Case Summary:            

We are proud to share the EB2-NIW (National Interest Waiver) approval of a transportation systems engineer from Iran whose research addresses one of the most critical challenges facing modern society: building smarter, more sustainable transportation networks. Through the innovative use of machine learning and optimization modeling, her work aims to reduce congestion, cut emissions, and improve the performance of public and freight mobility systems across the United States.

Engineering Solutions for Sustainable Mobility

This client specializes in the development of data-driven transportation systems that enhance the efficiency and sustainability of travel networks. Her work involves designing predictive models that account for real-time data—such as weather, traffic, and infrastructure conditions—to support smarter decision-making in route optimization, departure scheduling, and system-wide planning.

Currently conducting research at a U.S. institute of technology, she is also pursuing advanced collaborations to bring these intelligent systems into industry practice. From optimizing last-mile delivery through drone charging infrastructure to improving public bus travel time predictions, her research is shaping the next generation of resilient urban mobility.

A Portfolio Grounded in Impact

She has authored three peer-reviewed journal articles, one peer-reviewed book chapter, and is preparing additional manuscripts on smart logistics systems. Her publications have received 14 citations, with one paper ranking in the top 10% most cited in the field of Engineering for its year of publication. She has also served as a peer reviewer for Transportation Research Record and the Transportation Research Board Annual Meeting, completing 11 reviews to date.

Her studies cover a breadth of topics that blend technical depth with real-world applicability:

• Comparative modeling of departure time behaviors using machine learning vs. probabilistic methods.
• Ensemble learning for rural traffic forecasting, improving model reliability in sparse-data environments.
• Big data management for suburban traffic prediction, enabling LSTM models to achieve high accuracy.
• Deep learning for public bus travel times, enhancing service reliability through improved inter-stop forecasting.

Trusted by Experts and Institutions

Letters from distinguished faculty and professionals describe her contributions as indispensable. As one recommender stated:

“[Client’s] real-time traffic prediction work supports not only academic progress but also the operational performance of transportation systems across North America. Her research reduces congestion and improves the dependability of public transport systems, making it highly relevant to national mobility goals.”

Supporting National Infrastructure and Climate Goals

In the U.S., the transportation sector accounts for over 31% of energy-related CO2 emissions and contributes $1.7 trillion annually to the economy. The client’s work responds directly to these challenges by proposing models that optimize vehicle routing, enhance public transit reliability, and incorporate sustainable infrastructure into urban planning.

Her proposed research, including future work on integrating mobile drone charging stations into urban delivery networks, is aligned with U.S. goals of reducing emissions, supporting e-commerce, and future-proofing logistics.

Approved in Less Than Two Months

Filed on March 24, 2025, and approved on May 21, 2025, her EB2-NIW petition received swift approval based on the strength of evidence demonstrating both national importance and her exceptional qualifications to advance the endeavor.

We’re honored to support researchers who bring global insight and technological precision to the most pressing infrastructure challenges of our time. This client’s story exemplifies how strategic thinking, advanced analytics, and engineering excellence can transform not only how we move but how we live.