Integrated Framework for Reliable Work Zone Crash Classification: Combining Data Validation, Machine Learning Ensembles, and Natural Language Methods

This paper presents a comprehensive, publication-ready investigation into the problem of reliable work zone crash classification and risk prediction using an integrated pipeline that emphasizes rigorous data validation, modern machine learning ensembles, and natural language processing of crash narratives. Work zones are high-risk environments on road networks and accurate identification and classification of work zone crashes is essential to enable targeted safety interventions, resource allocation, and reliable research (Yang, 2015; Blackman et al., 2020). Yet, existing operational crash datasets suffer from misclassification, incomplete fields, and inconsistent semantics arising from heterogeneous reporting practices (Swansen et al., 2013; Carrick et al., 2009). We argue that improving data quality through systematic validation and hybrid AI-augmented checks is a prerequisite for robust predictive modeling (Van Der Loo & De Jonge, 2020; Redman, 1998). Building on advances in ensemble learning and hyperparameter optimization (Almahdi et al., 2023; Asadi & Wang, 2023), together with text-mining approaches for narrative analysis (Sayed et al., 2021), we design and describe an end-to-end methodology: (1) a layered data validation and correction module that uses deterministic rules and large language model-assisted anomaly detection; (2) a multimodal feature engineering strategy that integrates structured traffic and environmental data with unstructured narrative-derived features; (3) an ensemble classifier framework that uses stacked learners with hyperparameter tuning to achieve robust classification across varying traffic conditions; and (4) a human-in-the-loop verification stage to capture residual errors and provide continuous feedback for model retraining (Malviya & Parate, 2025; OpenAI, 2023). We present a descriptive analysis of modeled experimental outcomes and sensitivity studies, discuss theoretical implications, confront limitations, and outline future research directions. The findings demonstrate that combining principled data validation with ensemble learning and narrative text mining materially reduces misclassification rates, produces better calibrated crash-risk scores, and yields interpretability benefits valuable for practitioners and policymakers (Pande et al., 2011; Sayed et al., 2021). This article contributes a detailed procedural blueprint and theoretical rationale for transportation researchers seeking reliable, defensible analytics for work zone safety.