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Abstract This study presents a data-driven framework for modeling complex systems, with a specific emphasis on traffic modeling. Traditional methods in traffic modeling often rely on assumptions regarding vehicle interactions. Our approach comprises two steps: first, utilizing information- theoretic (IT) tools to identify interaction directions and candidate variables thus eliminating assumptions, and second, employing the sparse identification of nonlinear systems (SINDy) tool to establish functional relationships. We validate the framework’s efficacy using synthetic data from two distinct traffic models, while considering measurement noise. Results show that IT tools can reliably detect directions of interaction as well as instances of no interaction. SINDy proves instrumental in creating precise functional relationships and determining coefficients in tested models. The innovation of our framework lies in its ability to use data-driven approach to model traffic dynamics without relying on assumptions, thus offering applications in various complex systems beyond traffic.