Department of Computer Science and Applications, Faculty of Science and Humanities, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tamil Nadu, India.
Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India.
Faculty of Educational Sciences, Al-Ahliyya Amman University, Amman, 19328, Jordan, Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.
This research work presents a novel Multi-Agent Reinforcement Learning (MARL) model for optimising urban traffic flow (UTF) and resource allocation. The proposed MARL integrates specialised agents for traffic prediction, intersection control, resource allocation, and incident detection, all of which are coordinated through a centralised command structure. This work formulates the traffic management problem as a dual-objective optimisation task, simultaneously minimising congestion and optimising resource allocation. The model employs Proximal Policy Optimization (PPO) for training agents, enabling efficient real-time decision-making and adaptation to dynamic traffic conditions. This MARL's performance was evaluated using the Simulation of Urban Mobility (SUMO) traffic simulator, which features a network comprising 25 road segments and 8 resource allocation regions. Simulation results validate significant improvements over traditional methods, including a 47.4% reduction in average travel time, a 40.2% increase in network throughput, and a 35.7% improvement in resource utilisation. During peak hours, the model achieved a 51.2% reduction in queue lengths and maintained a 92.4% resource utilisation rate under increased demand. The Incident Detection System (IDS) proved 94.5% accuracy with an average response time of 4.3 minutes, significantly outperforming baseline approaches. The empirical results of this study indicate that the proposed MARL provides a robust and scalable solution for UTF, effectively balancing traffic flow optimisation with resource allocation efficiency. The system's ability to maintain performance under varying traffic conditions and incident scenarios recommends its viability for real-world implementation in smart city traffic management systems.
A. Ait Ouallane, A. Bakali, A. Bahnasse, S. Broumi, and M. Talea, “Fusion of engineering insights and emerging trends: Intelligent urban traffic management system,” Information Fusion, vol. 88, pp. 218–248, Dec. 2022, doi: 10.1016/j.inffus.2022.07.020.
M. Nama et al., “Machine learning‐based traffic scheduling techniques for intelligent transportation system: Opportunities and challenges,” International Journal of Communication Systems, vol. 34, no. 9, Apr. 2021, doi: 10.1002/dac.4814.
A. Alhussen and A. S. Ansari, “Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks (MANETS),” Computers, Materials & Continua, vol. 79, no. 2, pp. 1903–1923, 2024, doi: 10.32604/cmc.2024.049260.
N. T. F. E, V. P. A, S. K. Shanmugam, K. K. V, S. Sengan, and A. M. B. C, “Adaptive Approach to Anomaly Detection in Internet of Things Using Autoencoders and Dynamic Thresholds,” Journal of Machine and Computing, pp. 1–10, Jan. 2024, doi: 10.53759/7669/jmc202404001.
P. Razzaghi et al., “A survey on reinforcement learning in aviation applications,” Engineering Applications of Artificial Intelligence, vol. 136, p. 108911, Oct. 2024, doi: 10.1016/j.engappai.2024.108911.
N. Nezamoddini and A. Gholami, “A Survey of Adaptive Multi-Agent Networks and Their Applications in Smart Cities,” Smart Cities, vol. 5, no. 1, pp. 318–347, Mar. 2022, doi: 10.3390/smartcities5010019.
H. M. Ali, J. J. J, S. G, T. Palanisamy, V. Rachapudi, and S. Sengan, “Operating Cash Flow Ranking Using Data Envelopment Analysis with Network Security Driven Blockchain Model,” Journal of Machine and Computing, pp. 1839–1851, Jul. 2025, doi: 10.53759/7669/jmc202505144.
S. Abirami, M. Pethuraj, M. Uthayakumar, and P. Chitra, “A systematic survey on big data and artificial intelligence algorithms for intelligent transportation system,” Case Studies on Transport Policy, vol. 17, p. 101247, Sep. 2024, doi: 10.1016/j.cstp.2024.101247.
Omamode Henry Orieno, Ndubuisi Leonard Ndubuisi, Valentine Ikenna Ilojianya, Preye Winston Biu, and Beryl Odonkor, “THE FUTURE OF AUTONOMOUS VEHICLES IN THE U.S. URBAN LANDSCAPE: A REVIEW: ANALYZING IMPLICATIONS FOR TRAFFIC, URBAN PLANNING, AND THE ENVIRONMENT,” Engineering Science & Technology Journal, vol. 5, no. 1, pp. 43–64, Jan. 2024, doi: 10.51594/estj.v5i1.721.
H. B. Faheem, A. M. E. Shorbagy, and M. E. Gabr, “Impact Of Traffic Congestion on Transportation System: Challenges and Remediations - A review,” Mansoura Engineering Journal, vol. 49, no. 2, Jan. 2024, doi: 10.58491/2735-4202.3191.
S. H. Nowfal, V. B. Sadu, S. Sengab, R. G, A. N. R, and S. K, “Genetic Algorithms for Optimized Selection of Biodegradable Polymers in Sustainable Manufacturing Processes,” Journal of Machine and Computing, pp. 563–574, Jul. 2024, doi: 10.53759/7669/jmc202404054.
H. M. A. Ghanimi, A. K, H. P. A, S. Sengan, B. P. Prakash, and R. K. Bommisetti, “An Ensemble Cognitive Model for Stroke Prediction Using Unstructured Health Information Powered by Machine Learning,” Journal of Machine and Computing, pp. 611–621, Jan. 2025, doi: 10.53759/7669/jmc202505048.
A. Agrahari, M. M. Dhabu, P. S. Deshpande, A. Tiwari, M. A. Baig, and A. D. Sawarkar, “Artificial Intelligence-Based Adaptive Traffic Signal Control System: A Comprehensive Review,” Electronics, vol. 13, no. 19, p. 3875, Sep. 2024, doi: 10.3390/electronics13193875.
A. A. Musa, S. I. Malami, F. Alanazi, W. Ounaies, M. Alshammari, and S. I. Haruna, “Sustainable Traffic Management for Smart Cities Using Internet-of-Things-Oriented Intelligent Transportation Systems (ITS): Challenges and Recommendations,” Sustainability, vol. 15, no. 13, p. 9859, Jun. 2023, doi: 10.3390/su15139859.
G. Vadivel, M. J. M. Hussain, S. V. Tresa, and . S., “Smart Transportation Systems: Iot-Connected Wireless Sensor Networks For Traffic Congestion Management,” International Journal Of Advances In Signal And Image Sciences, vol. 9, no. 1, pp. 40–49, Jun. 2023, doi: 10.29284/ijasis.9.1.2023.40-49.
F. T. Ayasrah, N. S. Alsharafa, S. S, S. B, S. Sengan, and K. N, “Strategizing Low Carbon Urban Planning Through Environmental Impact Assessment by Artificial Intelligence Driven Carbon Foot-print Forecasting,” Journal of Machine and Computing, pp. 1140–1151, Oct. 2024, doi: 10.53759/7669/jmc202404105.
Spring, N. (2024). Multi-Agent Systems in Vehicular Edge Computing: A Communication-Centric Approach to Task Handovers (Doctoral dissertation, Technische Universität Wien).
T. Ahmed, H. Liu, and V. V. Gayah, “C-MP: A decentralized adaptive-coordinated traffic signal control using the Max Pressure framework,” Transportation Research Part B: Methodological, vol. 200, p. 103308, Oct. 2025, doi: 10.1016/j.trb.2025.103308.
A. G. Ismaeel et al., “Enhancing Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function,” Sustainability, vol. 15, no. 19, p. 14441, Oct. 2023, doi: 10.3390/su151914441.
C. Chen et al., “Toward A Thousand Lights: Decentralized Deep Reinforcement Learning for Large-Scale Traffic Signal Control,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, pp. 3414–3421, Apr. 2020, doi: 10.1609/aaai.v34i04.5744.
A. M. Ali, M. A. Ngadi, I. I. Al_Barazanchi, and P. S. JosephNg, “Intelligent Traffic Model for Unmanned Ground Vehicles Based on DSDV-AODV Protocol,” Sensors, vol. 23, no. 14, p. 6426, Jul. 2023, doi: 10.3390/s23146426.
K. S. Mohsin, J. Mettu, C. Madhuri, G. Usharani, S. N, and P. Yellamma, “Enhancing Urban Traffic Management Through Hybrid Convolutional and Graph Neural Network Integration,” Journal of Machine and Computing, pp. 360–370, Apr. 2024, doi: 10.53759/7669/jmc202404034.
H. M. A. Ghanimi, F. T. Ayasrah, V. C. Jadala, M. T C, B. K, and S. B, “An Innovative Artificial Intelligence Based Decision Making System for Public Health Crisis Virtual Reality Rehabilitation,” Journal of Machine and Computing, pp. 561–575, Jan. 2025, doi: 10.53759/7669/jmc202505044.
N. S. Alsharafa, S. R, R. J. Krishna, V. K. Sonthi, P. S M, and M. P, “Optimizing Building Energy Management with Deep Reinforcement Learning for Smart and Sustainable Infrastructure,” Journal of Machine and Computing, pp. 381–391, Apr. 2024, doi: 10.53759/7669/jmc202404036.
Haldorai, B. L. R, S. Murugan, and M. Balakrishnan, “Artificial Intelligence for Intelligent Spectrum Management Toward Futuristic Communication,” EAI/Springer Innovations in Communication and Computing, pp. 235–248, 2024, doi: 10.1007/978-3-031-53972-5_12.
CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Shaymaa H Nowfa, Revathi S, Kolluru Suresh Babu, Amarendra K, Abdul Muthalief Mohamed Anwar and Aseel Smerat;
Methodology: Shaymaa H Nowfa, Revathi S and Kolluru Suresh Babu;
Software: Amarendra K, Abdul Muthalief Mohamed Anwar and Aseel Smerat;
Data Curation: Shaymaa H Nowfa, Revathi S and Kolluru Suresh Babu;
Writing- Original Draft Preparation: Shaymaa H Nowfa, Revathi S, Kolluru Suresh Babu, Amarendra K, Abdul Muthalief Mohamed Anwar and Aseel Smerat;
Visualization: Shaymaa H Nowfa, Revathi S and Kolluru Suresh Babu;
Investigation: Amarendra K, Abdul Muthalief Mohamed Anwar and Aseel Smerat;
Supervision: Shaymaa H Nowfa, Revathi S and Kolluru Suresh Babu;
Validation: Amarendra K, Abdul Muthalief Mohamed Anwar and Aseel Smerat;
Writing- Reviewing and Editing: Shaymaa H Nowfa, Revathi S, Kolluru Suresh Babu, Amarendra K, Abdul Muthalief Mohamed Anwar and Aseel Smerat;
All authors reviewed the results and approved the final version of the manuscript.
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Revathi S
Department of Computer Science and Applications, Faculty of Science and Humanities, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tamil Nadu, India.
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Cite this article
Shaymaa H Nowfal, Revathi S, Kolluru Suresh Babu Amarendra K, Abdul Muthalief Mohamed Anwar and Aseel Smerat, “The Optimization of Traffic Flow and Resource Allocation in Urban Infrastructures Using Predictive Analytics and Multi Agent Systems”, Journal of Machine and Computing, vol.6, no.1, pp. 093-113, 2026, doi: 10.53759/7669/jmc202606008.
