| dc.contributor.author | Dhingra, Neeraj | |
| dc.description.abstract | he FRA mandated railroad companies to install a new monitoring system known as Positive Train Control (PTC). This system overlays sensors, signals, and transponders over existing track and other wayside infrastructure. Technologists designed the system to prevent accidents mainly caused by human negligence and communications. However, PTC will not address track-related defects, which is the second dominant cause of accidents.
A new track monitoring system called Railway Autonomous Inspection Localization System (RAILS) was proposed to address track-related accidents. RAILS is based on low-cost sensor technology that identifies defect symptoms, ranks their severity, classifies defect types, and localizes their positions. So, RAILS technology can augment the PTC by identifying track-related issues.
The main objectives of this dissertation are: (1) To compare the potential performance of RAILS with traditional inspection methods based on its fundamental theory of operation; (2) To identify factors contributing to railroad accidents; and (3) To determine and rank factors responsible for severe financial damages caused by railroad accidents.The first two objectives will help compare the proposed technology and identify the major factors responsible for causing train accidents. The final objective will help to categorize accidents based on the potential financial damage severity. Categorizing such incidents would help to create a database that prioritizes issues and suggest possible countermeasure based on the problems.
The study's key findings are as follows: (1) RAILS is more efficient in conducting continuous inspection and identifying potential defects than traditional systems by 33%, with only two trains per day and a 50% first-pass detection probability; (2) Nonparametric methods provide implicit information about rail accidents and function better than parametric methods by highlighting factors that are responsible for causing accidents rather than identifying the cause-and-effect relationship; (3) The most significant reasons for causing the financial damages are the number of derailed freight cars and the absence of territory signalization; and (4) Nonparametric methods automatically categorize rail accidents and, using text narratives, highlight causative factors responsible for a train derailment. | en_US |
| dc.publisher | North Dakota State University | en_US |
| dc.rights | NDSU policy 190.6.2 | en_US |
| dc.title | Three Essays on Railroad Safety Analysis Using Non-Parametric Statistical Methods | en_US |
| dc.type | Dissertation | en_US |
| dc.date.accessioned | 2024-01-02T17:23:42Z | |
| dc.date.available | 2024-01-02T17:23:42Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | https://hdl.handle.net/10365/33495 | |
| dc.subject | Automated track monitoring system | en_US |
| dc.subject | LIME | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Non-Parametric Statistics Models | en_US |
| dc.subject | Semi-Supervised Learning | en_US |
| dc.rights.uri | https://www.ndsu.edu/fileadmin/policy/190.pdf | en_US |
| ndsu.degree | Doctor of Philosophy (PhD) | en_US |
| ndsu.college | Business | en_US |
| ndsu.department | Transportation and Logistics | en_US |
| ndsu.program | Transportation | en_US |
| ndsu.advisor | Bridgelall, Raj | |
