Holistic understanding of accident-causing patterns using mixed methods of analysis
File(s)
Author(s)
Manole, Iulia
Type
Thesis
Abstract
The safety of transport systems is vital to everyday life and global commerce. Safety Management Systems (SMS) are essential frameworks for identifying risks and implementing preventative measures. However, current safety assessment methods tend to rely heavily on quantitative data and reactive approaches, often overlooking the complex socio-technical nature of transport systems. This thesis proposes a comprehensive safety analysis framework that integrates mixed methods (quantitative and qualitative) and data sources to address both technical and human-related safety factors. A complementary framework for evaluating safety data quality is also developed.
Adopting a pragmatic and epistemological stance, the research explores real-world safety challenges through four case studies from maritime and aviation sectors. These include safety in UK ports (with a focus on onboard safety and future autonomous vessels), maritime piracy, and the overlooked safety of recreational seaplane pilots in Canada. Each case applies a combination of reactive, proactive and predictive approaches, showcasing the framework’s flexibility and depth. Data sources include official databases, investigation reports, tracking systems and voluntary reporting schemes. To complement quantitative gaps, this thesis draws on interviews, focus groups and questionnaires involving 152 participants. Thematic analysis offers insight into human factors, decision-making and socio-technical dynamics not captured in traditional databases.
The framework is designed for broader applicability across diverse transport and safety-critical sectors, particularly in domains with limited regulation, underreporting, or evolving technologies. It supports consistent, system-wide comparisons and informed risk management across modes. The findings advocate for integrating mixed-methods analysis and stakeholder engagement in SMS development and for enhancing policy to promote transparent, high-quality safety reporting. This work advances academic understanding and industry practice by providing a robust tool to improve safety outcomes and strengthen SMS frameworks in both mainstream and niche contexts.
Adopting a pragmatic and epistemological stance, the research explores real-world safety challenges through four case studies from maritime and aviation sectors. These include safety in UK ports (with a focus on onboard safety and future autonomous vessels), maritime piracy, and the overlooked safety of recreational seaplane pilots in Canada. Each case applies a combination of reactive, proactive and predictive approaches, showcasing the framework’s flexibility and depth. Data sources include official databases, investigation reports, tracking systems and voluntary reporting schemes. To complement quantitative gaps, this thesis draws on interviews, focus groups and questionnaires involving 152 participants. Thematic analysis offers insight into human factors, decision-making and socio-technical dynamics not captured in traditional databases.
The framework is designed for broader applicability across diverse transport and safety-critical sectors, particularly in domains with limited regulation, underreporting, or evolving technologies. It supports consistent, system-wide comparisons and informed risk management across modes. The findings advocate for integrating mixed-methods analysis and stakeholder engagement in SMS development and for enhancing policy to promote transparent, high-quality safety reporting. This work advances academic understanding and industry practice by providing a robust tool to improve safety outcomes and strengthen SMS frameworks in both mainstream and niche contexts.
Version
Open Access
Date Issued
2024-08-05
Date Awarded
01/07/2025
License URL
Advisor
Majumdar, Arnab
Ochieng, Washington
Sponsor
Lloyd's Register Foundation
Grant Number
GA\100086
Publisher Department
Department of Civil and Environmental Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)