Bayes with Adaptive Memory Framework for Travellers’ Cost Perception Updating Under Network Disruptions

Transportation network disruptions can abruptly alter travel costs and trigger day-to-day route-choice adjustments. Conventional day-to-day dynamic traffic assignment models, however, often rely on fixed-weight or uniformly decaying memory structures, limiting their ability to represent traveller learning under non-stationary conditions. This study develops a Bayes with Adaptive Memory (BAM)-based framework that selectively retains, down-weights, or discounts past travel-cost experiences according to recency and statistical significance. The BAM mechanism is integrated into a link-based day-to-day traffic assignment model to examine how adaptive memory influences perceived costs, route-choice adjustment, and post-disruption traffic-flow evolution. Numerical experiments on a hypothetical network show smoother flow trajectories, faster stabilisation, and a 41.2% reduction in mean absolute perception-tracking error compared with a conventional fixed-weight model. Under a link-removal scenario, the framework captures immediate traffic redistribution followed by gradual stabilisation toward a new post-disruption state. Sensitivity analysis reveals a trade-off between responsiveness and stability: shorter memory improves cost tracking, whereas longer memory and stronger significance weighting promote smoother adjustment and earlier convergence. Within the tested setting, the results suggest that adaptive memory provides a behaviourally plausible mechanism for representing traveller learning and may support disruption-management assessments of adjustment periods, congestion redistribution, and information provision.