MURECAST: Memory-Based Utility-Aligned Residual Evidence with Calibrated Activation for Selective Correction of Traffic-Flow Forecasts

Abstract

Short-term traffic-flow forecasting predicts road-network states, yet spatio-temporal predictors can leave structured, context-dependent residuals. They are seldom reused at inference, while similarity-based transfer can introduce corrections that increase error. We propose Memory-Based Utility-Aligned Residual Evidence with Calibrated Activation for Selective Correction of Traffic-Flow Forecasts (MURECAST), which treats historical residuals as candidate interventions. After freezing a context-aware base forecaster, MURECAST builds a static out-of-sample residual memory from an independent period. At inference, same-node and time-valid constraints delimit records, a forecast-visible utility estimator re-ranks them, and utility-weighted top-K aggregation forms a multi-horizon proposal. A chronological calibration split provides an empirical one-sided lower score for applying the proposal or retaining the base forecast. Across PeMS03, PeMS04, PeMS07, and PeMS08, MURECAST ranked first in 11 of 12 reported dataset–metric comparisons, attaining the lowest mean absolute error (MAE) and root mean squared error (RMSE) on all four datasets and the lowest mean absolute percentage error (MAPE) on three; relative error reductions over the strongest published results were 1.69–9.76%. Non-beneficial corrections represented 17–26% of accepted proposals versus 41–48% of all valid proposals. These results show that MURECAST reuses observed errors while concentrating intervention on corrections with lower observed non-beneficial risk under the evaluated chronological protocol.