Design and Deployment of an AI-Driven Smart Traffic Prediction System for Real-Time Traffic Flow Analysis, Road Safety, and Environmental Sustainability

Srivarshini R , Desika. S , Sowmithran. V , Vignesh. V , Bharath. S

Traffic congestion, Artificial Intelligence, Smart City

The growing complexity of urban traffic systems has posed significant challenges to effective traffic management in modern cities. Traffic congestion exacerbates commuter stress and increases travel times, fuel consumption, and environmental pollution. To address these issues, the Smart Traffic Prediction Unit (Traffic Trek) has been developed as an innovative solution to enhance urban mobility through advanced technologies. Traffic Trek leverages AI algorithms and sensor-based technologies to collect and analyze real-time traffic data such as vehicle counts, speed patterns, and weather conditions. By processing these inputs, the system predicts traffic flow patterns and congestion levels, offering actionable insights to improve traffic signal timings and optimize route planning. Integration with user-friendly interfaces, including mobile applications and navigation systems, ensures real-time updates, alternative route suggestions, and accurate travel time estimations for commuters. This system not only enhances traffic efficiency but also contributes to reducing congestion, lowering fuel consumption, and improving environmental sustainability. Traffic Trek supports road safety initiatives by minimizing traffic conflicts and lays the groundwork for smart city infrastructures. Furthermore, it enables seamless integration with emerging technologies such as 5G communication networks and autonomous vehicles, creating a sustainable and future-ready urban mobility network.

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"Design and Deployment of an AI-Driven Smart Traffic Prediction System for Real-Time Traffic Flow Analysis, Road Safety, and Environmental Sustainability", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.10, Issue 9, page no.b172-b177, September-2025, Available :https://ijsdr.org/papers/IJSDR2509123.pdf

Volume 10 Issue 9, September-2025

Pages : b172-b177

Paper Reg. ID: IJSDR_304978

Published Paper Id: IJSDR2509123

Downloads: 000129

Research Area: Science and Technology

Country: Coimbatore, Tamil Nadu, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2509123

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2509123

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition