## 2 Literature Review  
### Misclassification in Work Zone Crash Data  
Accurate classification of work zone (WZ) crashes remains a persistent challenge in traffic safety datasets. Several studies have identified significant inconsistencies between coded data and the actual crash contexts as described in narrative reports. [PERSON] et al. (2018) revealed that numerous crashes involving construction zones were either incorrectly labeled or omitted entirely from WZ-related categories. Similarly, [PERSON] et al. (2019) reported that over one-third of WZ crashes in their case study were spatially misclassified due to vague work zone definitions and limitations in data reporting standards. [PERSON] et al. (2018) used a unigram + bigram noisy-OR classifier to detect misclassified work zone crashes based on narratives, highlighting how officer workload and vague descriptions contribute to overlooked cases.  
[PERSON] et al. (2019) addressed WZ crash severity prediction using ensemble SHAP methods, and highlighted the under-representation of severe cases. The authors in ([PERSON] et al., 2017) examined WZ crash under-reporting by comparing police-reported data and workplace health and safety (WHS) records in Australia, showing significant data discrepancies that mask the true frequency of such crashes. Previous studies ([PERSON] et al., 2018; [PERSON] et al., 2019; [PERSON] et al., 2019) applied transformer-based deep learning models to classify crash severity, and demonstrated the power of narrative analysis in safety datasets. While these works focused primarily on predictive modeling and narrative analysis, the current study introduces a novel application of fine-tuned LLMs for retrospective mislabel detection. This marks a shift from prediction to validation, using multimodal data fusion to identify inconsistencies between structured crash labels and narrative descriptions--an area not previously explored in the cited works.  
Recent studies have applied deep learning and NLP to improve crash classification by extracting nuanced information from narratives. For example, [PERSON] and [PERSON] (2015) and [PERSON] et al. (2016) used machine learning and sensor-based data to analyze crash risks in work zones, while [PERSON] et al. (2017) used mixed logit modeling to explore improper driving actions in work zones. Other machine learning methods, such as Random Forest and XGBoost, have also been applied to work zone crash severity prediction, with accuracies up to 88.6% in structured datasets ([PERSON] et al., 2018). Additionally, pre-crash behavioral variables from naturalistic driving studies (NDSs) have shown strong predictive power in modeling hazardous work zone events ([PERSON] et al., 2018), reinforcing the need to incorporate contextual data sources. However, most studies have focused on prediction tasks rather than retrospective mislabeling detection--a gap which this paper directly addresses.  
The issue of misclassification has a wider policy implication. [PERSON] and [PERSON] (2019) showed that crash report design influences how work zones are recorded, affecting national fatality estimates. [PERSON] and [PERSON] (2019) further differentiated between crashes caused by work zones and those merely occurring nearby, revealing flaws in current categorization systems. [PERSON] et al. (2019) and [PERSON] et al. (2020) confirmed the need for context-aware data collection strategies to improve classification consistency.