In fast expanding cities, urban traffic congestion creates major problems for general transportation efficiency and emergency response times. Using real-time sensor data and cloud analytics, this study offers an Internet of Things (IoT)-enabled Smart Traffic Signaling System that dynamically prioritizes emergency vehicles and improves traffic flow. The system uses NodeMCU ESP8266 for wireless data transfer to the ThingSpeak cloud platform, Arduino Nano microcontroller for local signal control, Radio Frequency Identification (RFID) readers for emergency vehicle identification, and Infrared (IR) sensors for vehicle detection. The device also tracks pedestrian pathway infractions and records them to the cloud for enforcement. Prototype testing revealed great performance with 95% vehicle identification accuracy, 98% emergency vehicle prioritization efficiency, and 97% cloud-based integration reliability. This study offers a useful and efficient tool for smart traffic control in smart metropolitan areas by tackling important concerns such environmental robustness, sensor fusion, real-time flexibility, and scalability.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    IoT based Intelligent Traffic Management System with Dynamic Lane Prioritization and Emergency Vehicle Clearance




    Publication date :

    2025-05-14


    Size :

    564314 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Dynamic Traffic Signal Optimization Congestion Management and Emergency Vehicle Prioritization

    Deoghare, Seema U. / Dhond, Yash / Gaikwad, Saakshi et al. | IEEE | 2025



    RF-Based Emergency Vehicle Prioritization and Congestion Management in a Connected Traffic System

    A, Logeswaran / C, Abinaya Bharathi N / T, Aswathi et al. | IEEE | 2025



    Intelligent Traffic Control Method for Emergency Vehicles Prioritization Based on DSRC Transportation System

    Guo, Peng / Wang, Mengdan / Rong, Hui et al. | British Library Conference Proceedings | 2018