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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
DOI: https://doi.org/10.64388/IREV9I5-1712030
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.
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}
}