CALM: A Calibrated LLM Choice Network Framework for Activity-Based Traveler Simulation
We present CALM, a reproducible hybrid framework that integrates an optional large language model (LLM) activity planner with calibrated stochastic choice, shared network feedback, memory and habit, typed feasibility checks, and deterministic offline replay. Unlike trip-mode classifiers or diary-only generators, CALM executes a closed traveler-day loop and evaluates each generative module against an empirical, reproducible baseline. On the 2024 New York City Citywide Mobility Survey (CMS), 110,691 seven-mode trips are split by respondent into 78,487 training and 32,204 holdout trips. Training-only alternative-specific constant calibration reduces mean holdout mode Jensen-Shannon divergenc...
| Authors | Yezhou Cheng |
|---|---|
| Primary category | cs.LG |
| Published | 6 September 2026 |
| Last updated at source | 6 September 2026 |
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