What traffic challenges did Tucson face before implementing NoTraffic’s technology?

Tucson experienced significant congestion, long pedestrian wait times, and increased emissions, especially along corridors like Campbell Avenue and Broadway Boulevard. Traditional fixed-timing signals could not adapt to changing traffic patterns.

What solution did Tucson use to improve traffic flow and safety?

The city deployed NoTraffic’s AI-driven adaptive signal control, which dynamically adjusts traffic light timing in real time based on current traffic conditions, improving flow and safety without replacing existing infrastructure.

What measurable improvements were achieved with NoTraffic in Tucson?

37% reduction in pedestrian delay 24% smoother commutes 20.7% reduction in AM peak delay 18.4% reduction in PM peak delay 30% fewer red-light running incidents 3,710 metric tons of CO₂ emissions reduced annually

How did NoTraffic's platform impact environmental sustainability in Tucson?

By cutting vehicle idle times and improving traffic flow, Tucson’s implementation reduced 3,710 metric tons of CO₂ annually—equivalent to planting over 61,000 trees, significantly improving air quality.

What key intersections were targeted in the Tucson project?

High-impact locations such as Campbell Avenue and Broadway Boulevard were prioritized due to chronic congestion, safety risks, and outdated signal infrastructure near major hubs like the University of Arizona.

Can other cities replicate Tucson’s success using their existing infrastructure?

Yes. Tucson’s case shows that AI-powered traffic optimization can deliver transformative results—including improved safety, reduced delays, and environmental benefits—without requiring expensive infrastructure overhauls.

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Tucson, AZ: Getting the Most from Existing Infrastructure

Author: Notraffic

Case Studies Feb 15, 2025

Overview

The Challenge for Tucson, Arizona

In 2021, Tucson partnered with NoTraffic to implement adaptive traffic technology, enhancing both traffic flow and community well-being. Since then, the city has significantly expanded its use of optimization across key corridors and intersections.

The Tucson project focused on four key objectives:
• Enhance Traffic Flow – Reduce vehicle delays at key intersections to keep Tucson moving.
• Prioritize Safety – Minimize pedestrian wait times and the risk of red-light running.
• Support a Greener City – Lower emissions to improve air quality and reduce Tucson’s carbon footprint.
• Improve Driving Experience – Enhance daily commutes and overall quality of life for residents.

Location

Tucson, Arizona

Population

500,000+

About

Through reduced delays, improved pedestrian safety, and lower emissions, Tucson has proven that optimization of traffic management is not just possible—it’s transformative. This case study highlights Tucson’s leadership in urban innovation, paving the way for smarter, safer, and greener cities nationwide.

This study focuses on one of the most critical corridors: Campbell Avenue and Broadway Boulevard. Previously, drivers along this route faced heavy daily congestion, long delays, and pedestrian safety concerns due to outdated, fixed-timing signals. Campbell Avenue, in particular, struggled with chronic gridlock, extended pedestrian wait times, and increased air pollution from idling vehicles -challenges that traditional signal systems failed to resolve.

The Result

The deployment of NoTraffic’s adaptive signal control led to significant improvements in efficiency, safety, and environmental impact:

Traffic Efficiency: 24% smoother commutes by reducing red-light delays, enhancing the driver experience.
Pedestrian Safety: Pedestrian wait times decreased by 37%, particularly benefiting areas near the University of Arizona.
Rush Hour Traffic Relief: AM peak delay times dropped by 20.7%, and PM delays fell by 18.4%, reducing frustration for thousands of daily drivers.
Environmental Impact: Annual CO₂ emissions dropped by 3,710 metric tons, equivalent to planting over 61,000 trees —delivering cleaner air for Tucson residents.

Metric	Improvement (%)
Control Delay	19.4%
Overall Delay	23.6%
Peak Hour Delay	16.0%
Delay at Key Locations	24.0%
Pedestrian Delay	37.0%
AM Peak Delay	20.7%
PM Peak Delay	18.4%
Red-Light Running Reduction (RLR)	30.0%

Conclusion: A Model for Agencies Looking to Get the Most from Their Infrastructure

By adopting NoTraffic’s AI-driven solution, Tucson has set a new standard for smart traffic management. The city’s partnership with NoTraffic demonstrates how intelligent technology can drive real, human-centered benefits using the existing infrastructure.