati City “, Indian Journal of Transport Management, July =
September 2010,Pp210-221.
25. Tanveer Ahmed , Hao Liu , Vikash V. Gayah A Max-Pressure framework to prioritize transit and high occupancy vehicles Transportation
Research Part C: Emerging Technologies Volume 166, September 2024, 104795
26. P. Varaiya, “Max pressure control of a network of signalized intersections,” Transportation Research Part C: Emerging Technologies, vol.
36, pp. 177–195, 2013.
27. A. Kouvelas, J. Lioris, S. Fayazi, and P. Varaiya, “Maximum pressure controller for stabilizing queues in signalized arterial networks,”
Transportation Research Record, 2014.
28. Federal Highway Administration (FHWA), Traffic Signal Timing Manual, U.S. Department of Transportation, 2015.
29. M. A. Wiering, “Multi-agent reinforcement learning for traffic light control,” Machine Learning, vol. 45, pp. 1–15, 2000.
30. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, real-time object detection,” Proceedings of CVPR,
2016.
31. C. P. Pappis and E. H. Mamdani, “A fuzzy logic controller for a traffic junction,” IEEE Transactions on Systems, Man, and Cybernetics,
vol. 7, no. 10, pp. 707–717, 1977.
32. S. Teklu, B. Sumalee, and D. Watling, “A genetic algorithm approach for optimizing traffic signal timing,” Transportation Research Part
C, vol. 15, pp. 115–135, 2007.
33. C.Cai and M. Wei, “Adaptive urban traffic signal control based on enhanced deep reinforcement learning,”
Scientific Reports, vol. 14, 2024.
34. L. Wang et al., “Adaptive traffic signal control method based on offline reinforcement learning,” Applied Sciences, vol. 14, no. 22, 2024.
35. P. Michailidis et al., “Traffic signal control via reinforcement learning: A review on applications and innovations,” Infrastructures, 2025.
36. M. Li et al., “Federated deep reinforcement learning-based urban traffic signal optimal control,”
Scientific Reports, 2025.
37. Y. Zheng et al., “Pri-DDQN: learning adaptive traffic signal control strategy through a hybrid agent,”
Complex & Intelligent Systems, 2024.
38. “Dynamic traffic signal control for heterogeneous traffic using max-pressure and reinforcement learning,”
Expert Systems with Applications, 2024.
39. K. Cao et al.,“Optimization control of adaptive traffic signal with deep reinforcement learning,”
Electronics, 2024.
40. L. Duan and H. Zhao, “Adaptive signal control considering carbon emissions using deep reinforcement learning,” Electronics, 2025.
1956.
2. Y. Sheffi, Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods. Englewood Cliffs, NJ, USA:
Prentice-Hall, 1985.
3. R. K. Ahuja, T. L. Magnanti, and J. B. Orlin, Network Flows: Theory, Algorithms, and Applications. Upper Saddle River, NJ, USA: Prentice-
Hall, 1993.
4. C. F. Daganzo, “Queue spillovers in transportation networks with a route choice,” Transportation Science, vol. 32, no. 1, pp. 3–11, Feb.
1998.5. H. S. Mahmassani, “Dynamic network traffic assignment and simulation methodology for advanced system management applications,”
Networks and Spatial Economics, vol. 1, no. 3–4, pp. 267–292, 2001.
6. F.-Y. Wang, “Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications,” IEEE
Intelligent Systems, vol. 25, no. 4, pp. 52–60, July–Aug. 2010.
7. S. Porta, P. Crucitti, and V. Latora, “The network analysis of urban streets: A primal approach,” Environment and Planning B: Planning
and Design, vol. 33, no. 5, pp. 705–725, 2006.
8. M. Batty, “The size, scale, and shape of cities,” Science, vol. 319, no. 5864, pp. 769–771, Feb. 2008.
9. M. E. J. Newman, Networks: An Introduction. Oxford, U.K.: Oxford Univ. Press, 2010.
10. T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolution networks,” in Proc. Int. Conf. Learning Representations
(ICLR), 2017.
11. Y. Wang, Z. Li, and L. Li, “Traffic flow prediction with graph convolution networks,” IEEE Transactions on Intelligent Transportation
Systems, vol. 20, no. 10, pp. 1–12, 2019.
12. R. Cohen and S. Havlin, Complex Networks: Structure, Robustness and Function. Cambridge, U.K.: Cambridge Univ. Press, 2010.
13. M. R. Singha, B. Kalita, “ Mapping Mobile Phone Network onto Urban Traffic Network “, Proceeding of International Multi Conference
of Computer Engineers and Scientists 2013“, Vol I, ISBN: 978-988-19251-8-3, 13-15 March 2013, Hongkong.
14. M. R. Singha, B. Kalita, “Using Mobile Phone Network for Urban Traffic Management” International Journal of Computer Applications,
(0975-8887), Volume 65-No.2, March 2013, Pp 12-17.
15. Tom Thomas, Wendy Weijermars, Eric Van Berkum, “ Prediction of Urban Volumes in Single Time Series”, IEEE Transactions on
Intelligent Transportation Systems, Vol 11 No. 1 March 2010.
16. Asad Salkham, Raymond Cunninggham, Anurag Garg, Vinny Cahil, “ A Collaborative Reinforcement Learning Approach to Urban Traffic
Control Optimisation”, IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology”, 2008.
17. S. O. Fadare , B.B, Ayantoyinbo, “ A Study of the effects of Road Traffic Congestion on Freight Movement in Laos Metropolos”, European
Journal of Social Sciences, Vol 16 no. 3 , 2010.
18. Adekunte J Adermo, Tolu I. Atomode, “Traffic Congestion at Road Intersections in Ilorin, Nigeria”, Austrian Journal of Basic and Applied
Sciences, 5(9), 1439-1448, 2011.
19. Kenedy Aliila Greyson, “Anticipated Traffic Jam Locations Using Inlet and Outlet Factors Analysis”, Int. J. Emerg Sci. 2(2), 193-203,
June 2012. ISSN 2222-4254.
20. Guillaume Leduc, “Road Traffic Data: Collection Methods and Applications” , JRC 47967 – 2008.
21. Hu Chunchun, Luo Nianxue, Yan Xiaohong, and Shi Wenzhong, “Traffic Flow Data Mining and Evaluation Based on Fuzzy
ClusteringTechniques”, International Journal of Fuzzy Systems, Vol. 13, No. 4, December 2011
22. Ryota Ayaki, Hideki Shimada, Kenya Sato, “A Proposal of Sensor Data Collection System Using Mobile Relay Nodes”, Wireless Sensor
Network, 2012, 4, 1-7
23. Xielin Liu, Feng-Shang Wu, and Wen-Lin Chu, “Diffusion of Mobile Telephony in China: Drivers and Forecasts”, IEEE Transactions
on Engineering Management, VOL. 59, NO. 2, MAY 2012
24. Bhaskara Tejaswi E , Ashish Verma, “Public Transport System in Guwahati City “, Indian Journal of Transport Management, July =
September 2010,Pp210-221.
25. Tanveer Ahmed , Hao Liu , Vikash V. Gayah A Max-Pressure framework to prioritize transit and high occupancy vehicles Transportation
Research Part C: Emerging Technologies Volume 166, September 2024, 104795
26. P. Varaiya, “Max pressure control of a network of signalized intersections,” Transportation Research Part C: Emerging Technologies, vol.
36, pp. 177–195, 2013.
27. A. Kouvelas, J. Lioris, S. Fayazi, and P. Varaiya, “Maximum pressure controller for stabilizing queues in signalized arterial networks,”
Transportation Research Record, 2014.
28. Federal Highway Administration (FHWA), Traffic Signal Timing Manual, U.S. Department of Transportation, 2015.
29. M. A. Wiering, “Multi-agent reinforcement learning for traffic light control,” Machine Learning, vol. 45, pp. 1–15, 2000.
30. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, real-time object detection,” Proceedings of CVPR,
2016.
31. C. P. Pappis and E. H. Mamdani, “A fuzzy logic controller for a traffic junction,” IEEE Transactions on Systems, Man, and Cybernetics,
vol. 7, no. 10, pp. 707–717, 1977.
32. S. Teklu, B. Sumalee, and D. Watling, “A genetic algorithm approach for optimizing traffic signal timing,” Transportation Research Part
C, vol. 15, pp. 115–135, 2007.
33. C.Cai and M. Wei, “Adaptive urban traffic signal control based on enhanced deep reinforcement learning,”
Scientific Reports, vol. 14, 2024.
34. L. Wang et al., “Adaptive traffic signal control method based on offline reinforcement learning,” Applied Sciences, vol. 14, no. 22, 2024.
35. P. Michailidis et al., “Traffic signal control via reinforcement learning: A review on applications and innovations,” Infrastructures, 2025.
36. M. Li et al., “Federated deep reinforcement learning-based urban traffic signal optimal control,”
Scientific Reports, 2025.
37. Y. Zheng et al., “Pri-DDQN: learning adaptive traffic signal control strategy through a hybrid agent,”
Complex & Intelligent Systems, 2024.
38. “Dynamic traffic signal control for heterogeneous traffic using max-pressure and reinforcement learning,”
Expert Systems with Applications, 2024.
39. K. Cao et al.,“Optimization control of adaptive traffic signal with deep reinforcement learning,”
Electronics, 2024.
40. L. Duan and H. Zhao, “Adaptive signal control considering carbon emissions using deep reinforcement learning,” Electronics, 2025.
Related Articles
2026
Artificial Intelligence in Learning and Teaching
2026
Admin Assist: An AI – Driven Configuration and Orchestration for Enterprise Application
2026
Enhancing Blood Group Identification using pigeon inspired optimization: An Innovative Approach
2026
Eco-Genius: Power Up Smart, Power Down Waste
2026
Crowd-Sourced Disaster Response and Rescue Assistant
2026