Behavioral Heterogeneity as Quantum-Inspired Representation
Not on the map. Why?
Abstract (arXiv)
Driver heterogeneity is often reduced to labels or discrete regimes, compressing what is inherently dynamic into static categories. We introduce quantum-inspired representation that models each driver as an evolving latent state, presented as a density matrix with structured mathematical properties. Behavioral observations are embedded via non-linear Random Fourier Features, while state evolution blends temporal persistence of behavior with context-dependent profile activation. We evaluate our approach on empirical driving data, Third Generation Simulation Data (TGSIM), showing how driving profiles are extracted and analyzed.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 12 references that are in the map. First posted on arXiv 24 Mar 2026.
Builds on
4 of 12 references.
References · 4
- 2021Learning with Density Matrices and Random Featurescited by 17
- 2023A Generic Stochastic Hybrid Car-following Model Based on Approximate Bayesian Computationcited by 3
- 2025Markov Regime-Switching Intelligent Driver Model for Interpretable Car-Following Behaviorcited by 3
- 2025When Context Is Not Enough: Modeling Unexplained Variability in Car-Following Behaviorcited by 1
Built on by
Not cited yet.