Initial findings show that the safest railway organisations in terms of safety outputs (low injuries and fatalities) achieve the highest maturity scores, while organisations with a significant number of injuries are assessed with lower maturity scores. Maturity scores equal or higher than 26 indicate organisations that tend to manage risk efficiently. Moreover, scores higher than 33 indicate organisations that tend to optimise both their safety standards and risk management. On the other hand, safety maturity scores lower than 26 represent organisations that should improve their safety standards.
To define any relationships between safety maturity and organisations safety performance, correlation tests were performed. To determine the appropriate test, first the distribution of the four variables that express organisations actual performance - precursors, top events, injuries and fatalities - was investigated. Data showed that none of the variables satisfy the criteria of normality. Therefore, non-parametric correlation tests should be conducted. For this, the Kendall’s tau and Spearman’s rho statistics, which both use the ranking of data to
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_________________________________________________________________________ calculate correlation coefficients, could be applied. However, although the two coefficients are equivalent, there is much reason to indicate that Kendall’s statistic is a better estimate of the correlation in relative small samples (Field, 2009, p.186); hence, it was chosen in this case8.
The value of Kendall’s tau, !, coefficient lies between -1 and 1. Its sign represents the direction of the relationship between the two variables (either positive or negative) whilst its absolute value reflects the strength of the relationship. The higher the value of ! the stronger the agreement between the two variables, while 0 value indicates that the two variables are completely independent (Field, 2009). Kendall tau is expressed by:
! ! !!!!!!!!! ! !!
!
! !! !!!!! (5-1)
where, S represents the number c of concordant pairs minus the number d of discordant pairs within the n sample of observations. For a pair of bivariate observations (Xi,Yi) and (Xj,Yj), if (Xj -Xi) and (Yj - Yi) have the same sign (i.e. both positive), then that pair is concordant, while if they have opposite signs the pair is considered discordant (Nelsen, n.d.).
The performed statistical test showed only one significant correlation (Table 5-5, Figure 5-5). The significant relationship is found between the maturity score and the number of recorded injuries. In the surveyed sample, the maturity score was appreciably negatively correlated (! ! -.357, p<.01) with the total number of injuries. This indicates that the higher the maturity score of metros, the lower the number of the recorded injuries.
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Figure 5-5 Maturity score versus reported injuries
Although correlation is not causality, it may be postulated that the characteristics that lead to a higher maturity index would be likely also to lead to a safer metro performance. It is noteworthy that data for the number of injuries is more likely to be accurate than for precursors or top events. This is explained because monitoring injuries is a legal requirement in most countries, as noted in Chapter 3.
On the other hand, data show that no correlation is identified between the maturity score and precursors, top events or fatalities. For the precursors and top events, this is explained due to the fact that not all metros monitor the same number of precursors and top events. Regarding fatalities, the very small number of observations across all metros could not lead to any useful findings. However, observations of metro behaviour over the years indicate that the more safety-conscious metros tend to record more accident precursors and top events and do so more rigorously, compared to less mature organisations.
To adjust for metros that did not provide a full set of precursors data, the number of precursors that metros could observe was calculated based on the Equation 5-2:
!
!!
! !!!"#
(5-2)
where, PA is the adjusted number of precursors, P is the number of precursors observed for
a specific year, O is the number of observations (including months with zero incidents) and Omax is the number of maximum observations (listed precursors ! 12) for that year.
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Table 5-5 Correlations between maturity score and recorded variables
Maturity Score Variables Kendall’s tau correlation
coefficient Statistical significance at 99% confidence level Spearman’s rho correlation coefficient Statistical significance at 99% confidence level Precursors -.156 .146 -.210 .157 Adjusted Precursors -.130 .234 -.173 .256 Top Events .092 .425 .076 .638 Injuries -.357 .001 -.492 .001 Fatalities .002 .987 .002 .992
where, precursors sample equals to 45, top events sample equals to 41, injuries sample equals to 43, fatalities sample equals to 30,
However, even after the adjustment, results indicate no significant correlation between maturity score and adjusted precursors. Subsequently, for both the monitoring of precursors and top events reliability considerations arose. Despite many efforts made to collect data with a similar degree of rigour across all metros, results verify that the safety-conscious metros are more objective than others in monitoring and registering them. Subsequently, having reviewed the relationships between safety maturity and organisations actual safety performance, it can be claimed that if an organisation has high safety maturity score then any data, which have been by law reported, derived by this organisation can be considered trustable and reliable for further analysis.
5.12
Summary
This chapter has presented the framework for the development of the R-PSFs taxonomy. To do so, a literature review of five existing railway PSF taxonomies was first conducted. The findings of the review were then augmented with the results of eleven additional PSF taxonomies from well-known and widely implemented techniques. These identified factors were tested for overlap and then compared with the hierarchical task analysis of railway operators outlined in Chapter 2. This resulted in a list of 43 factors divided into seven main categories, based upon their common characteristics. For each category and factor, specific definitions have been provided and examples are given. This R-PSF taxonomy will be validated using the analysis of 479 accident and incident reports gathered from several railway stakeholders worldwide. Therefore this chapter has addressed a major limitation of existing railway PSF taxonomies.
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_________________________________________________________________________ The latter part of this chapter also introduced a safety maturity model (SMM), which provides a novel approach to assessing (i) the safety levels of railway organisations and (ii) the quality of their reporting systems. This SMM was tested using safety data from a number of metro railways around the world. It is also implemented for the purposes of this thesis to assess the safety maturity of the organisations that provided data to this research, and subsequently the quality of such data.