Behavioral Heterogeneity as Quantum-Inspired Representation
This work addresses driver behavior modeling for transportation systems, but it appears incremental as it builds on existing quantum-inspired methods applied to a specific domain.
The paper tackled the problem of driver heterogeneity by introducing a quantum-inspired representation that models each driver as an evolving latent state using density matrices, and it evaluated this approach on empirical driving data from TGSIM to extract and analyze driving profiles.
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.