Railroad Transportation of Dangerous Goods in Canada: Data-Driven Risk Analysis and Implications for Emergency Management

The purpose of this study is to examine key risk factors associated with the railroad transportation of hazardous materials (hazmat) and to develop a data-driven risk analysis methodology using rail incident records from 1999 to 2023 across Canada from multiple resources and creating a risk management framework in case of hazmat release. In the first stage, we will identify the primary factors affecting hazmat release during rail transportation, such as time, location, activity, track type, train characteristics, load specifics, and weather-related attributes. At this stage, utilizing machine learning techniques, including Logistic Regression, Decision Trees, and a two-hidden-layer Neural Network, we aim to facilitate the second stage of the study. This subsequent stage intends to provide insights and recommendations for effective risk management strategies such as risk mitigation and disaster prevention, preparedness, and response strategies within the context of hazmat rail transportation, particularly in Canada.

Name thesis:degree_name

M.Sc. Management

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Faculty of Business

Department dc:contributor.department

Faculty of Business Programs

Grantor

Brock University

Year dc:date.issued

2024

Author dc:creator

• Izadpanah, Marjan

• Hazmat release
• Rail transportation
• Supervised machine learning
• Risk Management
• Data-driven risk analysis

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/10464/18902

OAI identifier oai:identifier

oai:brocku.scholaris.ca:10464/18902