Behavioral Heterogeneity as Quantum-Inspired Representation

Mohammad Elayan, Wissam Kontar
3/24/2026
cs.LGcs.MAstat.ME

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Abstract

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.

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Cite this paper

@article{elayan2026behavioral,
  title  = {Behavioral Heterogeneity as Quantum-Inspired Representation},
  author = {Mohammad Elayan and Wissam Kontar},
  year   = {2026},
  eprint = {2603.22729},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2603.22729v1}
}

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