Li Zhao

Avatar for Li Zhao

Li Zhao

Principal Investigator Civil & Environmental Engr-Lincoln University of Nebraska-Lincoln

Contact

Address
WHIT 262K
Lincoln, NE 68583-0851
Phone
402-472-1928 On-campus 2-1928
Email
lizhao@unl.edu
Website
Website

Academic Degrees

  • Ph.D. in Transportation Systems Engineering (Department of Civil and Environmental Engineering), Univ. of Nebraska-Lincoln, 2017
  • M.S. in Transportation Information Engineering & Control (School of Transportation Engineering), Tongji University, 2013
  • B.S. in Automation (Department of Electronic Engineering and Control), Chang'an University, 2010

Certifications

  • Ph.D., P.E.

Appointment

  • 80% research, 15% teaching, 5% service

Areas of Research and Professional Interest

  • Research Area 1. Human–Automation Interaction and Personalized Shared Control in Automated Driving Systems (ADS)

    Building on shared control strategies, this research is advancing toward personalized shared-control models that capture individual differences in takeover strategies and predict how drivers with different cognitive and behavioral profiles regain manual control after ADS disengagement. The study incorporates virtual-reality environments and field testing to evaluate ADS behavior under naturalistic and mixed-reality conditions to strengthen the foundation for human–automation collaboration, cognitive modeling, and safety-critical system design. 

  • Research Area 2. Safe Driving of Commercial Motor Vehicles and Truck–Vulnerable Road User (VRU) Interactions

    This research examines how truck drivers perceive hazards, manage workload, and respond to visibility limitations and unexpected conflicts in both urban and rural environments. The work integrates simulator experiments, sensor-based monitoring, and operational data from heavy-vehicle fleets to analyze distraction, decision-making, and control behavior under safety-critical conditions. This area also includes truck–VRU conflict modeling using mixed-reality and scenario-based testing in a truck simulator platform.

  • Research Area 3. Driver Behavior Effects on Traffic Flow, Capacity, and Operations in Uninterrupted-Flow Facilities

    This research area focuses on how individual driver behaviors, such as perception–reaction time, car-following tendencies, lane-change decisions, and responses to roadway geometry, shape macroscopic outcomes, including traffic flow stability, capacity, and level of service. The research develops approaches that link observed behavioral heterogeneity to flow breakdown mechanisms, operational reliability, and performance sensitivity under varying demand and geometric conditions. The area also includes assessing how automation-induced changes in driver behavior affect system-level performance.

  • Research Area 4. Electric Vehicle Infrastructure, User Behavior, and Socially Informed Transportation Systems

    This line of research examines how drivers adopt and use EV infrastructure, how charging availability and cost influence behavior, and how EV benefits and burdens are distributed across communities. This research is expanded toward behavioral modeling and planning applications that incorporate user sentiment, charging-station usage patterns, and community-level barriers to EV adoption. This area also supports collaborations with urban planning, data science, and policy researchers, and contributes directly to sustainable mobility, energy-transition readiness, and equitable technology deployment.

Courses Taught

  • CIVE 361 - Transportation Engineering

Funding

(In the recent 5 years)

F24Nebraska Risk-Informed Construction Scheduling and Impact Analysis. Funded by the Nebraska Department of Transportation, $189,448, 7/26 - 5/28. Role: PI
F23Modernizing NDOT’s Pavement Marking Program for Retroreflectivity Compliance and Cost-Effective Restriping. Funded by the Nebraska Department of Transportation, $218,687, 7/26 - 5/28. Role: PI
F22Improving Speed Compliance at Rural City Gateways through Safety Demonstrations, Funded by the U.S. Department of Transportation, Budget: $497,337, Period: 2/26 – 1/28. Role: PI
F19Simulator-Based Testing of Driver Physiological States in Automated Driving, Funded by the UNL Research and Innovation Grants. Budget: $15,000. Period: 1/26 – 12/26. Role: PI
F18Assessment of Crash Reduction Traffic Control Measures at T-intersections on Rural Highways, Funded by the Nebraska Department of Transportation, $175,616, 7/25 - 5/27. Role: PI
F17Selection, Integration, and Effectiveness Study of Input Data for Nebraska Variable Speed Signs on I-80, Funded by the Nebraska Department of Transportation, $169,946, 7/25 - 5/27. Role: PI
F13An Equity-Informed Research on Commercial Motor Vehicle Drivers’ Perceptions and Compliance with Electronic Logging Devices Mandates. Funded by the U.S. Department of Transportation, $433,592, 10/24 - 09/26. Role: PI
F12Development of Accurate and Reliable Annual Average Daily Traffic Factoring Methods. Funded by the Nebraska Department of Transportation, $150,732, 7/24 - 5/26. Role: PI
F9Understand the Sentiment of Rural Consumers on Electric Vehicle Charging Station Deployments using Real-time Data from Mobile Platforms. Funded by Nebraska Governance & Technology Center, $19,874, 5/23 - 4/24. Role: PI
F8Framework for Quantifying Benefits to Disadvantaged Communities: Application to Nebraska’s National Electric Vehicle Infrastructure (NEVI) Plan. Funded by the Nebraska Department of Transportation, $179,055, 7/23-5/25. Role: PI
F5Safety and Mobility Improvement at HRGC Using Real-Time Optimized Preemption of Traffic Signal Strategies. Funded by the U.S. Department of Transportation Region VII UTC - MATC, $101,434, 9/21-4/23. Role: PI. 
F3Developing a Safety Management System Including Hazardous Materials for Highway-Rail Grade Crossings (HRGCs) in Region VII. Funded by US DOT Region VII UTC - MATC, $108,792, 12/21 –12/23. Role: PI.
F1Intelligent Work Zone Using Automatic Queue Detection (AQD) Systems. Funded by the Nebraska Department of Transportation, $159,466, 7/20 –5/22.

Selected Journal Publications

(In the recent 5 years)

  • Ahmad, MU, M Rahman, A Sevim, D Bodoh, SM Khan, L Zhao, N Huynh, and EE Ozguven. Historical prediction attention  mechanism based trajectory forecasting for proactive work zone safety in a digital twin environment. IEEE Internet of Things Journal.
  • Jahangeer, J, N Huynh, L Zhao, Aida Riahifar, Zhenghong Tang. (2026). Truck parking demand analysis along Nebraska’s major truck freight corridor. Journal of Transportation Engineering, Part A: Systems.
  • L Zhao, MS Haque, E Tufuor, and LR Rilett. (2025). The effect of advance warning systems on headway distributions in rural freeway work zones. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations. 10.1080/15472450.2025.2584217
  • Haque, MS, AJ Khattak, and L Zhao. (2025). Modeling and operational performance evaluation of driveway assistance device for lane closure on two-lane highway work zones. Journal of Transportation Engineering, Part A: Systems, 151 (3): 04024122.
  • L Zhao, MU Farooq, and AJ Khattak. (2025). Data accuracy matters: Improving highway-rail grade crossings crash predictions through inventory verification. Transportation Research Record, 2679 (2): 1616-1627.
  • Wang, B, L Zhao, and A Cochran. (2024). Sentiments of rural U.S. communities on electric vehicles and infrastructure: Insights from Twitter data. Sustainability,16 (11): 4871.
  • L Zhao, LR Rilett, C. Liu. (2023). Modeling the impact of COVID-9 interventions on interstate crash rates using comparative interrupted time series. Journal of Transportation Engineering, Part A: Systems, DOI: 10.1061/JTEPBS.TEENG-7605.
  • MS Haque, LR Rilett, L Zhao. (2023). Impact of platooning connected and automated heavy vehicles on interstate freeway work zone operations. Journal of Transportation Engineering, Part A: Systems, 149(3), 04022160.
  • MS Haque, L Zhao, LR Rilett, E Tufuor. (2023). Calibration and validation of a microsimulation model of lane closures on a two-lane highway work zone. Transportation Research Record: Journal of the Transportation Research Board, 2677 (3): 974-990.
  • L Zhao, LR Rilett, C Liu. (2023). Gate violation prediction at highway-rail grade crossings using tree-based ensemble techniques. Journal of Transportation Safety & Security, 1-20.
  • L Zhao, LR Rilett. (2022). Verification and efficacy of automated queue detection systems on high-speed intelligent work zones. Transportation Research Record: Journal of the Transportation Research Board, 03611981221108155.
  • L Zhao, LR Rilett, MS Haque. (2022). A Calibration and validation methodology for simulation models of intelligent work zones. Transportation Research Record: Journal of the Transportation Research Board, 21-01034R1.
  • L Zhao, L Ying. (2022). Identifying origin-destination trips from GPS data – Application in travel time reliability of dedicated trucks. PROMET- Traffic & Transportation, 34(1): 25-38.
  • C Liu, L Zhao, C Lu. (2022). Exploration of the characteristics and trends of electric vehicle crashes: a case study in Norway. European Transport Research Review, 14(1): 1-11.
  • Y Li, L Zhao, K Gao. (2022). Revealing driver psychophysiological response to emergency braking in distracted driving based on field experiments. Journal of Intelligent and Connected Vehicles, 2022.
  • L Zhao, L Rilett. (2021). Modeling violation behavior at highway-rail grade crossings using a driver anxiety surrogate measure. Journal of Transportation Engineering, Part A: Systems, 147 (6): 04021025.
  • L Zhao, L Rilett, MS Haque. (2021). Hidden Markov model of lane-changing-based car-following behavior on freeways using naturalistic driving data. Transportation Research Record: Journal of the Transportation Research Board, 0361198121999382.