Unlocking Insights Addressing Alcohol Inference Mismatch through Database-Narrative Alignment
This work addresses data quality issues in crash management systems to improve prevention strategies and policy development, though it is incremental as it applies existing methods to a specific domain problem.
The study tackled the problem of alcohol inference mismatch (AIM) in road traffic crash data by developing a framework using BERT and statistical models, analyzing 371,062 records from Iowa to identify 2,767 AIM incidents, resulting in an overall AIM percentage of 24.03%.
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.