T3e Webinar Overview

Smart Sensors and Infrastructures for Transportation

Date: Thursday July 29, 2021
Time: 1:00 PM–2:15 PM ET
Cost: All T3 webinars are free of charge.
PDH: 1.5 | View PDH Policy

View Webinar: link to this webinar's archive materials

T3 and T3e Webinars are brought to you by the Intelligent Transportation Systems (ITS) Professional Capacity Building (PCB) Program of the U.S. Department of Transportation’s (USDOT) ITS Joint Program Office (JPO). References in this webinar to any specific commercial products, processes, or services, or the use of any trade, firm, or corporation name is for the information and convenience of the public, and does not constitute endorsement, recommendation, or favoring by the USDOT.

Background

This webinar aims to introduce some use cases of applying sensor and control technologies to develop smart transportation. Specific topics covered include intersection pedestrian detection using LIDAR, road surface slippery condition assessment using infrared, rail neutral temperature (RNT) estimation, and traffic operation with connected automated vehicles (CAVs):

Capturing Pedestrian Behaviors at Signalized Intersections with LIDAR Sensing Technology

In her presentation, Farzana R. Chowdhury introduces a new pedestrian behavior capturing system developed by the research group at the University of Texas at Arlington derived from generic LIDAR sensors. The objective is to examine the current guideline of pedestrian-related traffic signal timing and provide data-driven recommendations for necessary updates of the existing design guideline.
Roadway Ice/Snow Detection Using a Novel Infrared Thermography Technology

Keping Zhang presents on research that aims to develop a convenient tool capable of conducting multi-lane roadway temperature mapping and pavement slippery condition evaluation in winter. With the adoption of infrared and video cameras, the proposed technology will provide accurate and robust measures of road surface temperature and slippery conditions for winter weather severity index evaluation.
Rail Neutral Temperature Estimation Using Localized Vibration and Machine Learning

Yuning Wu presents on RNT estimation. When rail temperature changes, CWR develops internal longitudinal stress due to the lack of expansion joints, which may result in rail fracture or buckling. In this work, the team proposed a machine learning-based rail neutral temperature (ML-RNT) predictive tool that exploits local vibration modes and machine learning for RNT estimation.
Adaptive and Multi-Path Progression Signal Control Under Connected Vehicle Environment

In this presentation, Qinzheng Wang introduces a traffic signal control system integrating adaptive traffic signal control and dynamic signal coordination control in a connected environment. This system consists of optimization problems at intersection and corridor levels.

Target Audience

The target audience includes State Departments of Transportation (DOTs), metropolitan planning organizations (MPOs), and other agencies that need to adopt emerging technologies to deal with transportation-related problems.

Learning Objectives

Objectives of this webinar are for the audience to learn about:

An overview of smart sensors and infrastructures that can support transportation systems.
Control methods that can be applied to solve core transportation problems.
Transportation use cases that adopt smart sensors and field implementation opportunities.

Hosts

Xianfeng (Terry) Yang, Assistant Professor, Department of Civil & Environmental Engineering, University of Utah
Terry Yang Dr. Yang is an Assistant Professor (Transportation Engineering) in the Department of Civil & Environmental Engineering at the University of Utah. Dr. Yang’s current research areas include machine learning for transportation modeling, traffic operations with connected automated vehicles (CAVs), traffic safety, transportation equity, transportation planning, and operations research. He received the prestigious NSF CAREER award in 2021. Terry is currently associate editor of ASCE Journal of Urban Planning and Development and IEEE OJ- Intelligent Transportation Systems, and is the handling editor of TRB Transportation Research Record. He is also the vice chair of INFORMS JST ITS committee, secretary of the ASCE Artificial Intelligence Committee, and is the appointed member of two TRB standing committees.

Presenters

Farzana R. Chowdhury, PhD Candidate, Department of Civil Engineering, University of Texas, Arlington
Ms. Chowdhury is a fourth-year PhD student in Transportation Engineering at the University of Texas at Arlington. Farzana’s research focuses on advanced traffic control modeling and simulation within the environment of mixed traffic flow. She also focuses on traffic big data analytics. She is currently conducting a research project about the impact of dynamic flash yellow arrow (for left-turn vehicles) on traffic mobility at intersections. Prior to joining UT Arlington, Farzana received a master’s degree of science in civil engineering (thesis-track) from Mississippi State University, in 2019.

Keping Zhang, Graduate Research Assistant, Department of Civil and Environmental Engineering, University of Utah
Ms. Zhang is a graduate research assistant in the Civil and Environmental Engineering Department at the University of Utah. She has been working in the Infrastructure Sensing and Experimental Mechanics (iSEM) Lab for two years. Keping’s research interests focus on smart sensor development and wave propagation modeling applicable for civil infrastructure maintenance. Prior to joining the University of Utah, Keping was a research assistant at Hong Kong Polytechnic University, Hong Kong, where she worked on the modeling of piezoelectric composite sensors.

Yuning Wu, Graduate Research Assistant, Department of Civil and Environmental Engineering, University of Utah
Mr. Wu is a graduate research assistant in the Civil and Environmental Engineering Department at the University of Utah. He has been working in the Infrastructure Sensing and Experimental Mechanics (iSEM) Lab for two years. Yuning’s research interests focus on sensing technology and machine learning framework development for the maintenance of transportation infrastructure. Prior to joining the University of Utah, Mr. Wu was a research assistant at Beihang University, Beijing, China, where he worked on nonlinear ultrasound and wave propagation.

Qinzheng Wang, PhD Candidate, Department of Civil & Environmental Engineering, University of Utah
Mr. Wang is a PhD candidate in Transportation Engineering at the University of Utah. Qinzheng is involved in a variety of research and projects regarding connected automated vehicles (CAVs). During his time at the University of Utah, he co-authored multiple studies on topics including signal optimization using CAV technology, CAV mobility, CAV trajectory control, and traffic operation with machine learning methods.

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