Transportation Engineering Research

Transportation Engineering

Driving the Advancement of Transportation Infrastructure and Mobility

Researchers in Transportation Engineering use state-of-the-art technology such as AI and machine-learning to make innovative advancements in the planning, design, operations, maintenance, and assessment of transportation systems.

Our exceptional faculty who specialize in Transportation Engineering conduct cutting-edge research in transportation system design and modeling.

Research Areas

Transportation System Design & Modeling
Shared
Mobility
Vehical Electrification & Automation
Computational Analytics

Research Spotlight

Transportation Engineering Research is Saving Utah Millions

Dr. Nikola Markovich is using cutting-edge data science techniques to innovate and improve a wide array of infrastructure to keep our communities moving smoothly and safely.

Backed by funding from UDOT and in collaboration with fellow Civil & Environmental Engineering Professors, three of his recent projects are optimizing resources, improving efficiencies, and saving the state millions.

$3M U.S. Department of Transportation Grant to Improve Mobility Equity

On September 30, 2024, the DOT announced $2.97 million in funding for the establishment of the Mobility Equity Research Center at Florida A&M University, a Historically Black College and University (HBCU). Drs. Nikola Markovich and  Abbas Rashidi  will use their combined expertise in machine vision (MV), artificial intelligence (AI), and optimization algorithms (OA) will be integral to support the initiative's mission of expanding accessibility and mobility for underserved populations, including people with disabilities, older adults, Tribal Nations, and rural and disadvantaged communities.

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One of 12 U.S. Department of Transportation Grants Secured

Drs. Nikola Markovich and Abbas Rashidi secured one of 12 SBIR U.S. Department of Transportation (USDOT) $200K contracts. The USDOT initiative, Complete Streets AI Initiative, aims to leverage advancements in Artificial Intelligence (AI) to improve transportation. Drs. Markovich and Rashidi will team up with local Utah engineering firm WCEC, Inc. to conduct the work.

Titled “Complete Street Data Collection and Assessment using Machine Vision,” the $200k initiative will focus on using computer vision and AI to extract infrastructure information.

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Research Spotlight

Transportation Research is Designing Sustainable Infrastructure in Utah

A recent report published by the Upper Great Plains Transportation Institute presents a comprehensive review of ten years’ worth of barrier-related work order data and transactional expenses to provide the foundation for a big-picture analysis of barrier systems. The case study provided valuable insights into the challenges and opportunities in maintenance data collection and asset cost tracking over time.

These findings are crucial for conducting comprehensive life-cycle cost analyses and evaluating alternative design options.

Dr. Zhu Secures Competitive Grant for his Proposal to Enhance Railroad Safety

The Association of American Railroads' call for proposals received nearly 40 submissions vying for limited funding slots. Titled “Improvements on Machine Learning – Rail Neutral Temperature Predictive Tool,” Dr. Zhu’s innovative approach to addressing critical issues within the railway industry ultimately secured the funding to move forward with his project.

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Student-Led Research

Students of our program conduct research in the various areas of the transportation system design and modeling.

Current Ph.D. student Yirong Zhou’s research focuses on data-driven transportation engineering, including operation research, optimization, machine learning, and spatio-temporal analysis and simulation. He was recently awarded a competitive scholarship from the Institute of Transportation Engineers.

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Researchers in this Area

Dr. Xiaoyue Cathy Liu Associate Professor

Dr. Xiaoyue (Cathy) Liu is passionate about programming, computational analysis, and urban informatics. Dr. Liu's teaching and research is focused on sustainable transportation systems including public transit, managed lanes, large-scale transportation system modeling and simulation, GIS-based infrastructure asset management, network complexity of social sciences, and Intelligent Transportation Systems (ITS).

Dr. Nikola Markovic Assistant Professor

Dr. Nikola Markovic is currently working with his students to apply operations research and data science to analyze and improve transportation systems, as well as looking at the value of information in network models. Dr. Markovic's teaching interests include His research includes transportation methods and probability and statistics. His research focus is operations research and data science in transportation.

Dr. Juan Medina Research Associate Professor

Dr. Juan Medina's research is focused on application of data-driven and computing-based techniques to integrate and analyze large and heterogeneous datasets with the objective of improving transportation operations and safety, deployment and harvesting of web-based resources through online applications and services, and exploration of innovative datasets and data collection techniques to enhance our understanding of traffic operations and safety issues. Dr. Medina’s current research projects are working on leading the Crash Data Initiative, a long-term effort to operate a management system that hosts, maintains, and provide online services and analytics related to Utah’s crash data, conducting research to identify and develop alternative methods for roadway safety analysis using surrogate measures, and quantifying the effects of events in transportation facilities (e.g. crashes) by integrating high-resolution traffic data from crashes, traffic sensors, and video feeds.

Dr. Chenxi (Dylan) Liu Assistant Professor

Dr. Chenxi Liu is passionate about customizing situation-aware machine intelligence to establish a connected and autonomous transportation system. More specifically, his research is focused on using AI/ML methods including cooperative traffic sensing technologies, cyber-physical cooperation, distributed computing to address transportation resilience, safety, equity, and related challenges. He encourages every CvEEN student to learn the basic knowledge about ML and AI and learn to how to apply them to real-world applications.

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