Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
—Urban intersections increasingly struggle to handle rising traffic levels, particularly when traditional fixed-timer signals fail to respond to real-time conditions. To address this issue, this work proposes an adaptive traffic management system that integrates artificial intelligence with sensor-driven monitoring. A Raspberry Pi processes live camera footage through a YOLObased detection model to identify vehicles and measure lane-wise density, enabling dynamic adjustment of signal timing. Alongside this, an ESP32 unit collects data from ultrasonic and infrared sensors and supports wireless communication for emergencyvehicle prioritization. By combining visual analytics with sensor validation, the system enhances reliability, reduces delays, and ensures rapid clearance for ambulances, offering a practical solution for smart-city environments.
Keywords:
–Artificial Intelligence (AI), YOLO, Raspberry Pi, ESP32, Internet of Things (IoT), Smart Traffic Management, Ultrasonic Sensor, IR Sensor, Image Processing, Ambulance Priority Syste I
Cite Article:
"AI POWER TRAFFIC MANAGEMENT SYSTE,", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.a434-a436, May-2026, Available :http://www.ijrti.org/papers/IJRTI2605051.pdf
Downloads:
000205565
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator