What traffic issues did Downtown Tucson face before using NoTraffic’s solution?

Downtown Tucson experienced increasing traffic congestion, excessive vehicle delays, and long pedestrian wait times due to outdated signal timing systems that couldn’t handle the complex downtown traffic patterns effectively.

What is NoTraffic’s AI Mobility Platform, and how does it work?

NoTraffic’s AI Mobility Platform includes Optimization Mode, which evaluates over 1,000 traffic scenarios per second in real-time. It dynamically adjusts traffic signal timing to reduce delays, enhance safety, and improve traffic flow for all road users, including pedestrians and cyclists.

What were the main goals of the Tucson traffic optimization project?

The primary goals were to: Reduce delays at intersections Improve traffic flow on major corridors Increase safety and efficiency for Vulnerable Road Users (VRUs), such as pedestrians

What measurable improvements were achieved after deploying NoTraffic in Tucson?

The deployment led to major enhancements in traffic efficiency and safety, including: 48.5% reduction in control delay Up to 80% reduction in pedestrian delay 32.9% reduction in AM peak delay 57.1% reduction in PM peak delay Up to 45% reduction in red-light running incidents 1,848 metric tons of CO₂ emissions reduced annually

How did NoTraffic’s system impact off-ramp safety in Tucson?

Queueing on off-ramps was significantly reduced, minimizing the risk of crashes between ramp traffic and high-speed freeway vehicles, thereby improving safety.

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From Gridlock to Green Lights in Downtown Tucson

Q: How many intersections in Tucson were optimized with NoTraffic’s technology?

A total of 15 intersections were optimized, including key areas such as Speedway Boulevard, Euclid Avenue, Broadway Boulevard, and 22nd Street, primarily located near the University of Arizona.

Author: Notraffic

Case Studies Mar 20, 2025

Overview

The City of Tucson faced increasing congestion in key downtown corridors. Traditional traffic signal timing approaches struggled to manage the complexity of downtown traffic, leading to excessive vehicle delays and long pedestrian wait times. Tucson partnered with NoTraffic to deploy Optimization Mode, part of the NoTraffic AI Mobility Platform to address these challenges. This cutting-edge technology dynamically adapts traffic signals in real-time, evaluating over 1,000 scenarios per second to improve traffic flow and road user safety.

The primary goals of the project were to:
• Reduce delay at all intersections
• Improve congestion along major streets
• Enhance safety and efficiency for Vulnerable Road Users (VRUs) like pedestrians

Location

Tucson, Arizona

Population

500,000+

About

By leveraging AI-driven optimization, Tucson successfully transformed its downtown streets into smoother and safer transportation networks. Notable improvements include reduced spillback, shorter platoon spreads, and fewer blocked left-turn movements, all contributing to more efficient traffic flow. Additionally, off-ramp queueing was significantly reduced, lowering the risk of crashes and enhancing overall roadway safety.

For the deployment area, Tucson selected a geographic region in the city’s center, east of the University of Arizona campus. To enhance traffic efficiency and safety, 15 intersections were optimized, including Speedway Boulevard, Euclid Avenue, Broadway Boulevard, and 22nd Street.

The Result

By leveraging AI-driven optimization, Tucson successfully transformed congested corridors into more fluid and safer transportation networks, setting a benchmark for other cities. Some of the noticeable improvements include:

Reduced Spillback: Spillback was reduced throughout the peaks, as was the duration of congestion.
Reduced Platoon Spread: Platoon spread was reduced on all approaches, allowing more effective use of green time due to shorter gaps between vehicles.
Fewer Blocked Lefts: Left turns that were frequently blocked by spillback were addressed by Optimization, reducing the frequency of spillback and maintaining the fluidity of the through movements.
Shorter Off-Ramp Queue: Queueing on off-ramps was reduced, decreasing the risk of crashes between queued traffic on the ramp and high-speed traffic on the freeway.

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

Metric	Improvement (%)
Control Delay	48.5 %
Annual Emissions Reduction (CO2):	1,848 MT
Pedestrian Delay	up to 80 %
AM Peak Delay	32.9 %
PM Peak Delay	57.1 %
Red-Light Running Reduction (RLR)	up to 45 %

Conclusion Solving the Downtown Traffic Puzzle: AI-Powered Solutions for Complex Congestion

This case study highlights how AI-driven adaptive signal control can drastically improve urban mobility in downtown areas. Cities facing congestion, high pedestrian delays, or inefficiencies in traffic signal coordination can benefit from similar solutions.

NoTraffic’s implementation in Tucson provides a model for data-driven decision-making that reduces emissions, improves safety, and enhances traffic flow.