Current Projects

Optimizing Traffic Signals Using Multi-Source Data

Aug. 2017 ~ Aug. 2020

PI: Yao-Jan Wu, Ph.D., P.E.

Project Managers: Francisco Leyva (Contact), Jennifer Toothaker

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.

Comparative Analysis and Integration of Region-Wide Traffic Data

Jan. 2020 ~ May. 2021

PI: Yao-Jan Wu, Ph.D., P.E.

Project Manager: Hyunsoo Noh, Ph.D. (Contact)

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?

Photo: OpenStreetMap

Tech Support for Transportation Network Management System

May 2020 ~ June 2021

PI: Yao-Jan Wu, Ph.D., P.E.

Co-PI: Mohammad Shaon, Ph.D. (Contact)

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

Automated Data Collection and Analysis for Arterial Traffic Operations

Nov. 2019 ~ Aug. 2020

PI: Yao-Jan Wu, Ph.D., P.E.

Project Manager: Diahn Swartz, P.E. (Contact)

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.

Photo:Xiaobo Ma

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

Data Analytics for Traffic Signal Optimization

Aug. 2019 ~ Jun. 2021

PI: Yao-Jan Wu, Ph.D., P.E.

Project Manager: Michelle Montagnino, P.E., PTOE (Contact)

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

Land Use and Transportation Policies for a Sustainable Future with Autonomous Vehicles: Scenario Analysis with Simulations

Aug. 2018 ~ Nov. 2019

PI: Limin Wang, Ph.D. Portland State University (Contact)

Co-PI: Yao-Jan Wu, Ph.D., P.E.

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.

Multi-Criteria Evaluation of Advanced Traffic Management Systems (ATMS)

Feb. 2019 ~ Mar. 2020

PI: Yao-Jan Wu, Ph.D., P.E.

Project Manager: Simon Ramos, P.E.

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.  

2016 ~ 2020

PI: Yao-Jan Wu, Ph.D., P.E.

Point of Contact: David Alter, PAG

After-Study Point of Contact: Alejandro Angel

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.

Before-and-After Traffic Study for Indirect Left Turns on Grant Road, Tucson

Past Projects

2019

Pima County Speed Management Evaluation and Strategic Development: Data Driven Enforcement

Dec. 2017 ~ Dec. 2019

PI: Yao-Jan Wu, Ph.D., P.E.

Co-PI: Dean Papajohn, P.E. (Contact)

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).

Incorporating Freeway Performance Measures into Real-Time Crash Prediction

Oct. 2016 ~ May. 2018

PI: Yao-Jan Wu, Ph.D., P.E.

PM: Vahid Goftar, P.E. 

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.

Visualizing Performance Measures with Multi-Source Traffic Data

2015 ~ 2018

PI: Yao-Jan Wu, Ph.D., P.E.

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.

Evaluation of Traffic Signal Communication

Mar. 2016 ~ Dec. 2017

(Final report being reviewed by the ADOT Research Center)

PI: Yao-Jan Wu, Ph.D., P.E.

 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

Mar. 2016 ~ Dec. 2017

PI: Yao-Jan Wu, Ph.D., P.E. 

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. 

Nov. 2015 ~ Aug. 2017

PI: Yao-Jan Wu, Ph.D., P.E.

This project is funded by Pima Association of Governments/ADOT: More than 20 Bluetooth sensors are installed to evaluate the performance of signal timing on Oracle Rd in Tucson.

University of Arizona Bluetooth Solution – Oracle Rd Travel Time Data Collection

Metropia App Quality Assurance and Data Examination

Sep. 2015 ~ Jan. 2017

PI: Yao-Jan Wu, Ph.D., P.E. 

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.

Analyzing Travel Time Data: Development of Traffic Data Quality Assurance Tool

Feb. 2016 ~ Dec. 2016

PI: Yao-Jan Wu, Ph.D., P.E.

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.

Multi‐modal Arterial Performance Measurement using Multi‐source ITS Data

Aug. 2014 ~ Oct. 2015

PI: Yao-Jan Wu, Ph.D., P.E. 

Co-PI: Dr. Yi-Chang Chiu, Ph.D. (Contact)

The project aims to classify Bluetooth-based travel time data into different modes of transportation (bicycles, pedestrians, transit and auto vehicles) for performance measurement.

The Real Time Signal Timing and Traffic Information Project (Tucson Bluetooth Systems Deployment)

Apr. 2014 ~ Dec. 2015

PI: Yao-Jan Wu, Ph.D., P.E.

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.

A Real-time Online Decision Support System for Intermodal Passenger Travel

Aug. 2013 ~ July 2015

PI: Mengqi Hu, Ph.D.

Co-PI: Yao-Jan Wu, Ph.D., P.E.

This project aims to improve the efficiency of inter-modal passenger transportation, improve the use of public transportation modes, and reduce transportation cost and time for passengers

Freeway Travel Time Estimation using Existing Fixed Traffic Sensors | Phases 1 and 2

Sep. 2012 ~ Dec. 2014

PI: Yao-Jan Wu, Ph.D., P.E.

This project aims to develop a new method to estimate travel times for all major corridors in St. Louis, MO, employing more than 900 existing fixed sensors.

2018

2017

2016

2015

2014

Data-Driven Evaluation for ADOT Ramp Metering

May 2018 ~ Aug. 2019

PI: Yao-Jan Wu, Ph.D., P.E.

Project Manager: Vahid Goftar, P.E. (Contact)

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.

2013

Development of a Region-wide Traffic Emission Estimation Platform for Environmental Sustainability Evaluation

Oct. 2012 ~ Dec. 2013

PI: Yao-Jan Wu, Ph.D., P.E. 

Housing and Urban Development (HUD) Grant: Sustainable Communities Regional Planning Grant Program

Sep. 2012 ~ Sep. 2013

PI: Sarah L. Coffin, Ph.D.

Co-PI: Yao-Jan Wu, Ph.D., P.E.

2020

 
 
 

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Tucson, AZ 85719
USA

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