Developing an understanding of the relationship between railway safety and operational performance

Wemakor, Wendy ORCID: 0000-0003-4631-4622 (2022). Developing an understanding of the relationship between railway safety and operational performance. University of Birmingham. M.Phil.

Text - Accepted Version
Available under License All rights reserved.

Download (1MB) | Preview


Railway risk and delays are most often analysed apart with no common language for collecting data making it difficult to analyse or compare railway safety and operational performance. This study attempts to develop an understanding of the relationship between the two performance measures by comparing them on a common scale (in this case using monetary terms/values) while building on existing approaches in the industry.

To achieve this, a causal loop diagram (CLD) is firstly used to show the existing known relationships between risk and delays by documenting their causes using data from the Rail Safety and Standard Board (RSSB) and Network Rail respectively. This part of the research identified differences in the classification of some events although these datasets are collated from the reporting of the same incidents on the network. Also identified are the common causes of risk and delays.

The common causes which are identified are defined for the purpose of this research as the common performance influencing factors (CPIFs). The existence of CPIFs shows that there are incidents/events that can impact both the safety and operational performance of the railways. Understanding the relationship between the two measures and being able to compare them on a common scale can potentially be beneficial to stakeholders especially in finding solutions to reduce the occurrence of CPIF related incidents on the network.

As the CPIFs can lead to both risk (typically measured by Fatalities and Weighted Injuries) and delay consequences (usually measured by delay minutes), it is appropriate to convert their values into a common measure, and this is possible using existing monetary values for both measures. That enabled this research to categorise on a common scale the impact CPIFs have on railway safety and operational performance. This thesis develops an approach to help convert delays into monetary values.

It is noted that the values which are taken from published sources are a representation of what delays and risks on the network cost the industry. Given the approach adopted, this creates a reasonable estimate of the relative significance of an event for both risk and performance but would need to be supplemented with real life data from particular routes and contracts to be used to support decision making. This is attempted in the thesis by using route data on trespass incidents for which scenarios were developed to demonstrate the use of the approach in the stakeholder decision making process.

In conclusion, the thesis develops our understanding of the relationship between railway safety and operational performance by identifying the common causes of risk and delays on the network (i.e., CPIFs). The identification of the CPIFs have made it possible to develop an approach for comparing risk and delays on a common scale. In addition, the researcher also identified the need for a consistent language and data collection/analysis approach (e.g., an ‘event – based’ reporting system). It is hoped that over time, this would enable a much clearer understanding to be developed and be more efficient in terms of data capture.

Type of Work: Thesis (Masters by Research > M.Phil.)
Award Type: Masters by Research > M.Phil.
Licence: All rights reserved
College/Faculty: Colleges (2008 onwards) > College of Engineering & Physical Sciences
School or Department: School of Engineering, Department of Electronic, Electrical and Systems Engineering
Funders: None/not applicable
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TF Railroad engineering and operation


Request a Correction Request a Correction
View Item View Item


Downloads per month over past year