Research Overview

Our research advances resilient, secure, and trustworthy intelligent transportation networks under cyber and physical uncertainties.

Application Areas

• Transportation cybersecurity & resilience

• Transportation network interdependence & intelligence

Methodological Areas

• Decision-making under uncertainty: Trustworthy AI; Robust optimization; Game theory

• Network analytics: Traffic network modeling; Network optimization; Graph-based AI

Thrust 1: Transportation Cybersecurity & Resilience

Cyber-physical transportation systems create both new vulnerabilities and new opportunities for resilience. Growing reliance on sensing, communication, computation, and automation can expose transportation systems to new cyber vulnerabilities, while these same capabilities can enhance their ability to anticipate, withstand, and recover from physical disruptions such as natural disasters and extreme events. Our research examines these complementary dimensions of cyber-physical transportation systems, with particular emphasis on cybersecurity and cyber resilience. We develop analytical and data-driven approaches for decision-making under uncertainty, integrating interdisciplinary perspectives from trustworthy AI, robust optimization, game theory, and related fields to understand and mitigate cyber risks while strengthening transportation system resilience to a broader range of disruptions.

Selected Related Products

• Noruzoliaee, M., Hernandez, M., Sauceda, O., Nazari, F., 2026. Cyber resilience of connected and autonomous transportation systems (Phase II): Game-theoretic security assurance of network-level traffic signal control. Report prepared for the U.S. Department of Transportation.

• Noruzoliaee, M., Sauceda, O., Hernandez, J., Nazari, F., Hernandez, M., 2026. Certified cybersecurity for traffic signal control under sensor observation attacks: A game-theoretic reinforcement learning approach. Transportation Research Board Annual Meeting, Washington, D.C.

• Noruzoliaee, M., Nazari, F., 2025. Cyber resilience of connected and autonomous transportation systems (Phase I): State-of-the-art and research gaps. Report prepared for the U.S. Department of Transportation.

• Hernandez, M., Noruzoliaee, M., Nazari, F., 2025. Certifiable hybrid cyber attack and defense on learning-based traffic signal control. INFORMS Annual Meeting, Atlanta, GA.

• Zou, B., Rockne, K., Vitousek, S., Noruzoliaee, M., 2018. Ecosystem and transportation infrastructure resilience in the Great Lakes. Environment: Science and Policy for Sustainable Development 60(5), 18-31.

Thrust 2: Transportation Network Interdependencies & Intelligence

Transportation systems are fundamentally networked, interdependent, and multiscale, with local interactions among travelers, vehicles, services, and infrastructure shaping network-wide conditions that, in turn, influence local behavior and performance. Our research examines these interdependencies across two broad classes of transportation networks: (1) multimodal traffic networks, including emerging connected and automated vehicles and advanced air mobility, and (2) physical infrastructure networks, with emphasis on aging infrastructure and network-level asset management. Across both, we seek to develop network intelligence for system-level situational awareness, prediction, and informed decision-making. We develop analytical and data-driven approaches, drawing on interdisciplinary perspectives from network modeling and optimization, operations research, and graph-based AI and machine learning, to model these interdependencies and translate network-level information into prediction and decision-making capabilities.

Selected Related Products

• Lin, Y., Zou, B., Noruzoliaee, M., 2026. Preserving equity: Multi-objective connected and automated vehicle (CAV) lane deployment in mixed traffic. Transportation Planning and Technology 49(6), 1381-1422.

• Noruzoliaee, M., Zou, B., 2022. One-to-many matching and section-based formulation of autonomous ridesharing equilibrium. Transportation Research Part B: Methodological 155, 72-100.

• Noruzoliaee, M., Zou, B., Yan, Z., 2021. Truck platooning in the U.S. national road network: A system-level modeling approach. Transportation Research Part E: Logistics and Transportation Review 145, 102200.

• Noruzoliaee, M., Zou, B., Liu, Y., 2018. Roads in transition: Integrated modeling of a manufacturer-traveler-infrastructure system in a mixed autonomous/human driving environment. Transportation Research Part C: Emerging Technologies 90, 307-333.

• Nazari, F., Noruzoliaee, M., Zou, B., Mohammadian, A., 2017. Optimal facility-specific inspection and maintenance decisions under measurement uncertainty: Unifying framework. Journal of Infrastructure Systems 23(4), 04017036.