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 salience. 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 lower-amplitude day-to-day flow oscillations, earlier numerical 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 shows that the effects of memory parameters are regime- and phase-dependent: shorter memory improves cost tracking under undisrupted conditions, more persistent memory performs better immediately after disruption, while strong salience weighting can become detrimental when combined with persistent memory. 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.