AI for Urban Mobility: Reducing Traffic Congestion in Seattle

Abstract

This study examines the application of AI-driven traffic management systems to reduce congestion in Seattle's metropolitan area. Using real-time data from IoT sensors and historical traffic patterns, we developed a predictive model that optimizes signal timing and provides dynamic routing recommendations. Our findings suggest that AI-optimized traffic systems could reduce average commute times by 15-20% while decreasing emissions from idling vehicles.

Key Findings

• •AI-optimized signal timing reduced intersection wait times by 23% during peak hours
• •Dynamic routing recommendations distributed traffic more evenly across arterial roads
• •Predicted 12% reduction in vehicle emissions due to decreased idle time
• •Community feedback emphasized the importance of transparent AI decision-making

Methodology

Our research combined quantitative traffic analysis with qualitative community engagement. We collected data from 150 traffic sensors across Seattle over 18 months, trained machine learning models on historical congestion patterns, and conducted focus groups with residents in affected neighborhoods. The participatory design process ensured that our AI recommendations aligned with community priorities such as pedestrian safety and neighborhood accessibility.

Implications

This research demonstrates the potential for AI to address urban mobility challenges while maintaining community trust through transparent and participatory approaches. The Seattle Digital Commons model of civic AI development—where affected communities have input into system design—proved essential for building public acceptance of AI-driven traffic management.