Current Projects
Optimizing Traffic Signals Using Multi-Source Data | Multi-Phase Project
Aug. 2017 ~ Present
PI: Yao-Jan Wu, Ph.D., P.E.
Project Manager: Francisco Leyva
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.
Automated Data Collection and Analysis for Arterial Traffic Operations | Multi-Phase Project
Nov. 2019 ~ Aug. 2020
PI: Yao-Jan Wu, Ph.D., P.E.
Project Manager: Diahn Swartz, P.E.
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. The event data from these sensors 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. 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
Comprehensive Literature Review for Traffic Signal Timing Issues/Events Related to Red-Light Running
Dec. 2021 ~ May 2022
PI: Yao-Jan Wu, Ph.D., P.E.
Co-PI: Abolfazl Karimpour, Ph.D.
Project Manager: Simon Ramos, P.E.
Red-light running (RLR) behavior is one of the riskiest behaviors at signalized intersections and is becoming a prominent cause of intersection-related crashes (P. Chen et al., 2017). According to the report published by AAA Foundation for Traffic Safety, during 2019 more than two people were killed every day due to noncompliance with red signal indications (AAA Foundation for Traffic Safety, 2020). The City of Phoenix has teamed up with the University of Arizona (UArizona) to draft a comprehensive literature review on the factors influencing RLR violation (such as yellow light interval, clearance time), and identify potential solutions and countermeasures (such as photo enforcement cameras, modifying yellow/clearance time).
Photo:Link
Traffic Signal Performance Measurement: Case Study of McDowell Rd.
Nov. 2021 ~ June 2022
PI: Yao-Jan Wu, Ph.D., P.E.
CO-PI: Abolfazl Karimpour, Ph.D.
Project Manager: Simon Ramos, P.E.
To enhance the operations of traffic signal timing and improve the corridor progression and coordination, the City of Phoenix is implementing emerging technologies field pilots. As one of the emerging technologies, Rhythm Engineering is implementing its Timing Optimization System for a pilot study on one corridor in the City of Phoenix. The City of Phoenix has teamed up with the University of Arizona (UArizona) to evaluate the operational effectiveness of the Timing Optimization System by Rhythm Engineering in terms of intersection and corridor performance evaluation. Below is the detailed scope of this project.
Feasibility Assessment for Adaptive Signal Control (ASC) System.
Date Mar 2021 ~ Oct 2022
PI Yao-Jan Wu, Ph.D., P.E.
Project Manager Richard Hooker
The goal of the UArizona Team is to help the Town of Gilbert understand a variety of Adaptive Signal Control (ASC) and other signal timing improvement solutions in the market and assess the feasibility of implementing the selected solution. The study will be conducted on 26 traffic signals around the area of San Tan Mall and will help give the Town of Gilbert more knowledge about the different types of ASC systems.
Statistical Comparisons of Traffic Data for Traffic Signal Re-Timing
Date Mar 2021 ~ May 2022
PI Yao-Jan Wu, Ph.D., P.E.
Project Manager Simon Ramos
The goal of the UArizona Team is to provide a statistical comparison of multiple traffic data sources for network performance evaluation for conducting traffic signal retiming. The study will include the investigation of more innovative signal re-timing strategies that will help improve the efficiency of traffic signal operations and optimal progression along key corridors. This will ultimately save money and resources within the City of Phoenix.
Image Credit: https://thinktransportation.net/projects/real-time-adaptive-traffic-signal-control-system/
Technical Support for Metropia Data Analytics & DynusT Modeling
Date Sept 2020 ~ June 2021
PI Yao-Jan Wu, Ph.D., P.E.
Project Manager
The objective of the UArizona team will be providing technical support pertaining to data analytics and DynusT modeling. The support will include data mining and analysis. Providing support on transportation modeling utilizing DynusT.
Data-Driven Optimization for E-Scooter System Design
Aug. 2020 ~ Jan. 2022
PI: Dr. Jianqiang Cheng
CO-PI: Yao-Jan Wu, Ph.D., P.E.
The objective of this project is to develop data-driven decision-making models and computational methods for shared mobility system design and operation using a shared e-scooter system as a representative, with the ultimate goal of facilitating an electric shared-mobility revolution that promises a more sustainable future. We aim at solving the urgent questions that arise at the e-scooter sharing company and policymaker level (e.g., planning, operations). Specifically, we will (i) develop a data-driven robust optimization model to provide the decision-maker with a robust solution enabling low cost and high service quality by explicitly capturing endogenous uncertainty in demand in case of limited demand information; (ii) design computationally efficient methods with solution quality guarantees for solving the e-scooter sharing system design and operation problems.
Traffic Data Analytics Platform Development for TSMO
Aug. 2017 ~ Aug. 2020
PI: Yao-Jan Wu, Ph.D., P.E.
Project Managers: Susan Anderson, P.E., PTOE, John Roberts
The goal of this project is for the UA project team to maintain the ramp metering evaluation tool that was developed in the “Data-Driven Evaluation for ADOT Ramp Metering: Developing Ramp Metering Evaluation Tool” project, and to expand the functionality of the tool to handle more traffic operations issues. This tool has now come to be known as the Statewide Mobility Analytics in Real Time (SMART) tool. Modules have been developed to perform a variety of analysis such as analytics of speed, flow, delay, cost-benefit, level of service, on top of real time monitoring capabilities for freeways in and around the Phoenix metro area.
Evaluation of Emerging Transportation Technologies
Dec. 2019 ~ Jan. 2021
PI: Yao-Jan Wu, Ph.D., P.E.
Project Manager: 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
Past Projects
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.
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
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
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.
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
Development of Low-cost Radar-based Sensor for Multi-modal Traffic Monitoring
Sep. 2019 ~ Nov. 2020
PI: Siyang Cao, Ph.D.
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
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
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.
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.
PM: Stephen Wilson
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).
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.
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.
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
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.
Project Manager: Weihua Zhang
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.
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.
PI: Larry Head., Ph.D.
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.
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
