Text Mining the Contributors to Rail Accidents Appsshops2016

Rail accidents represent an important safety concern
for the transportation industry in many countries. In the 11 years
from 2001 to 2012, the U.S. had more than 40 000 rail accidents
that cost more than $45 million. While most of the accidents
during this period had very little cost, about 5200 had damages
in excess of $141 500. To better understand the contributors to
these extreme accidents, the Federal Railroad Administration has
required the railroads involved in accidents to submit reports that
contain both fixed field entries and narratives that describe the
characteristics of the accident. While a number of studies have
looked at the fixed fields, none have done an extensive analysis of
the narratives. This paper describes the use of text mining with
a combination of techniques to automatically discover accident
characteristics that can inform a better understanding of the contributors
to the accidents. The study evaluates the efficacy of text
mining of accident narratives by assessing predictive performance
for the costs of extreme accidents. The results show that predictive
accuracy for accident costs significantly improves through the
use of features found by text mining and predictive accuracy
further improves through the use of modern ensemble methods.
Importantly, this study also shows through case examples how the
findings from text mining of the narratives can improve understanding
of the contributors to rail accidents in ways not possible
through only fixed field analysis of the accident reports.

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