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PhD Research on AI-Empowered Transportation Systems Resilience and Safety

Marquette University Transportation Engineering
✓ Fully Funded 🎓 Civil Engineering 🎓 Computer Science digital twin machine learning control theory simulation ai transportation resilience traffic safety connected automated vehicles

Explore AI-driven innovations in transportation engineering focused on enhancing system resilience and safety under diverse disturbances. Work on cutting-edge research involving machine learning, control theory, and simulation to shape future intelligent transportation systems.

AI-generated overview

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Why This Research Matters

This research addresses critical challenges in modern transportation systems by enhancing their ability to withstand and recover from physical and cyber disruptions. Improving transportation safety and resilience has direct societal benefits, including reducing accidents, optimizing traffic operations, and ensuring public mobility and infrastructure robustness.

Transportation Mobility Data Mining and Knowledge Discovery Transportation Modeling

Project Description

Project Overview

This PhD project develops knowledge-grounded and AI-empowered paradigms to enhance efficiency, safety, and resilience of transportation systems. Research spans multimodal AI and agentic large language models (LLMs) to intelligent transportation systems, cyber-physical-human resilience in traffic operations, active traffic safety analysis and intervention, as well as connected automated transportation and vehicle control.

What You Will Do

Students will engage in advancing control theory, machine learning including deep learning and reinforcement learning, digital twin technology, and traffic flow modeling. Expect strong use of programming and simulation tools such as Python, SUMO, and CARLA.

Expected Outcomes

Outcomes include novel AI-based models and control frameworks to improve transportation safety and resilience against physical and cyber disruptions. Published work in leading journals and conferences is expected, alongside building a solid foundation for careers in academia, industry, or public agencies.

Why This Matters

Transportation systems face multi-faceted disturbances from both physical and cyber sources. Enhancing system resilience and safety with AI tools ensures sustained performance and effective recovery, directly impacting public safety, mobility efficiency, and infrastructure robustness.

Entry Requirements

Hold or pursue a B.S. or M.S. in Civil Engineering, Mechanical Engineering, Computer Science and Engineering, Electrical and Computer Engineering, Applied Mathematics, Statistics, or a related field. Strong background in control theory, machine learning, digital twin development, or traffic flow modeling preferred. Proficiency in Python, SUMO, or CARLA preferred. Strong motivation and communication skills required.

How to Apply

Email CV, unofficial transcript, and one-page statement of research experience and interests to scottlzh@tamu.edu with subject line: "PhD Application – Transportation Engineering – [Anticipated Start Semester] – [Your Name]." Formal PhD application through Marquette University may be required.

Eligibility

UK/Home
EU
International

Supervisor Profile

DZ
Dr. Zihao "Scott" Li
Marquette University, Transportation Engineering
236 Citations
Google Scholar

Dr. Zihao "Scott" Li, incoming Assistant Professor at Marquette University, earned his Ph.D. in Transportation Engineering from Texas A&M University in 2024. His research focuses on transportation resilience and operations, addressing physical and cyber disruptions by applying AI, control theory, and data-driven methods. He has notable publications in top transportation journals and has received significant fellowships and scholarships.

Key Publications

2026
Knowledge Is Not Static: Order-Aware Hypergraph RAG for Language Models
This paper enhances large language model outputs by grounding them in retrieved knowledge while accounting for order in reasoning tasks.
2026
How Independent are Large Language Models? A Statistical Framework for Auditing Behavioral Entanglement and Reweighting Verifier Ensembles
The study develops a framework to assess hidden behavioral dependencies in large language model ensembles, improving multi-model system reliability.
2026
CyPortQA: Benchmarking Multimodal Large Language Models for Cyclone Preparedness in Port Operation
This work provides benchmarking for multimodal LLMs to support actionable guidance for U.S. ports under tropical cyclone conditions.
2026
Unveiling Traffic Wave of Linear Adaptive Cruise Control: A Second-order Macroscopic Traffic Flow Model
It models traffic waves under adaptive cruise control to improve understanding and management of congestion dynamics.
2026
Cyclic Modulation Control of Multi-Conflict Connected Automated Traffic
The paper proposes a control strategy for connected automated vehicles in complex traffic scenarios to enhance safety and throughput.

Research Contributions

Developed advanced models and frameworks for analyzing and improving transportation resilience against physical and cyber disruptions.
Supports designing transportation systems that sustain performance under stress and recover efficiently, enhancing overall system resilience.
Pioneered the integration of machine learning, statistical methods, and simulation in adaptive cruise control and connected automated vehicle traffic flow analysis.
Advances safety and efficiency in mixed traffic environments with human-driven and automated vehicles.
Established benchmark datasets and methodologies for applying multimodal large language models to disaster preparedness and traffic safety analysis.
Enables better informed decision-making for emergency response and infrastructure management.

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