Unlocking the Narrative: Using Text Mining to Reveal the Hidden Factors behind Suicide Related Traffic Crashes.

Abstract

Suicide is the deliberate act of ending a person's own life due to multifarious reasons. In the U.S., suicide is the 10th major cause of death. Nearly 45,000 people died by suicide in 2016 across the nation. It is anticipated that not all traffic crashes can be considered as accidents. Traffic crash related injuries are occasionally considered a means of suicide, and some crashes occur due to the suicidal attempts. These attempts can be made by operators of motor vehicles, jumpers into the pathway of trains, and pedestrians deliberately jumping into the vehicle trajectory. There are a handful of studies that have focused on traffic crashes (both railroad and roadway) related to suicidal incidents. This study aimed to explore the insights associated with suicide related traffic crashes (SRTCs) by collecting traffic data for seven years (2010-2016) from Louisiana. At first, exploratory data analysis was performed to examine the five Ws (who, what, why, when, and where) associated with SRTCs. Later, this study applied text network analysis, which was not performed in any of the previous studies, to provide additional contexts of these crashes. The findings of this study can shed lights on an unexplored arena of transportation safety research.