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Adaptive Traffic Management Systems (ATMS) leverage real-time data and intelligent decision-making algorithms to optimize traffic flow and reduce congestion in urban road networks. Unlike traditional fixed-time signal control, ATMS dynamically adjusts traffic signal timings based on live traffic conditions collected through sensors, cameras, and connected vehicles. By integrating machine learning, IoT, and data analytics, the system predicts traffic patterns, detects anomalies, and responds effectively to fluctuating traffic loads. This results in reduced travel time, minimized fuel consumption, improved safety, and enhanced overall transportation efficiency. The proposed system demonstrates that adaptive control strategies can significantly improve urban mobility and support the development of smarter, more sustainable cities.
Keywords:
Adaptive Traffic Control, Intelligent Transportation System (ITS) ,Real-Time Traffic Monitoring ,Dynamic Signal Control, Machine Learning, Internet of Things (IoT), Traffic Flow Optimization ,Smart City, Sensor Networks, Congestion Management, Vehicle Detection ,Predictive Analytics, Traffic Signal Optimization ,Data-Driven ,Decision Making, Urban Mobility
Cite Article:
"Adaptive Traffic Management System", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 12, page no.a189-a191, December-2025, Available :http://www.ijrti.org/papers/IJRTI2512024.pdf
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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