Unlocking Insights Addressing Alcohol Inference Mismatch through Database-Narrative Alignment

TL;DR

This study employs database narrative alignment and machine learning to identify alcohol inference mismatches in crash data, improving data quality and supporting targeted interventions for road safety.

Contribution

Introduces a novel framework using BERT for detecting alcohol inference mismatches in crash records, enhancing data accuracy for policy and safety improvements.

Findings

24.03% of crashes involved AIM incidents

Alcohol-related and nighttime crashes have lower AIM mismatch

Older drivers and unknown vehicle types are more prone to AIM

Abstract

Road traffic crashes are a significant global cause of fatalities, emphasizing the urgent need for accurate crash data to enhance prevention strategies and inform policy development. This study addresses the challenge of alcohol inference mismatch (AIM) by employing database narrative alignment to identify AIM in crash data. A framework was developed to improve data quality in crash management systems and reduce the percentage of AIM crashes. Utilizing the BERT model, the analysis of 371,062 crash records from Iowa (2016-2022) revealed 2,767 AIM incidents, resulting in an overall AIM percentage of 24.03%. Statistical tools, including the Probit Logit model, were used to explore the crash characteristics affecting AIM patterns. The findings indicate that alcohol-related fatal crashes and nighttime incidents have a lower percentage of the mismatch, while crashes involving unknown vehicle types and older drivers are more susceptible to mismatch. The geospatial cluster as part of this study can identify the regions which have an increased need for education and training. These insights highlight the necessity for targeted training programs and data management teams to improve the accuracy of crash reporting and support evidence-based policymaking.