Interpretable Traffic Event Analysis with Bayesian Networks
Tong Yuan Jian Yang Zeyi Wen
Although existing machine learning-based methods for traffic accident
analysis can provide good quality results to downstream tasks, they lack
interpretability which is crucial for this critical problem. This paper
proposes an interpretable framework based on Bayesian Networks for traffic
accident prediction. To enable the ease of interpretability, we design a
dataset construction pipeline to feed the traffic data into the framework while
retaining the essential traffic data information. With a concrete case study,
our framework can derive a Bayesian Network from a dataset based on the causal
relationships between weather and traffic events across the United States.
Consequently, our framework enables the prediction of traffic accidents with
competitive accuracy while examining how the probability of these events
changes under different conditions, thus illustrating transparent relationships
between traffic and weather events. Additionally, the visualization of the
network simplifies the analysis of relationships between different variables,
revealing the primary causes of traffic accidents and ultimately providing a
valuable reference for reducing traffic accidents.