Capturing behavioral heterogeneity for traffic flow: A scalable and personalized decomposition approach

Traffic flow heterogeneity, stemming from diverse behaviors of its agents, presents a fundamental challenge in developing accurate predictive and descriptive models that are at the heart of understanding traffic. This paper adopts a data decomposition framework to address the limitations of global data aggregation methods that often obscure individual agent behaviors in traffic systems. We propose a decomposition framework that represents each agent’s data as a sum of population-level common component and agent-specific personalized component. The common component is learned as a shared temporal subspace across agents, while personalized components are extracted in an orthogonal complement, yielding an interpretable separation between common and personalized behavior. Through stylized experiments we show that pooled learning can collapse under heterogeneity, misleading traffic-level interpretation. Whereas the proposed decomposition recovers agent-level behavior, enhancing interpretability. We further apply the method to empirical driving data to perform behavioral analysis, personalization, and behavioral clustering across different agents. The framework provides a bottom-up bridge between traffic agent heterogeneity and traffic-level analysis.