PhD Researcher · Civil & Environmental Engineering (Transportation)
I teach autonomous vehicles to navigate mixed traffic the way it actually behaves, using reinforcement learning and quantum-inspired models of driver heterogeneity to balance driving objectives against drivers who are anything but uniform.
Transportation & Autonomy Laboratory for Intelligence
University of Nebraska-Lincoln
§01 · About
Mohammad Elayan is a PhD Researcher in Civil Engineering (Transportation) at the University of Nebraska-Lincoln and a member of the Transportation and Autonomy Laboratory for Intelligence, directed by Dr. Wissam Kontar.
His research develops intelligent, human-aware autonomous driving for mixed traffic, centering on multi-objective driving, behavioral trade-off balance, and driver heterogeneity modeling. His work draws on empirical trajectory analysis, safety analysis, microsimulation, geospatial analysis, optimization, and artificial intelligence and machine learning methods, including reinforcement learning and quantum-inspired techniques. His research has featured in top journals and conferences.
Before his doctorate, he spent nearly a decade as a Senior Transportation Engineer in Dubai, leading numerous transportation projects across the MENA region, including transportation master plans, traffic impact studies, and multimodal integration for major developments and city-scale planning efforts.
§02 · Research
Fig. 1 · Consensus
Quantifying how automated vehicles balance safety, interaction quality, and efficiency in mixed traffic, using empirical Pareto frontiers and reinforcement learning to steer behavior toward better trade-offs.
Fig. 2 · Heterogeneity
Representing each driver as an evolving, probabilistic behavioral state, a density matrix learned without supervision, so that heterogeneity and its interactions emerge directly from naturalistic trajectory data.
Fig. 3 · Trajectories
Mining large-scale naturalistic and simulated trajectories for interaction and conflict measures, combining street-view, LBS, and crash data to model risk with finer spatial and behavioral resolution.
Fig. 4 · Optimization
Calibrating microsimulation environments to real-world traffic and using reinforcement learning to steer AV/HDV behavior toward system-level, consensus-aware objectives.
§03 · News
Sep 23, 2026
"Learning Behavioral Consensus for Autonomous Driving in Mixed Traffic" (first author), "Quantum-Inspired Modeling of Driving Behavior" (first author), and "Where Pharmaceutical Deserts Are and How to Serve Them: A Geospatial Index and Autonomous Routing Study in Rural Nebraska" (second author) have all been accepted for presentation at the 2027 Transportation Research Board Annual Meeting.
Sep 4, 2026
Our paper "Behavioral Heterogeneity as Quantum-Inspired Representation" has been shortlisted for the Best Paper Award at IEEE ITSC 2026 in Naples, Italy!
Aug 27, 2026
Our paper "Quantum-Inspired Modeling of Driving Behavior," submitted to Transportation Research Part B, represents each driver as an evolving density matrix that is continuous, probabilistic, and history-dependent, with individual behaviors aggregating to recover known traffic phenomena such as the fundamental diagram and hysteresis.
May 25, 2026
Our paper "Can Automated Vehicles Have It All? A Consensus Framework for Diagnosing Behavioral Trade-offs in Mixed Traffic" introduces a framework for quantifying behavioral consensus, evaluating how well traffic behavior satisfies safety, interaction, and efficiency objectives across six metrics.
May 3, 2026
Our paper "Behavioral Heterogeneity as Quantum-Inspired Representation" was accepted for IEEE ITSC 2026 in Naples, Italy (Sept 15 to 18), introducing a quantum-inspired framework for representing behavioral heterogeneity in intelligent transportation systems.
Feb 9, 2026
Our talk "Consensus-Aware AV Behavior: Empirical Pareto-Guided Reinforcement Learning for Mixed Traffic" was accepted to the NAIRR Pilot Annual Meeting 2026, supported by NSF computational resources for training consensus-aware RL models at scale.
Jan 15, 2026
Our paper "Learning the Pareto Space of Multi-Objective Autonomous Driving: A Modular, Data-Driven Approach" was accepted for IEEE IV 2026 in Detroit, a timestep-based, data-driven framework for consensus-aware driving with no access to future data.
Nov 24, 2025
Our paper "Better Safety Analyses through Smarter Data" is officially published in the Transportation Research Record, integrating street-view and traffic-calibrated LBS data into pedestrian crash analysis in Lincoln, NE.
Nov 19, 2025
Presented "Consensus-Aware AV Behavior: Trade-offs Between Safety, Interaction, and Performance in Mixed Urban Traffic" at IEEE ITSC 2025 in Gold Coast, Australia, the first installment of an ongoing line toward reshaping how AVs are designed around real-world trade-offs.
Oct 15, 2025
Phase one of our AV behavioral consensus work analyzes how AVs balance safety, interaction, and traffic performance using high-resolution TGSIM trajectory data, quantifying an empirical Pareto frontier to guide prescriptive control design.
Oct 14, 2025
"A Unified Framework for Consensus-Aware AV Behavior" (first author) and "Multi-Stage Clustering of Daily Human Activities" (co-author) were both accepted for presentation at the 2026 TRB Annual Meeting.
Sep 5, 2025
Our paper "Better Safety Analyses through Smarter Data" was accepted in TRR, integrating StreetLight-calibrated traffic volumes with Mapillary street-view features via a granular grid approach to model crash counts and severity.
Jul 1, 2025
Our paper "Consensus-Aware AV Behavior: Trade-offs Between Safety, Interaction, and Performance in Mixed Urban Traffic" was accepted for the 2025 IEEE ITSC in Gold Coast, Australia (Nov 18 to 21).
Jun 12, 2025
Our paper "Integrating StreetLight Data, EPS Smart Location Data, and Road Attributes: A Random Forest Approach to Multimodal Traffic Calibration in Lincoln, Nebraska" is now online.
§04 · Connect
Contact
melayan2@nebraska.edu
WHIT 362E
Prem Paul Research Center at Whittier School
Lincoln, Nebraska, 68503
Academic Profiles
Research Interests