In 2018, Marana equipped traffic signals with Autoscope Terra video detection that were configured to collect traffic data according to parameters consistent to studies conducted in the City of Tucson. Event data is stored in the traffic signal controller and can be downloaded from the traffic signal cabinet. The event data are processed using algorithms developed by the UArizona to determine occupancy and delay at traffic signals to estimate travel speeds. These metrics can be used to evaluate measures to improve the efficiency of traffic signals and corridors. In addition, Miovision systems have been installed at 9 Marana intersection.

The overall goal of this multi-year collaboration is to facilitate data collection and analytics and develop data-driven solutions that provide efficient traffic signal operations and optimal progression along key corridors within the entire Town of Marana. Efficient traffic operations save time and money, and positions the Town for future investment in business and technology.

The goal of this project is to develop a comparative analysis approach to integrate region-wide traffic data and provide guidelines to manage and maintain regional traffic data to improve PAG’s traffic count program and to calibrate and validate our ongoing regional modelling efforts. This project will use existing/available and collected traffic data from the various sources of region-wide traffic data using the existing sensors in the Tucson Metropolitan Area along with additional sources of data, i.e., traffic count data and crowd-sourced data, to answer the following questions: 1)What kind of regional traffic data is available and where are the sources of data located? 2) How do the various data sources interrelate? 3) How to integrate different sources data and maintain the quality of regional traffic data?

Intelligent transportation systems (ITS) significantly change our communities by improving the safety and convenience of people's daily mobility. The system relies on multimodal traffic monitoring, that needs to be reliable, efficient and detailed traffic information for traffic safety and planning. Signalized traffic intersections are critical spots for collecting such mix-traffic data because the most conflicts and crash occurrences involve multiple transportation modes, such as pedestrians, bicyclists, motorcyclists, and cars. How to reliably and intelligently monitor intersection traffic with multimodal information is one of the most critical topics in intelligent transportation research.

This project will Investigate a low-cost, low-weight, compact size, and reliable monitoring platform. This platform will incorporate mmWave radar and machine learning techniques to collect multimodal traffic data at intersections is robust to light and adverse weather conditions. The products of this project consists of 1) a prototype of the proposed multimodal traffic, and 3) a demo platform at a road intersection to illustrate the performance in terms of measuring multimodal traffic counts, speeds, and directions.

The Public Universities Task Force designated by Maricopa Association of Government (MAG) is conducting pilot evaluation of smart region technologies implemented in the MAG region. The University of Arizona (UA) is the leading agency to conduct the pilot evaluation of NoTraffic’s traffic management platform. The UA research team is conducting different aspects of a pilot evaluation of the smart sensors and control units installed by NoTraffic Inc. Two study corridors has been selected for the smart traffic sensor implementation: Glendale Avenue in the City of Phoenix and Chandler Blvd in the City of Chandler. NoTraffic Inc. will install smart sensors at five intersections on Glendale Avenue, Phoenix and 11 intersections on Chandler Blvd in the City of Chandler. This pilot study will focus on the evaluation of the operational and safety effectiveness of the smart sensors in the area of traffic coordination and progression through the corridor as well as traffic optimization.

The goal of this multi-phase project is to optimize traffic signals in the City of Tucson to improve mobility, safety, and efficiency. Multi-source data was automatically collected or estimated city wide, and a website (Tucson.UA-Star.org) was developed to implement and analyze the big traffic data. The data is used to provide support for signal re-timing and safety studies.