Enhanced Secondary Crash Classification through Functional Class-Based Weighting and Hybrid Machine Learning Models

<div class="section abstract"><div class="htmlview paragraph">Road safety and traffic management face significant challenges due to secondary crashes, which frequently cause increased traffic, delays, and collisions. Traditional methods for anticipating secondary crashes often overlook the importance of different road types, resulting in suboptimal predictions and response plans. This research presents a novel method that combines a hybrid machine-learning model with a functional class-based weighting strategy to classify secondary crashes. The functional classes in the dataset are categorized as interstates, arterial roads, collector roads, and local roads. The dataset also includes comprehensive crash narratives and various road attributes. Each functional class is assigned a weight reflecting its proportional importance in the likelihood of a subsequent crash, based on historical data and road usage patterns. This weighting technique is integrated into a hybrid model architecture that trains a Random Forest (RF) model on structured data to utilize road-specific features and a Long Short-Term Memory (LSTM) network on crash narratives to capture contextual and sequential information. A meta-classifier, which leverages the strengths of both methods, combines these models to produce a more comprehensive forecast. The functional class-based weighting increases the model's sensitivity to high-risk road types—such as interstates and arterial roads—which are more likely to experience secondary crashes. By achieving higher precision, recall, and F1 scores than individual models, the hybrid model demonstrates significantly improved performance. The findings reveal that integrating functional class weights with hybrid models resulted in a validation accuracy of 98%, providing crucial insights for traffic management. This strategy enables transportation authorities to effectively manage secondary crash risks, allocate resources more efficiently, and improve overall road safety.</div></div>