Work

Big data-driven pavement condition monitoring and anomaly detection using multi-sensor time series

Year: 2026

Type: article

Abstract: Pavement condition monitoring is essential for road maintenance, but many practical inspection systems still rely on periodic surveys or manual visual checking. Such schemes are difficult to capture short-term pavement changes caused by traffic load, local damage, or environmental disturbance. Meanwhile, smartphone and vehicle-mounted sensors can provide continuous monitoring signals, but their data are often noisy, heterogeneous, and strongly time-dependent. To address this problem, this paper proposes PaveMTS, a multi-sensor time-series framework for pavement condition monitoring and anomaly detection. The framework first aligns and cleans heterogeneous sensor streams, and then learns temporal pavement responses together with cross-sensor correlation patterns. An anomaly score is further constructed by combining reconstruction and prediction errors, so that both gradual deterioration and sudden abnormal disturbances can be detected. Experiments on a public road monitoring dataset show that PaveMTS achieves higher Accuracy, F1-score, and AUC than several classical machine learning and deep time-series baselines. The results also indicate that the proposed method remains relatively stable under missing-data and noisy-data settings. These results suggest that multi-sensor temporal modeling can provide practical support for data-driven pavement maintenance.

Source: Journal Of Big Data

DOI: 10.1186/s40537-026-01550-1

Authors: Feng Xu, Mohammad Jafar Mokarram, Qin Wang, Shanqun Lu, Xiaoshun Qin, Dejuan Li