Modeling dynamic traffic processes requires detailed traveler-level demand unavailable in aggregated national forecasting models. We present a kernel-based approach for generating synthetic disaggregate travel demand using travel survey data. The method links survey respondents to agents in a synthetic population through similarity kernels over socio-demographics, schedule characteristics, and activity–location feasibility, and embeds these in a Metropolis–Hastings sampling framework to generate complete daily plans. The approach provides a modular way to combine heterogeneous information sources and to transfer observed behaviour to new populations without requiring exact matches between individuals. We demonstrate the approach on a stylized grid-world and on real data, where survey records are mapped to a national synthetic population. Results show that the method reproduces key marginal travel patterns, preserves individual variability, and avoids collapse toward modal behaviour, illustrating its potential as a demand synthesis and data-fusion tool in dynamic traffic modelling.