Multiple Imputation of Missing Traffic Volume: An Advanced Framework and Multi-Domain Validation

Abstract

High-frequency traffic data from remote sensors often suffer from severe gaps and multi-day blackouts. Traditional deterministic imputation fails during these extended failures, artificially destroying natural traffic variance. To resolve this, this study develops an adaptive Multiple Imputation (MI) framework to reconstruct missing 2-min volumes. A novel multi-tier historical median predictor with adaptive expansion (up to ±30 min) serves as a variance-protected anchor for two stochastic engines: Stochastic Linear Regression and Predictive Mean Matching with Approximate Bayesian Bootstrap (PMM-ABB). PMM-ABB features dynamic K-neighbor autotuning, with simulation convergence governed by a dual-metric algorithm. Performance was evaluated against a Historical Average (HA) baseline via multi-domain validation, strictly assessing the models’ ability to recover hidden, real-world ground-truth counts rather than replicating the engineered input features. Under severe block-missingness, the stochastic models prevented collapse, reducing Temporal Cross-Validation MAE from 33.7 (HA) to 22.7 (Cohen’s d = −0.4). Power Spectral Density matching confirmed that both models preserved macro-periodic traffic waves, keeping spectral tracking errors under 4.8 dB. Ultimately, PMM-ABB slightly outperformed Stochastic Regression in sequential time dependency and point accuracy, confirming that the framework provides a highly reliable structural proxy for continuous highway flow modeling.