Current Projects

Investigating Road User’s Compliance of Yellow and Clearance Time Intervals for Signal Timing Design

1 May 2022 - Present

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

NCHRP 03-144: Leveraging Existing Traffic Signal Assets to Obtain Quality Traffic Counts and Enhance Transportation Monitoring Programs

1 Apr. 2022 - Present

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

Optimizing Traffic Signals Using Multi-Source Data: Phases 1 - 6

Aug. 2017 - Present

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

PM: 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.

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. & Abolfazl Karimpour, Ph.D.

PM: 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).

Region-Wide Traffic Performance Evaluation and Performance Measure Development Using Multi-Source Data

1 Dec. 2021 - Present

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

Traffic Signal Performance Measurement: Case Study of McDowell Rd.

Nov. 2021 - Jun. 2022

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

PM: 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

Mar. 2021 - Oct. 2022

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

PM: 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

Mar. 2021 - May 2022

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

PM: Simon Ramos, P.E.

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.

Automated Data Collection and Analysis for Arterial Traffic Operations

Nov. 2019 - Present

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

PM: 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.

Technical Support for Metropia Data Analytics & DynasT Modeling

Sept. 2020 - June 2021

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

Traffic Data Analytics Platform Development for TSMO – Expansion of Ramp Metering Evaluation Tool

Aug. 2017 - Aug. 2020

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

PM: 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: MAG Public Universities Task Force Support

Dec. 2019 - Jan. 2021

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

PM: 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.

Past Projects

Data-Driven Optimization for E-Scooter System Design

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

Comparative Analysis and Integration of Region-Wide Traffic Data

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

Data-Driven Optimization for E-Scooter System Design

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

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.

Evaluation of Emerging Transportation Technologies: Case Studies in Phoenix and Chandler

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PI: Yao-Jan Wu, Ph.D., P.E. & Mohammad Shaon, Ph.D. (Phase 1) & Abolfazl Karimpour (Phase 2)

Comparative Analysis and Integration of Region-Wide Traffic Data

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

PM: 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?

Evaluation of Emerging Transportation Technologies: Case Studies in Phoenix and Chandler

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PI: Yao-Jan Wu, Ph.D., P.E. & Abolfazl Karimpour, Ph.D. & Mohammad Shaon, Ph.D.

Data-Driven Mobility Strategies for Multi-Modal Transportation

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

Tech Support for Transportation Network Management System

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

Development of Low-Cost Radar-Based Sensor for Multi-Modal Traffic Monitoring

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

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.

Data Analytics for Traffic Signal Optimization

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

PM: Michelle Montagnino, P.E., PTOE

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.

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

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

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

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

PM: 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.