Project Managers: Francisco Leyva, Jennifer Toothaker
Optimizing Traffic Signals Using Multi-Source Data
The goal of this proposed project is to optimize traffic signals in the City of Tucson to improve mobility, safety and efficiency. In Phase 1 and Phase 2, more than $100m worth of data was atomically collected city wide, and a website (Tucson.UA-Star.org) was developed to implement and analyze the big traffic data. Signal timings along three corridors were optimized and evaluated with the help of TranSync. Additionally, a before-and-after study was conducted to evaluate and compare the mobility and safety impacts of a protected-only and a protected/permissive left-turn phase at the intersection of Speedway Blvd. and Campbell Ave.
Aug. 2019 ~ Aug. 2020
PI: Yao-Jan Wu, Ph.D., P.E. (Contact)
Project Manager: Michelle Montagnino, P.E., PTOE
Data Analytics for Traffic Signal Optimization
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 Miovision connected signal performance across the network; 2) Optimize Traffic signal timing for intersections equipped with Miovision 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.
Data-Driven Mobility Strategies for Multimodal Transportation
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
Feb. 2019 ~ Dec. 2019
PI: Yao-Jan Wu, Ph.D., P.E. (Contact)
Project Manager: Simon Ramos, P.E.
Multi-Criteria Evaluation of Advanced Traffic Management Systems (ATMS)
The Advanced Traffic Management System (ATMS) plays a critical role in traffic management because the ATMS communicates with all traffic signals and sensors. The ATMS is considered the “brain” of each traffic management center (TMC). Therefore, the ATMS helps traffic engineers manage all traffic signals and sensors to improve traffic operations in a city. However, various jurisdictions use different ATMS products and no consensus has been made on how to decide which system most cost efficient. There is no standard procedure to compare those ATMS. In the Phoenix Metropolitan area, four products are typically used. The primary goal of this project is to compare all four systems through a multi-criteria decision analysis process. The proposed process will be able to compare all four products depending on the criteria determined by each participating jurisdiction.
Land Use and Transportation Policies for a Sustainable Future with Autonomous Vehicles: Scenario Analysis with Simulations
With this project, we aim to contribute to addressing the problem of lack of a comprehensive framework and appropriate tools for understanding the long-term effects of AV. To tackle this research problem, our first objective is to develop a parsimonious conceptual framework for examining AV’s long-term effects implement it as a modeling tool, and test and validate the tool with a hypothetical model city to check for reasonable sensitivities. The second objective is to transfer the insights from our conceptual framework and modeling tool to improve the capacity of operational modeling systems at MPOs through the Portland, OR and Tucson, AZ case studies and assessing the impacts of AV and various scenarios in these two regions with different travel and land use patterns and regulatory environments.
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.
This project is a collaboration between the City of Tucson and the Smart Transportation Lab to analyze and re-time signals along two major corridors in Tucson. The project has evolved to include a study on obtaining delay from video sensors, a pedestrian near miss study, and the development of the UA-STAR website that calculates and displays video sensor data from intersections in Tucson.
Pima County Speed Management Evaluation and Strategic Development: Data Driven Enforcement
The purpose of this study is to investigate the impact of deploying speed feedback signs, supported by some pattern of speed enforcement, to impact driver speed behavior in a consistent and predictable manner that will be beneficial to road operations and the utilization of enforcement resources. This project will explore the relationship between speed feedback signs, posted speed limit enforcement, and of speed of travel, and investigate how these features may act as a system to achieve better operations (in terms of number of crashes, reduced severity, and better utilization of capacity).
The objective of this project is to evaluate the performance and operational impacts/benefits before, during, and after the indirect left turns (ILTs) are constructed on Grant Rd. In addition to the Grant Rd/Oracle Rd intersection, two ILT intersections will be constructed between Stone Ave and Park Ave on E. Grant Rd. The analysis will focus on the mobility aspect of the existing and new ILT intersections on Grand Rd.
Incorporating Freeway Performance Measures into Real-Time Crash Prediction
The main objective of this project is to incorporate freeway performance measures into real-time crash prediction. With predicated crash information, traffic engineers can develop countermeasures that could alleviate crash risk or possibly avoid a crash. To fully automate the process of crash prediction, a standalone computer program tool was developed to predict the risk of crashes for all freeway segments in the Phoenix area in real-time. A performance measurement system was also developed to calculate and store the safety related performance measures, covering mobility, freight, and incident management.
The Jiangsu Zhitong Traffic Technology Ltd. is one of the leading transportation technology companies in China. The company has been working with several major cities on developing innovative transportation solutions and collecting multi-source traffic data to support decision making in transportation design and urban planning. Zhitong was seeking for a breakthrough of traffic data management, analysis and visualization. In this project, Zhitong collaborated with the Smart Transportation Laboratory (STL) at the University of Arizona (UA) to implement innovative approaches to help them achieve their goal. The major objectives of the project are to 1) thoroughly investigate and organize the traffic data managed by Zhitong; 2) produce and deliver useful and understandable traffic information for decision support; and 3) effectively present traffic information with novel visualization techniques.
Visualizing Performance Measures with Multi-Source Traffic Data
Mar. 2016 ~ Dec. 2017
(Final report being reviewed by the ADOT Research Center)
This project investigated ADOT’s procedures in deploying, operating, and maintaining signalized arterial corridors. During the project the team analyzed hi-resolution signal data from ADOT’s ATMS, developed a multi-criteria decision making prototype spreadsheet tool to assist ADOT traffic engineers with ITS deployments, and recommended a strategy for ADOT engineers to implement.
SHRP2 Implementation Assistance Program (IAP): Reliability Data and Analysis Tools
This project is expected to take ADOT travel time and SHRP2 tool products to the next level, including improve loop sensor data quality, incorporating multi-source data into travel time reliability calculation, bring SHRP2 products into university classrooms, and advance the use of the existing SHRP2 tools.
Metropia is a Tucson-based traffic smartphone app company. This project is to provide Metropia with technical support to assure and control GPS data and App quality. Three major tasks are completed in this project: 1) Support Quality Assurance/Quality Control of the Metropia App, 2) perform quality assurance/control on the anonymous GPS trajectory data collected by the Metropia App, and 3) help review existing traffic management procedure and helps update the real-time incident report throughout the Tucson area.
Metropia App Quality Assurance and Data Examination
This project, funded by Arizona Department of Transportation (ADOT) aims to identify potential data errors that may occur in the ADOT traffic stations in order to further improve data quality. The methods developed for error identification will be implemented in a computer program to assist TOC’s staff in repetitively examining the data quality and potential causes.
Analyzing Travel Time Data: Development of Traffic Data Quality Assurance Tool
Our team developed a low-cost Bluetooth-based travel-time sensors project. More than 50 Bluetooth-based sensors are installed in Tucson to monitor real-time travel time on five major arterials. In this project, traffic signal timing on Speedway Blvd was examined and improved using real-time signal timing data collected from MaxviewTM, probe vehicle data and video sensor data.
The Real Time Signal Timing and Traffic Information Project (Tucson Bluetooth Systems Deployment)