Application of the Cox Regression Model for Analysis of Railway Safety Performance

TL;DR

This paper explores the use of Cox regression models for analyzing railway safety performance, emphasizing early detection of safety issues through trend analysis and comparing classical and Bayesian methods on real data.

Contribution

It introduces a Cox regression-based model for trend analysis in railway safety and evaluates classical versus Bayesian approaches, highlighting Bayesian influence on small samples.

Findings

Bayesian prior significantly affects results in small samples.

Cox regression effectively models safety performance trends.

Bayesian approach offers advantages for limited data scenarios.

Abstract

The assessment of in-service safety performance is an important task, not only in railways. For example it is important to identify deviations early, in particular possible deterioration of safety performance, so that corrective actions can be applied early. On the other hand the assessment should be fair and objective and rely on sound and proven statistical methods. A popular means for this task is trend analysis. This paper defines a model for trend analysis and compares different approaches, e. g. classical and Bayes approaches, on real data. The examples show that in particular for small sample sizes, e. g. when railway operators shall be assessed, the Bayesian prior may influence the results significantly.