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TraffiSense AI: A Quantum-Inspired Congestion Prediction and Signal Scheduling Assistant
Subject area: Science,Engineering and Technology · Area of research: IOT, AIML
Abstract
Traffic congestion remains one of the primary challenges in developing smart cities, primarily caused by rapid urban growth and infrastructure expansion. Existing intelligent traffic management systems often depend on cloud-based processing, which introduces latency, higher operational costs, and data security risks. To overcome these limitations, this paper introduces TrafficSense AI, an offline-first, quantum-inspired traffic optimization framework designed for efficient, real-time signal coordination without relying on cloud infrastructure. The system reformulates the traffic signal scheduling problem as a Quadratic Unconstrained Binary Optimization (QUBO) model and applies Quantum-Inspired Evolutionary Algorithms (QIEA) to identify near-optimal solutions. Additionally, a Reinforcement Learning (RL) controller dynamically adjusts signal timings based on live traffic conditions to improve flow and minimize congestion. Implemented using Python and the SUMO traffic simulator on realistic urban road networks, TrafficSense AI demonstrates a 21% increase in throughput and a 17?32% reduction in vehicle waiting times compared to conventional fixed-time systems, even under CPU-only local execution. With its privacy-preserving, scalable, and cloud-independent architecture, TrafficSense AI offers a promising foundation for future advancements such as multi-intersection coordination, Vehicle-to-Infrastructure (V2I) integration, and autonomous route optimization, paving the way for efficient and sustainable urban mobility.
Keywords
Quantum-Inspired Optimization, Smart Cities, Traffic Flow Management, Reinforcement Learning, QUBO, Intelligent Transportation Systems (ITS), SUMO.
References
[1] A. Lucas, “Ising formulations of many NP problems,” Frontiers in Physics, vol. 2, pp. 1–15, 2014.
[2] E. Farhi, J. Goldstone, and S. Gutmann, “A quantum approximate optimization algorithm,” arXiv preprint arXiv:1411.4028, 2014.
[3] A. Narimani and S. Mirjalili, “Quantum-inspired evolutionary algorithms: A review and analysis,” Journal of Computational Science, vol. 36, pp. 1–15, 2019.
[4] P. Date, D. Arthur, and F. Pusey, “Efficient classical optimization using a quantum-inspired algorithm,” IEEE Transactions on Quantum Engineering, vol. 2, pp. 1–12, 2021.
[5] S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” Science, vol. 220, no. 4598, pp. 671–680, 1983.
[6] M. F. R. Younes and M. Boukerche, “Intelligent traffic light control systems for smart cities: A survey,” IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 1982–2018, 2020.
[7] R. R. Rajawat, A. Gupta, and P. K. Mishra, “Adaptive traffic signal control for smart cities using reinforcement learning,” in Proc. IEEE Int. Conf. Smart City Innovations (SCI), 2022, pp. 112–118.
[8] S. R. Mousavi, M. Schukat, and E. Howley, “Traffic signal control using deep reinforcement learning,” in Proc. 29th Irish Signals and Systems Conference (ISSC), 2018, pp. 1–6.
[9] T. Chu, S. Wang, L. Codec, and J. M. Smith, “Multi-agent deep reinforcement learning for traffic signal control,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 6, pp. 2403–2414, 2020.
[10] B. Prencipe and R. L. Cruz, “Network traffic flow optimization using graph-based control,” IEEE/ACM Transactions on Networking, vol. 29, no. 2, pp. 689–701, 2021.
[11] H. N. Shami and R. C. Dorf, “Dynamic traffic signal timing optimization using real-time data,” Transportation Research Part C, vol. 134, pp. 103–114, 2022.
[12] “NetworkX: Network Analysis in Python,” NetworkX Project. [Online]. Available: https://networkx.org/
[13] “NumPy: Fundamental package for scientific computing,” NumPy Developers. [Online]. Available: https://numpy.org/
[14] “Matplotlib: Visualization library,” Matplotlib Developers. [Online]. Available: https://matplotlib.org/
[15] “Python Language Reference,” Python Software Foundation. [Online]. Available: https://www.python.org/
How to cite this paper
@article{1712030,
author = {Sakshi Yadav, Diksha Vasekar, Neha Dorge, Yash Yadav, Prof. Omkar Wadne},
title = {TraffiSense AI: A Quantum-Inspired Congestion Prediction and Signal Scheduling Assistant},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {5},
pages = {841-847},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712030.pdf},
abstract = {Traffic congestion remains one of the primary challenges in developing smart cities, primarily caused by rapid urban growth and infrastructure expansion. Existing intelligent traffic management systems often depend on cloud-based processing, which introduces latency, higher operational costs, and data security risks. To overcome these limitations, this paper introduces TrafficSense AI, an offline-first, quantum-inspired traffic optimization framework designed for efficient, real-time signal coordination without relying on cloud infrastructure. The system reformulates the traffic signal scheduling problem as a Quadratic Unconstrained Binary Optimization (QUBO) model and applies Quantum-Inspired Evolutionary Algorithms (QIEA) to identify near-optimal solutions. Additionally, a Reinforcement Learning (RL) controller dynamically adjusts signal timings based on live traffic conditions to improve flow and minimize congestion. Implemented using Python and the SUMO traffic simulator on realistic urban road networks, TrafficSense AI demonstrates a 21% increase in throughput and a 17?32% reduction in vehicle waiting times compared to conventional fixed-time systems, even under CPU-only local execution. With its privacy-preserving, scalable, and cloud-independent architecture, TrafficSense AI offers a promising foundation for future advancements such as multi-intersection coordination, Vehicle-to-Infrastructure (V2I) integration, and autonomous route optimization, paving the way for efficient and sustainable urban mobility.},
keywords = {Quantum-Inspired Optimization, Smart Cities, Traffic Flow Management, Reinforcement Learning, QUBO, Intelligent Transportation Systems (ITS), SUMO.},
month = {November},
doi = {https://doi.org/10.64388/IREV9I5-1712030}
}