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“Facts are many, but the truth is one.” ― Rabindranath Tagore
The Complex Dynamical Systems Laboratory (CDSL) studies how individual actions come together to create emergent group behavior in real-world systems. We focus on situations where many people or units interact at the same time, such as traffic, crowds, animal groups, or connected vehicles, and ask how local interactions shape overall system behavior.
A major focus of the lab is traffic and transportation, supported by a prestigious NSF CAREER Award, where we study how human drivers influence one another and how these interactions affect overall traffic behavior. To do this, we use driving simulators that allow multiple drivers to share the same virtual environment and interact in real time, while collecting detailed vehicle data and brain activity data to better understand workload and decision making.
The lab also explores collective behavior, drawing inspiration from nature to understand how groups move, coordinate, and respond to information from their surroundings. These insights help guide the design of smarter and safer engineered systems.
Overall, CDSL brings together engineering, data science, and human behavior to better understand complex systems and to develop tools that can improve real-world decision making and system design.
At CDSL, we take an interdisciplinary approach that brings together theory, experiments, and computation, guided by three key objectives:
(Objective 1) study of interactions in real-world networked systems,
(Objective 2) contribute to data analysis tools for modeling real-world interactions and complex systems,
(Objective 3) build bio-inspired robust artificial systems using analytical frameworks.
CDSL research is supported by the National Science Foundation (CMMI-2238359).
CDSL research summary:
Facilities:
Virtual-Reality multi-driver (networked) driving simulators with motion platforms
Dry Electrode EEG Headset (DSI-24 - Wearable Sensing)
Current research projects:
Data-driven multiscale modeling of complex traffic systems utilizing networked driving simulators (NSF CAREER Award)
Relevant publications:
Ramlall, P., & Roy, S. (2025). A Data-Driven Framework for Modeling Car-Following Behavior using Conditional Transfer Entropy and Dynamic Mode Decomposition. Applied Sciences, 15, 9700.
Ramlall, P., & Roy, S. (2025). Data-driven car-following traffic modeling using dynamic mode decomposition (accepted for presentation at MECC 2025)
Ramlall, P., Jones, E., & Roy, S. (2025). Development of a networked multi-participant driving simulator with synchronized EEG and telemetry for traffic research. Systems, 13, 564.
Lane, D., & Roy, S. (2024). Validating a data-driven framework for vehicular traffic modeling. Journal of Physics: Complexity, 5(2), 025008.
Ramlall, P., & Roy, S. (2024). Determining critical vehicle connectivity in connected autonomous vehicles using information theory. IFAC-PapersOnLine, 58(28), 995–1000 (MECC 2024).
Lane, D., & Roy, S. (2023). Using information theory to detect model structure with application in vehicular traffic systems. IFAC-PapersOnLine, 56(3), 367–372 (MECC 2023).
Multimodal collective behavior modeling: integrating visual, auditory, and offset vision sensing using agent-based approaches
Relevant publications:
Ramlall, P., Roy, S., 2025: "The role of sensory cues in collective dynamics: a study of three-dimensional Vicsek models", Applied Sciences
Roy, S., Lemus, J., 2021:"How does the fusion of sensory information from audition and vision impact collective behavior?", Frontiers in Applied Mathematics and Statistics
Lemus, J., Roy, S., 2020: "The Effect of Simultaneous Auditory and Visual Sensing Cues in a Two-Dimensional Vicsek Model", Proceedings of the ASME Dynamic Systems and Control Conference (DSCC)
Past research projects: (click on each topic to learn more)
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