Is this project an undergraduate, graduate, or faculty project?
Graduate
Project Type
individual
Campus
Daytona Beach
Authors' Class Standing
Poorendra Ramlall, Graduate Student
Lead Presenter's Name
Poorendra Ramlall
Lead Presenter's College
DB College of Engineering
Faculty Mentor Name
Subhradeep Roy
Abstract
Traffic systems are driven not only by motion, but by interaction: vehicles influence one another, drivers continuously adapt to surrounding behaviour, and cognitive processes shape decisions that can propagate through the flow of traffic. Understanding these layered interactions is essential for improving traffic safety and for designing the next generation of intelligent, connected, and automated transportation systems. This PhD research develops a multiscale, data-driven framework for identifying, modelling, and ultimately interpreting interaction structure in traffic systems. The work first established an information-theoretic basis for this problem, demonstrating how information flow can uncover directional relationships in traffic dynamics and help infer causally relevant variables. Building on that insight, the research then explored linear system concepts as interpretable tools for estimating nonlinear car-following dynamics from data. This progression led to a unified framework in which information-theoretic measures guide the selection of meaningful inputs for DMDc-based system identification and prediction. To extend these ideas beyond canonical models, a networked multi-participant driving simulator with synchronised EEG and vehicle telemetry was developed to enable controlled experiments on real driver interaction. The current phase of the work investigates whether DMDc, together with a newly proposed metric, can move beyond prediction to serve as a tool for causal discovery itself. Taken together, this research aims to bridge idealised traffic models, real interactive driving behaviour, and cognitive-state analysis within a single human-centered framework for understanding complex transportation systems.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Included in
Dynamic Systems Commons, Numerical Analysis and Computation Commons, Urban Studies and Planning Commons
Human-Centered Modeling of Traffic as a Complex System
Traffic systems are driven not only by motion, but by interaction: vehicles influence one another, drivers continuously adapt to surrounding behaviour, and cognitive processes shape decisions that can propagate through the flow of traffic. Understanding these layered interactions is essential for improving traffic safety and for designing the next generation of intelligent, connected, and automated transportation systems. This PhD research develops a multiscale, data-driven framework for identifying, modelling, and ultimately interpreting interaction structure in traffic systems. The work first established an information-theoretic basis for this problem, demonstrating how information flow can uncover directional relationships in traffic dynamics and help infer causally relevant variables. Building on that insight, the research then explored linear system concepts as interpretable tools for estimating nonlinear car-following dynamics from data. This progression led to a unified framework in which information-theoretic measures guide the selection of meaningful inputs for DMDc-based system identification and prediction. To extend these ideas beyond canonical models, a networked multi-participant driving simulator with synchronised EEG and vehicle telemetry was developed to enable controlled experiments on real driver interaction. The current phase of the work investigates whether DMDc, together with a newly proposed metric, can move beyond prediction to serve as a tool for causal discovery itself. Taken together, this research aims to bridge idealised traffic models, real interactive driving behaviour, and cognitive-state analysis within a single human-centered framework for understanding complex transportation systems.