What does this research mean for the field?

Spatially transferable and uncertainty-aware pavement deterioration forecasts can be reliably generated in data-scarce environments using a probabilistic hindcasting framework combined with machine learning models. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Develop and validate a probabilistic hindcasting framework to model pavement roughness evolution in road networks characterized by temporally sparse condition surveys and incomplete maintenance records.

September 2, 2026Transportation Research Interdisciplinary PerspectivesOpen Access

Predicting pavement roughness evolution in data-limited contexts using spatially validated probabilistic hindcasting

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Authors

AOAngela Odera MHMichael HenryShibaura Institute of Technology

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Implication

Predictive modeling study reveals reliable road deterioration forecasting from sparse condition archives in national trunk networks, highlighting viable pavement management under data scarcity.

Key Points

Develop and validate a probabilistic hindcasting framework to model pavement roughness evolution in road networks characterized by temporally sparse condition surveys and incomplete maintenance records.
Analyzed network-level survey datasets from 2018 and 2023 across asphalt-surfaced national trunk roads in Kenya to model five-year International Roughness Index (IRI) interval class changes under binary, three-class, and six-class configurations.
Trained penalized logistic regression (PLR), classification and regression trees (CART), and random forest (RF) algorithms conditioned on baseline inventory, structural, and contextual data using spatially blocked nested cross-validation.
Assessed performance via discrimination metrics (ROC-AUC, PR-AUC) and calibration diagnostics, applying post-hoc Platt scaling and isotonic regression to correct miscalibration.
Models demonstrated moderate-to-strong spatially transferable discrimination, achieving ROC-AUC values between 0.66 and 0.87, with PR-AUC consistently exceeding baseline class prevalence.
Penalized logistic regression and random forests reliably ranked deterioration risks and demonstrated improved probability reliability following recalibration, whereas CART exhibited discrimination collapse on select targets.
Retrospective consistency analysis confirmed the temporal robustness of baseline model forecasts when incorporating structural and contextual attribute changes.

Cite This Study

Odera et al. (2026) studied this question.

synapsesocial.com/papers/6a97e237c562ede874ec63e2 https://doi.org/10.1016/j.trip.2026.102231

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Implication

Predictive modeling study reveals reliable road deterioration forecasting from sparse condition archives in national trunk networks, highlighting viable pavement management under data scarcity.

Predicting pavement roughness evolution in data-limited contexts using spatially validated probabilistic hindcasting

Discussion

Member takes

Implication

Key Points

Cite This Study

Predicting pavement roughness evolution in data-limited contexts using spatially validated probabilistic hindcasting

Discussion

Member takes

Implication

Key Points

Cite This Study

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