IPOD Abstract for presentation (Poster or Podium) with a Paper in the Conference Proceedings
Rail Transport
Fateme Ansari, na
PhD student
Oklahoma State University
Stillwater, Oklahoma, United States
Fateme Ansari, na
PhD student
Oklahoma State University
Stillwater, Oklahoma, United States
.jpg "Joshua Q. Li, PhD, PE photo")
Joshua Q. Li, PhD, PE
Associate Professor Williams Professor
Oklahoma State University
Stillwater, OK, United States
.jpg "Xue Yang, na photo")
Xue Yang, na
PhD student
Oklahoma State University
Stillwater, Oklahoma, United States
Joshua Q. Li, PhD, PE
Associate Professor Williams Professor
Oklahoma State University
Stillwater, OK, United States
Numerous lives are lost annually due to trespassing incidents near railroad areas, despite extensive safety measures. Reducing such incidents requires a thorough understanding of the underlying risk factors. This study employed statistical analysis and natural language processing (NLP) techniques to identify potential risk factors for railroad trespassing incidents, utilizing data from the Federal Railroad Administration (FRA) Accident and Incident Reporting System (RAIRS), including "Short Line Casualties " and "Injury/Illness Summary - Casualty Source Data." The data analysis encompassed a summary of descriptive statistics related to trespassing incidents and an examination of narrative injury text to extract high-frequency factors through text data mining. A comparative analysis was conducted to assess whether these two approaches yielded significantly divergent findings. The insights derived from this research are anticipated to enhance the comprehension of the root causes of trespassing and, ultimately, contribute to a reduction in casualties resulting from trespassing incidents.