Text Mining Analysis of Railroad Accident Investigation Reports

2016 ◽  
Author(s):  
Trefor Williams ◽  
John Betak ◽  
Bridgette Findley
Keyword(s):  
United States ◽  
Text Mining ◽  
Topic Modeling ◽  
The United States ◽  
Transportation Safety ◽  
Accident Investigation ◽  
Probabilistic Topic Modeling ◽  
Grade Crossing ◽  
National Transportation Safety Board

The National Transportation Safety Board in the United States and the Transportation Safety Board of Canada publish reports about major railroad accidents. The text from these accident reports were analyzed using the text mining techniques of probabilistic topic modeling and k-means clustering to identify the recurring themes in major railroad accidents. The output from these analyses indicates that the railroad accidents can be successfully grouped into different topics. The output also suggests that recurring accident types are track defects, wheel defects, grade crossing accidents, and switching accidents. A major difference between the Canadian and U.S. reports is the finding that accidents related to bridges are found to be more prominent in the Canadian reports.

scholarly journals What is Data Science? An Operational Definition based on Text Mining of Data Science Curricula

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Zhiyong Zhang ◽  
Keyword(s):  
United States ◽  
Text Mining ◽  
Computer Science ◽  
Topic Modeling ◽  
Data Science ◽  
The United States ◽  
Operational Definition ◽  
Bottom Up ◽  
Science Curricula

Data science has maintained its popularity for about 20 years. This study adopts a bottom-up approach to understand what data science is by analyzing the descriptions of courses offered by the data science programs in the United States. Through topic modeling, 14 topics are identified from the current curricula of 56 data science programs. These topics reiterate that data science is at the intersection of statistics, computer science, and substantive fields.

scholarly journals ANALYSIS OF WING SEPARATION AND MID-AIR BREAKUP IN LIGHT TRAINING AIRCRAFT

Aviation ◽  
2021 ◽  
Vol 25 (4) ◽  
pp. 278-282
Author(s):  
Matthew Hirabayashi
Keyword(s):  
United States ◽  
The United States ◽  
Transportation Safety ◽  
National Transportation Safety Board ◽  
Aircraft Accident ◽  
Wing Spar

Despite increasing discussions concerning the recently published wing spar airworthiness directive (AD) that affects many training aircraft and several current ADs for wing struts, there remains limited objective literature on incidents of wing separation or mid-air breakup. This paper attempts to report and analyse instances of wing separation and mid-air breakup of light training aircraft. A careful review of the United States National Transportation Safety Board (NTSB) aircraft accident database revealed that wing separations were more likely occur as mid-air breakup in PA28s than 172s/182s (OR: 3.06, 95 % CI: 1.3682 to 6.8536, p = .008). Additionally, wing separations were less likely to occur as mid-air breakups in the strutted 172s/182s than 177s/210s that don’t have a wing strut (OR: 0.11, 95 % CI: 0.04 to 0.29, p = <.001). This implies that non-strutted wing designs may be more susceptability to mid-air breakup than the strutted design of similar aircraft.

Collisions between Radar-equipped Merchant Ships U. S.

1969 ◽  
Vol 22 (04) ◽  
pp. 454-463
Author(s):  
Keyword(s):  
United States ◽  
Relative Motion ◽  
The United States ◽  
Transportation Safety ◽  
Directed Attention ◽  
The Road ◽  
National Transportation Safety Board

This study by the United States National Transportation Safety Board was undertaken to determine why ship collisions persist despite the use of radar and what recommendations could usefully be made to prevent collisions between radar-equipped vessels. The samples analysed cover the fiscal years 1963–7. An Appendix giving details of these collisions is omitted.1. Introduction. The National Transportation Safety Board's review of the investigation of the collision between the S.S.Arizonaand the M.V.Meiko Marudirected attention to collisions of vessels equipped with radar. In this case, the Masters of both vessels were navigating by radar to the exclusion of the Rules of the Road and the requirements of good seamanship, and neither was utilizing properly the radar to best advantage by plotting the relative motion of targets.

scholarly journals The COVID-19 Pandemic and Mental Health Concerns on Twitter in the United States

2021 ◽  
Author(s):  
Senqi Zhang ◽  
Li Sun ◽  
Daiwei Zhang ◽  
Pin Li ◽  
Yue Liu ◽  
...  
Keyword(s):  
Mental Health ◽  
United States ◽  
Topic Modeling ◽  
The United States ◽  
Allocation Model ◽  
Health Concerns ◽  
Demographic Composition ◽  
The Us ◽  
Mental Health Concerns ◽  
Twitter Users

AbstractBackgroundMental health illness is a growing problem in recent years. During the COVID-19 pandemic, mental health concerns (such as fear and loneliness) have been actively discussed on social media.ObjectiveIn this study, we aim to examine mental health discussions on Twitter during the COVID-19 pandemic in the United States and infer the demographic composition of Twitter users who had mental health concerns.MethodsCOVID-19 related tweets from March 5th, 2020 to January 31st, 2021 were collected through Twitter streaming API using COVID-19 related keywords (e.g., “corona”, “covid19”, “covid”). By further filtering using mental health keywords (e.g., “depress”, “failure”, “hopeless”), we extracted mental health-related tweets from the US. Topic modeling using the Latent Dirichlet Allocation model was conducted to monitor users’ discussions surrounding mental health concerns. Demographic inference using deep learning algorithms (including Face++ and Ethnicolr) was performed to infer the demographic composition of Twitter users who had mental health concerns during the COVID-19 pandemic.ResultsWe observed a positive correlation between mental health concerns on Twitter and the COVID-19 pandemic in the US. Topic modeling showed that “stay-at-home”, “death poll” and “politics and policy” were the most popular topics in COVID-19 mental health tweets. Among Twitter users who had mental health concerns during the pandemic, Males, White, and 30-49 age group people were more likely to express mental health concerns. In addition, Twitter users from the east and west coast had more mental health concerns.ConclusionsThe COVID-19 pandemic has a significant impact on mental health concerns on Twitter in the US. Certain groups of people (such as Males, White) were more likely to have mental health concerns during the COVID-19 pandemic.

Analysis of Train Collision Risk in the United States: 2001 to 2015

2018 ◽  
Author(s):  
Tejashree Turla ◽  
Xiang Liu ◽  
Zhipeng Zhang ◽  
Zheyong Bian
Keyword(s):  
United States ◽  
Collision Frequency ◽  
The United States ◽  
Substantial Contribution ◽  
Collision Risk ◽  
Human Errors ◽  
Transportation Risk ◽  
Common Causes ◽  
Grade Crossing ◽  
Major Factors

Railways have a substantial contribution to the economy of the United States. However, a train accident can result in casualties and extensive damages to infrastructure and the environment. Most of the prior research focused on derailments or grade-crossing accidents rather than the study of train collisions. The Federal Railroad Administration (FRA) identifies over 300 causes for all types of accidents, among which we aim to recognize the major factors that cause train collisions. Evaluating how collision frequency and severity vary with the accident cause is the key part of this research, in order to identify, evaluate and mitigate transportation risk. This paper presents a statistical analysis of passenger and freight train collisions in the United States from 2001 to 2015 to statistically analyze train collision frequency, severity, accident cause, and safety risk. The analysis finds that human errors and signal failures are among the most common causes of train collisions in U.S. in the 15-year study period. There is a significant decline in the overall train collision frequency by year. By observing these trends with respect to train collisions, possible accident prevention strategies could be developed and implemented accordingly.

Do We Need an Interdisciplinary Approach to Bring Consumers' Desires to e-Market?

2014 ◽  
Vol 5 (2) ◽  
pp. 1-12
Author(s):  
Patrick Letouze ◽  
David N. Prata
Keyword(s):  
United States ◽  
Text Mining ◽  
Marketing Strategy ◽  
United States Of America ◽  
Interdisciplinary Research ◽  
Interdisciplinary Approach ◽  
The United States ◽  
The Internet ◽  
Consumer Theory ◽  
Interdisciplinary Research Project

In 2012, the internet advertising revenue in the United States of America reached a total of 36.6 billion dollars, a growth of 15.2% when compared to 2011. The efficiency of a marketing strategy relies in the ability to understand and to direct the consumers' desires. In this work, the authors propose an approach that combines the Internet-Based Information Consumer Theory (IBICT) with semiotics to bring consumers' desires to e-Market. Hence, we present IBICT's framework as a collective network set based on a semiotic human-machine approach. For implementation purposes, we propose a text mining architecture towards IBICT's framework, which leads to an IBICT's architecture, and an Interdisciplinary Research Project Management (IRPM) approach to determine IBICT's dimensions.

scholarly journals Helicopter Pilots Encountering Fog: An Analysis of 109 Accidents from 1992 to 2016

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 994
Author(s):  
Alex de Voogt ◽  
Hilary Kalagher ◽  
Andrew Diamond
Keyword(s):  
United States ◽  
Decision Making ◽  
The United States ◽  
Meteorological Conditions ◽  
Transportation Safety ◽  
Online Database ◽  
Fatal Accident ◽  
Instrument Meteorological Conditions ◽  
High Prevalence ◽  
Helicopter Pilots

Helicopters have the ability to make maneuvers or precautionary off-airport landings to avoid flights into instrument meteorological conditions (IMC) such as fog. Flight accidents in which fog was encountered as well as inadvertent and intentional flights into fog were examined to understand their occurrence. A 25-year period in the United States using the National Transportation Safety Board online database was used to collect 109 accident reports of which 73 (67%) were fatal. Pilots flying intentionally into IMC were more likely to be a part of a fatal accident than those who did so inadvertently. Those pilots who were reported as being under pressure when encountering fog conditions were also more likely to be in an accident. The findings confirm a high prevalence and an added danger to intentional flights into IMC. In addition, decision-making under pressure when encountering IMC conditions is now linked to a higher proportion of fatalities, emphasizing that helicopter pilots should be made aware of these specific decision-making circumstances in their operations.

scholarly journals Review of Regulatory Emphasis on Transportation Safety in the United States, 2002-2009: Public versus Private Modes

Risk Analysis ◽  
2017 ◽  
Vol 38 (5) ◽  
pp. 1085-1101 ◽  
Author(s):  
Garrett C. Waycaster ◽  
Taiki Matsumura ◽  
Volodymyr Bilotkach ◽  
Raphael T. Haftka ◽  
Nam H. Kim
Keyword(s):  
United States ◽  
The United States ◽  
Transportation Safety ◽  
Public Versus Private

A case study in text mining: Textual analysis of the Territorial Papers

2019 ◽  
Author(s):  
Johannes Ledolter ◽  
Lea VanderVelde
Keyword(s):  
Text Mining ◽  
Federal Government ◽  
Topic Modeling ◽  
The United States ◽  
State Governments ◽  
Textual Information ◽  
Modeling Methods ◽  
Frontier Settlers ◽  
Initial Statistical Analysis

Abstract The Territorial Papers of the United States are a valuable and underused resource containing almost 10,000 documents written between 1789 and 1848 about the formation of new sovereign states from US territory. These communications between the federal government and frontier settlers comprise the actual discourse of the nation’s expansion over six decades. Digitizing the Territorial Papers permits the possibility of analyzing the entire corpus globally. Text mining and topic modeling methods give us a lens on the language patterns through which new state governments and the expanding nation were formed. An initial statistical analysis of the textual information provides a visualization of content, helps discern how ideals about governance emerged, and lays the foundation for developing more sophisticated hypotheses and theoretical constructs.

Sign in / Sign up

Export Citation Format

Share Document