Statistical analysis of geoinformation data for increasing railway safety

TL;DR

This paper presents a Bayesian statistical analysis of wild animal railway accidents in southern Poland to develop a warning system, revealing patterns related to train frequency, speed, and geography, suitable for less developed railway systems.

Contribution

Introduces a Bayesian-based method for analyzing geolocation accident data that is accessible and effective for railway safety improvements in diverse landscapes.

Findings

Unusual accident patterns linked to train speed and location

Model effective in regions with varied landscapes

Applicable in low IT infrastructure settings

Abstract

The impact of rail transport on the environment is one of the crucial factors for the sustainable development of this form of mass transport. We present a data-driven analysis of wild animal railway accidents in the region of southern Poland, a step to create the train driver warning system. We built our method by harnessing the Bayesian approach to the statistical analysis of information about the geolocation of the accidents. The implementation of the proposed model does not require advanced knowledge of data mining and can be applied even in less developed railway systems with small IT support. Furthermore, we have discovered unusual patterns of accidents while considering the number of trains and their speed and time at particular geographical locations of the railway network. We test the developed approach using data from southern Poland, compromising wildlife habitats and one of the most urbanised regions in Central Europe, based on this we conclude that our model is best suited to railway lines that pass through varying types of landscape.