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Machine learning-based incident duration prediction integrated with microscopic traffic simulation for expressway resilience evaluation.
Article in Science progress. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traffic incidents are a primary contributor to non-recurrent congestion on urban expressway networks, generating economic, safety, and operational impacts. Although machine learning has shown promise for incident duration prediction, a gap remains between predictive modelling and practical integration into traffic management workflows. This study proposes an integrated resilience-based decision-support framework that couples machine learning-based incident duration prediction with microscopic traffic simulation to support evidence-based incident management. A dataset of 2,772 incident records from the Expressway Authority of Thailand (EXAT) was used to train and evaluate four tree-based ensemble models: Random Forest, XGBoost, CatBoost, and LightGBM. LightGBM achieved the best test performance, with a test R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.