Beyond sensors: IntelliSignal’s pap-integrated intelligence in traffic flow optimization
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Published version
Date
2024-03-11
Authors
Sreejith, K.
Mathi, Senthilkumar
Pradeep, Preeja
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Published Version
Abstract
The burgeoning growth of vehicular traffic, fuelled by rapid urbanization and an ever-expanding population, has resulted in congested road networks. Traditional and sensor-based adaptive traffic light management systems have shown commendable progress in some scenarios, but they suffer from inherent disadvantages that hinder their effectiveness and scalability. To combat these challenges, a dynamic and adaptable traffic control system is imperative to optimize traffic flow. The present paper explores the limitations of sensor-based adaptive traffic light management and advocates for integrating a novel algorithm to overcome the existing drawbacks. It proposes an intelligent traffic management algorithm called IntelliSignal that leverages the map service for fetching real-time traffic information to calculate the optimal green time and a penalty-based road selection to optimize traffic flow. The proposed IntelliSignal is designed to provide equal chances for all roads while prioritizing higher-density roads with more green time, effectively mitigating traffic congestion and improving overall transportation efficiency. It incorporates Q-learning, a reinforcement learning technique that enables the system to adapt and learn from traffic patterns. The proposed IntelliSignal’s performance is assessed through rigorous evaluations conducted on the simulation platform SUMO. The acquired results demonstrate substantial enhancements across various crucial metrics, including average waiting time, vehicle density, travel time, CO2 emissions, and queue length. Furthermore, the simulation results demonstrate that the proposed IntelliSignal algorithm exhibits a remarkable 30.52% increment in system throughput compared to the traditional approach. This significant enhancement underscores the efficacy of the proposed IntelliSignal in optimizing system performance and merits consideration for practical implementation.
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Keywords
IntelliSignal , Map service , Q-learning , Sensor-free traffic optimization , Sustainability , Traffic control , Road traffic , Sensors , Real-time systems , Heuristic algorithms , Adaptive systems , Vehicle driving , Q-learning , Sustainable development , Urban areas , Intelligent sensors
Citation
Sreejith, K., Mathi, S. and Pradeep, P. (2024) ‘Beyond sensors: Intellisignal’s map-integrated intelligence in traffic flow optimization’, IEEE Access, 12, pp. 39028–39040. Available at: https://doi.org/10.1109/ACCESS.2024.3375335