PhD Student & Transportation Researcher
I am a PhD student in Civil Engineering at the University of Nebraska–Lincoln, with a decade of experience in transportation planning and traffic engineering, including leading roles in Dubai, UAE. My current research focuses on automated vehicle behavior and data-driven safety analysis using machine learning and artificial intelligence. I also serve as a teaching assistant and contribute to collaborative projects in AV-human interaction, traffic safety, simulation and traffic calibration.
I am a PhD student in Civil Engineering at the University of Nebraska–Lincoln, where my research focuses on the empirical and simulation-based analysis of automated vehicles behavior. My work seeks to refine AV driving and interactions in diverse traffic conditions, drawing on methods that stretch from trial-and-error to artificial intelligence..
Before starting my doctorate, I spent nearly a decade in Dubai, UAE, where I grew into a Senior Transportation Engineer role and led more than 100 transportation projects across the MENA region. These included transportation master plans, traffic impact studies, and multimodal integration projects for major developments and city-scale planning efforts. My role also involved producing traffic simulations, conducting feasibility and sustainability reviews, and developing demand forecasting models.
Academically, I hold both a Master’s and Bachelor’s degree in Civil Engineering (Transportation) from the Jordan University of Science and Technology.
In my current role as a Graduate Research Assistant, I explore the intersection of automated mobility, data-driven planning, and traffic safety, with particular interest in methods that combine spatiotemporal data, simulation tools, and machine learning and artificial intelligence frameworks to support more intelligent and adaptive transportation systems.
Transportation Engineering
Empirical & Simulation Analysis
Adaptive AV Systems
Forecasting & Prediction
Crash Modeling & Risk Metrics
Trade-off Navigation
Spatial Processing & Analysis
Calibration & Transportation Application
The first phase of our work on AV behavioral consensus design is now online: "The Empirical Pareto Frontier of Automated Driving: Consensus Across Safety, Interaction, and Traffic."
This study analyzes how automated vehicles (AVs) balance safety, interaction quality, and traffic performance using high-resolution TGSIM trajectory data from varied urban settings. Results show limited consensus across these dimensions, and an empirical Pareto frontier quantifies potential improvements and guides prescriptive AV control design.
Two of our papers have been accepted for presentation at the 2026 TRB Annual Meeting in Washington, D.C:
Our paper, "Better Safety Analyses through Smarter Data: Adding Open-Street-View and Traffic-Calibrated LBS Data to Pedestrian Crash Analysis in Lincoln, NE", has just been accepted in the Transportation Research Record (TRR).
Using a granular grid approach, we integrated StreetLight-calibrated traffic volumes with Mapillary street-view features to model both crash counts and crash severity.
Read on ResearchGateOur paper titled "Consensus-Aware AV Behavior: Trade-offs Between Safety, Interaction, and Performance in Mixed Urban Traffic" has been accepted for publication and presentation at the 2025 IEEE International Conference on Intelligent Transportation Systems (ITSC 2025) in Gold Coast, Australia, November 18–21, 2025.
Our paper titled "Integrating StreetLight Data, EPS Smart Location Data, and Road Attributes: A Random Forest Approach to Multimodal Traffic Calibration in Lincoln, Nebraska" is now online!