Mitigating the impact of outliers in traffic crash analysis: A robust Bayesian regression approach with application to tunnel crash data

Traffic crash datasets are often marred by the presence of anomalous data points, commonly referred to as outliers. These outliers can have a profound impact on the results obtained through the application of traditional methods such as logit and probit models, commonly used in the domain of traffic safety analysis, resulting in biased and unreliable estimates. To mitigate this issue, this study introduces a robust Bayesian regression approach, the robit model, which utilizes a heavy-tailed Student's t distribution to replace the link function of these thin-tailed distributions, effectively reducing the influence of outliers on the analysis. Furthermore, a sandwich algorithm based on data augmentation is proposed to enhance the estimation efficiency of posteriors. The proposed model is rigorously tested using a dataset of tunnel crashes, and the results demonstrate its efficiency, robustness, and superior performance compared to traditional methods. The study also reveals that several factors such as night and speeding have a significant impact on the injury severity of tunnel crashes. This research provides a comprehensive understanding of the outliers treatment methods in traffic safety studies and offers valuable recommendations for the development of appropriate countermeasures to effectively prevent severe injuries in tunnel crashes.

Automated summary

Traffic crash datasets are often affected by outliers, which can greatly influence the results obtained from traditional methods like logit and probit models used in traffic safety analysis. This study proposes a robust Bayesian regression model called robit, which addresses this issue by using a heavy-tailed Student's t distribution to reduce the impact of outliers. A sandwich algorithm based on data augmentation is also proposed to improve the estimation efficiency of posteriors. The model is rigorously tested using a tunnel crash dataset and outperforms traditional methods in terms of efficiency, robustness, and performance. The study also identifies significant factors like night and speeding that impact the severity of injuries in tunnel crashes. This research provides a comprehensive understanding of outlier treatment methods in traffic safety studies and valuable recommendations for preventing severe injuries in tunnel crashes.