The influence of system design-related factors on the safety performance of metro drivers

Abstract

Although it is accepted that system design affects train driver performance, the literature related to this phenomenonin relation to urban railways and metro systems in particular-is scarce. Metro systems differ significantly from mainline railways, being closed systems, with shorter headways, a greater number of stations and more signals encountered. This paper aims to investigate the effects of design-related performance shaping factors on metro driver performance, by analysing historical incident records for the 2011-2013 period on the Tyne & Wear (T&W) Metro (UK). Bivariate statistical analysis has been used, to assess the potential inter-dependency of the performance shaping factors and other common causal factors, for various driver-related incident types. In addition to category A signals passed at danger incidents, station overruns, and incidents associated with station procedures have also been assessed. The results show the significant importance of the location (design) based performance shaping factors in incident propagation mechanisms in the Metro. The two years under investigation display increased consistency between driver-related incidents and locations, rather than time of day, or season. In addition, the highest correlation between incidents has been found in terms of locations. Deviations from a standardised T&W Metro station design were found to associate with either an increase or decrease in incident rates, depending on whether additional complexity or simplicity was introduced. Although the features of metro systems suggest improved route knowledge and system familiarity among drivers, the results show that this can actually lead to an unsatisfactory safety-related performance during non-routine operations, e.g. engineering works, or driving in sidings.

Multi-modelling for Decarbonisation in Urban Rail Systems

“…While the example presented in this paper has used a combination of bespoke source models and models taken from the authors' previous work, this basic multi-model can now be extended to accommodate other models, as long as the model either is FMI compliant or could be made compliant to generate an FMU. A specific future step is to develop this multi-model to examine more realistic power consumption performance, particularly for regenerative braking [13] and a more accurate, actual driver behaviour model based on Metro performance data [54] and humanin-the-loop simulation. Importantly, the ability of the multi-model to support multiple instances means it will be possible to model not just the power implications of driver performance on one driver but also how different drivers with different performance cause system interactions in the timetable and, subsequently, with power performance.…”

“…Other future directions include applications as diverse as agent-based modelling for optimised design for buildings usage [55], for subway climatology [56] or optimising construction processes and logistics to minimise both transportation carbon emissions and material waste [57]. [17] Driver model Driver advisory systems [16,54]; driving style [23] Train model Passenger loading [11]; resistance due to weather/wind [23]; adhesion [23]; mix of diesel/heavy rail with urban electric rail (further use of Karlsuhe model [48])…”

Investigation into Effects of System Design on Metro Drivers’ Safety-Related Performance: An Eye-Tracking Study

“…This is the second largest urban rail system in the UK and the only one powered by an overhead DC 1500 V supply network. Further details on the T&W network can be found in [9][10][11][12]. The in-field eye-tracking experiment was designed as a naturalistic driving study.…”

“…In each trial only certain timeframes were analysed: 15 s before a full stop at a station and 10 s before a departure from a station. Previous research [11,12,14] suggested a relationship between a type of physical environment at a station and drivers' performance, which was selected as an avenue for this research. The arrival timeframes were selected to analyse potential effects of the built environment on selection of a stopping position, or distractions causing a dip in performance.…”

Data Analysis to Study Sub-threshold Delays Incurred by Tyne and Wear Metro Trains

“…Performance of metro systems and train delays have been studied by Marinov and Viegas [1], Rjabovs et al [2], Rjabovs and Palacin [3], Wales and Marinov [4], Dampier and Marinov [5], Darlton and Marinov [6], Rjabovs and Palacin [7], Rjabovs and Palacin [8], and Powell et al [9,10].…”