The Sustainable Mobility and Advanced Research in Transportation Lab
Dedicated to pushing the boundaries of technology through cutting-edge research, collaborative partnerships, and mentoring the next generation of researchers.
About
Principal Investigator
Aboah Armstrong is an Assistant Professor at North Dakota State University. He is an ingenious and resourceful Transportation Data Scientist with a proven track record of success in research and hands-on experience developing cutting-edge database solutions, statistical modeling, data products, and computer vision systems aimed at improving transportation system management and operations.
His broad interests lie in computer vision and machine learning. His research involves visual reasoning, vision and language, image generation, air taxis, naturalistic studies, and autonomous vehicles. He received his PhD from the University of Missouri-Columbia, where he was advised by Professor Yaw Adu-Gyamfi.
Expertise
Research Focus Areas
Big Data Analytics
Leveraging large-scale data and machine learning to uncover insights, predict trends, and support decision-making in transportation systems.
Autonomous & Connected Vehicles
Exploring the development, deployment, and societal impacts of self-driving and networked vehicles.
Digital Twins & Smart Cities
Utilizing digital replicas of physical assets and urban environments to optimize city planning and sustainability.
Intelligent Transportation Systems
Integrating advanced technologies to create smarter, adaptive transportation networks.
Pavement & Asset Management
Developing methods for monitoring, maintaining, and optimizing the lifecycle of roads, bridges, and transportation assets.
Transportation Safety
Reducing roadway accidents using data analysis, simulation, and safety interventions.
Education
Teaching Experience
North Dakota State University
University of Arizona
University of Missouri-Columbia
Tennessee Technological University
Our Work
Current Research
Active projects advancing transportation technology through computer vision, machine learning, and data science.
Autonomous Driving & Cooperative Perception
Exploring cooperative perception for autonomous driving using the V2X-Real dataset. This project investigates vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and infrastructure-to-infrastructure (I2I) communication strategies, training V2X-ViT baseline models on multi-agent 3D detection tasks with LiDAR and camera fusion.
UGPTI Pavement Condition Automation
Automating Pavement Condition Index (PCI) estimation using the Gemini large language model. The system identifies pavement type and distress categories, measures severity levels, computes deduct values from standard curves, and calculates corrected deduct values (CDV) to produce final PCI scores — streamlining what has traditionally been a manual inspection process.
Traffic Movie Prediction
Developing RCSNet (Road-Conditioned Spatiotemporal Network) for multi-city traffic flow forecasting. The architecture features four modules — Static Road Encoder, Temporal Traffic Encoder, Road-Guided Cross-Attention Fusion, and Road-Weighted Loss — to predict traffic patterns on high-resolution grids across cities including Moscow, Barcelona, Bangkok, and Chicago.
Partners & Sponsors
