Traffic Control System for Smart City Using Image Processing

Bhardwaj, Vedansh; Rasamsetti, Yaswanth; Valsan, Vipina

doi:10.1007/978-981-16-7498-3_6

Cited by 4 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A self-adaptive traffic management plan is required because the current traffic system only provides fixed time-based traffic control. By assisting sensor networks with network mobility, image processing can help to establish an intelligent traffic control method for smart cities [3]. Intelligent traffic signal control systems, capable of controlling and monitoring real-time traffic scenarios, are also integrated into smart cities [4].…”

Section: Introductionmentioning

confidence: 99%

Beyond Sensors: IntelliSignal’s Map-Integrated Intelligence in Traffic Flow Optimization

Sreejith,

Mathi,

Pradeep

2024

IEEE Access

View full text Add to dashboard Cite

The burgeoning growth of vehicular traffic, fuelled by rapid urbanization and an everexpanding 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 Qlearning, 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.

show abstract

Section: Introductionmentioning

confidence: 99%