Pooja Umesh Pawar
Computer Science
Feburary 2025
Urban traffic congestion is still a core issue, increasing travel time, fuel usage, and air pollution. This research explores the potential of Green-Wave Traffic Optimization with Artificial Intelligence (AI)-based models to improve traffic flow efficiency and decrease congestion. A comparative experimental approach is used, comparing traffic flow under conventional signal control, Green-Wave optimization, and AI-optimized Green-Wave scenarios. Critical performance indicators like travel time, intersection delay, queue length, fuel saving, CO₂ emissions, and cost savings are measured based on real-time traffic information and simulation-based modeling. The findings prove that AI-based Green-Wave optimization greatly enhances traffic efficiency, decreasing travel time and emissions while optimizing fuel saving and economic returns. The research underscores the capacity of AI-based adaptive traffic signal systems to enable sustainable urban mobility and suggests policy interventions for smart city traffic management
205- 215
© Airo Journals 2021
