Crash Severity Analysis of Child Bicyclists using Arm-Net and MambaNet
It addresses crash severity prediction for child bicyclists, a vulnerable group, using deep tabular learning, but is incremental as it applies existing models to a specific dataset.
This study analyzed 2,394 child bicyclist crashes in Texas from 2017 to 2022 to predict crash severity, finding that MambaNet outperformed ARM-Net with higher precision, recall, F1-scores, and accuracy, particularly for fatal/severe and no injury categories.
Child bicyclists (14 years and younger) are among the most vulnerable road users, often experiencing severe injuries or fatalities in crashes. This study analyzed 2,394 child bicyclist crashes in Texas from 2017 to 2022 using two deep tabular learning models (ARM-Net and MambaNet). To address the issue of data imbalance, the SMOTEENN technique was applied, resulting in balanced datasets that facilitated accurate crash severity predictions across three categories: Fatal/Severe (KA), Moderate/Minor (BC), and No Injury (O). The findings revealed that MambaNet outperformed ARM-Net, achieving higher precision, recall, F1-scores, and accuracy, particularly in the KA and O categories. Both models highlighted challenges in distinguishing BC crashes due to overlapping characteristics. These insights underscored the value of advanced tabular deep learning methods and balanced datasets in understanding crash severity. While limitations such as reliance on categorical data exist, future research could explore continuous variables and real-time behavioral data to enhance predictive modeling and crash mitigation strategies.