Studying how AVs balance safety, interaction, and efficiency compared to human-driven vehicles under varied traffic conditions. A consensus framework highlights gaps in performance and what AVs can learn from HDVs to improve adaptive behavior.
Developing AV policies that adapt to competing objectives in simulated networks. Current work uses reinforcement learning with Pareto-based rewards from empirical data to test how consensus-aware designs change outcomes.
Using high-resolution datasets like TGSIM to study AV responses at intersections. Focuses on quantifying safety-critical events, pedestrian hesitation, and consensus violations across performance dimensions, with implications for AV policy design.
Integrating innovative datasets into crash count and severity models. Using context-specific spatial indicators to improve predictive performance and enhance urban safety assessments.
Applying optimization frameworks to design automated delivery systems for the agricultural sector that achieve fast, cost-effective, and sustainable logistics operations by balancing efficiency, resource use, and environmental impact.
Developing models that turn mobility indicators into reliable estimates of real-world traffic volumes. This research advances scalable, data-driven approaches for multi-modal planning and safety applications.
1. Elayan, M., Karki, S. and Hawkins, J. (2025) “Better Safety Analyses through Smarter Data: Adding Open-Street-View and Traffic Calibrated-LBS Data to Pedestrian Crash Analysis in Lincoln, NE.” Transportation Research Record (Accepted).
2. Elayan, M., Aldridge, N., Hawkins, J., & Nam, Y. (2025). “Integrating StreetLight, EPS Smart Location Data and Road Attributes: A Random Forest Approach to Multi-Modal Traffic Calibration in Lincoln, Nebraska.” Journal of Transportation Engineering, Part A: Systems, Vol. 151 (8).
3. Al-Khateeb, G., Al-Suleiman, T., Khedaywi, T. and Elayan, M. (2016). “Studying Rutting Performance of Superpave Mixtures Using Unconfined Dynamic Creep and Simple Performance Tests.” Road Materials and Pavement Design, Vol. 19 (2).
4. Alsheyab, M., Khedaywi, T., and Elayan, M. (2013). “Laboratory Study on Solidification/Stabilization of Unwanted Medications Using Asphalt as a Binder.” Journal of Material Cycles and Waste Management, Vol. 15 (2).
5. Al-Suleiman, T. and Elayan, M. (2013). “Gap Acceptance Behavior at U-turn Median Openings – Case Study in Jordan.” Jordan Journal of Civil Engineering, Vol. 7 (3).
1. Elayan, M., & Kontar, W. (2025). “The Empirical Pareto Frontier of Automated Driving: Consensus Across Safety, Interaction, and Traffic.” (Preprint)
2. Elayan, M., & Kontar, W. (2025). “Consensus-Aware AV Behavior: Trade-offs Between Safety, Interaction, and Performance in Mixed Urban Traffic.” (arXiv preprint)>
3. Nam, Y., Hawkins, J., Butler, D., Aldridge, N., Elayan, M., & Yoo, J. (2024). “Modeling Pedestrian and Bicyclist Crash Exposure with Location-Based Service Data.” Nebraska Department of Transportation, Technical Report No. SPR-FY23(025).
1. Elayan, M., & Kontar, W. (2026). “A Unified Framework for Consensus-Aware AV Behavior: Trade-offs Across Traffic Contexts.” Accepted for presentation at the TRB 105th Annual Meeting, Washington, DC.
2. Elayan, M., & Kontar, W. (2026). “Consensus-Aware AV Behavior: Trade-offs Between Safety, Interaction, and Performance in Mixed Urban Traffic.” Accepted for presentation at the IEEE International Conference on Intelligent Transportation Systems (ITSC), Gold Coast, Australia.
3. Elayan, M., Karki, S., & Hawkins, J. (2025). “Better Safety Analyses through Smarter Data: Adding Open-Street-View and Traffic Calibrated-LBS Data to Pedestrian Crash Analysis in Lincoln, NE.” Presented at the TRB 104th Annual Meeting, Washington, DC.
1. Elayan, M., & Kontar, W. (2025). “The Empirical Pareto Frontier of Automated Driving: Consensus Across Safety, Interaction, and Traffic.” Submitted to Transportation Research Part C: Emerging Technologies.
2. Armantalab, O., Elayan, M., & Hawkins, J. (2025). “Multi-Stage Clustering of Daily Activity Time Series: Patterns and Socio-Demographic Insights.” Submitted to ASCE Journal of Transportation Engineering – Part A: Systems.