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.
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 modeling 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 of data and maintain the quality of regional traffic data?
This project is a partnership with PCDOT to perform various acts of technical support for their department. This involves researching the best practices, software tools, and approaches to resolve PCDOT business problems, as well as researching and comparing available datasets and recommending the best data sets for analysis. UArizona is also working closely with Pima County IT and PCDOT to provide input to the design and implementation of complex spatial-analytical models, products, and services, including web map, web app, and reporting. There is also the intention to perform independent and cooperative complex analysis as well as evaluate management problems and recommend decisions regarding the proper course of action
Source: Pima County Department of Transportation "Smart Transportation" Webpage
In 2018, Marana equipped traffic signals with video detection sensors 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, another brand of sensors have been installed at 9 Marana intersections.
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.
Photo: Town of Marana Locations of Traffic Sensors
Evaluation of Emerging Transportation Technologies
Dec. 2019 ~ Jan. 2021
PI: Yao-Jan Wu, Ph.D., P.E.
Project Managers: Shuyao Hong
The Public Universities Task Force designated by Maricopa Association of Government (MAG) is conducting a 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 a new vendor'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 this vendor. Two study corridors have been selected for the smart traffic sensor implementation: Glendale Avenue in the City of Phoenix and Chandler Blvd in the City of Chandler. This vendor 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.
Data-Driven Mobility Strategies for Multimodal Transportation
Sep. 2019 ~ Sep. 2020
PI: Yao-Jan Wu, Ph.D., P.E.
Co-PI: Terry Yang, Ph.D
Co-PI: Sirisha Kothuri, Ph.D.
This project outlines a data-driven approach to achieve three primary objectives. The first, evaluating and quantifying the best approach for speed management tactics for conventional arterials. The second, evaluating the transferability of using conventional mobility management strategies on connected roadways. The third, scaling speed management tactics for mixed traffic flow and evaluating the impact of the speed management tactics on pedestrians and cyclists' safety.
Credit: Terry Yang University of Utah
Development of Low-cost Radar-based Sensor for Multi-modal Traffic Monitoring
Sep. 2019 ~ Nov. 2020
PI: Siyang Cao, Ph.D. (Contact)
Co-PI: Yao-Jan Wu, Ph.D., P.E.
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 consist 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.
Photo: Dr. Siyang Cao
To mitigate congestion and smooth traffic flow without costly infrastructure improvement or construction, one cost-effective way is signal optimization and coordination. Traffic signal optimization can significantly reduce the amount of delay, travel time, and stops experienced by the drivers, resulting in reduction of fuel consumption and safety enhancement. The objectives of this project are: 1) Determine a baseline for a vendor's connected signal performance across their network of sensors; 2) Optimize Traffic signal timing for intersections equipped with this vendor's sensors. This project will start with Ina Road and Orange Grove Road and determine the percentage performance gain and traveler dollar-value gain based on the signal timing optimization.
Image Source: University of Arizona
ADOT has been using ramp metering strategies to actively control the traffic on the freeway network of the Phoenix Metropolitan area. This project is aimed at developing an evaluation tool to evaluate three possible strategies for ramp metering. Different performance measures are utilized to quantify the impacts of each strategy on the freeway traffic operations and recommendations will be provided to ADOT.