Active Inference for Route Choice Under Uncertainty

Abstract

This paper adapts the Free Energy Principle (FEP) to route choice under uncertainty and examines whether an active inference account can explain travel learning better than conventional reinforcement learning (RL). We develop a route choice model in which travellers update beliefs about hidden traffic conditions and select routes by minimising expected free energy, thereby jointly capturing preferences, uncertainty, and exploration. The model is estimated in a hierarchical Bayesian framework and applied to two complementary experimental datasets: an incentive-compatible driving simulator study and a laboratory route choice study with information manipulations. Across both datasets, the FEP model provides a better account of observed behaviour than RL, especially for participants showing exploratory or belief-adaptive responses. The results suggest that active inference offers a promising neurocomputational foundation for richer models of adaptive travel behaviour.