Elements inducing additional stress and workload during departures 135

potential environmental factors. Similar layouts are used at Felling and Gateshead Stadium albeit a narrower FOW angle, and do not demonstrate mirror usability

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issues. This corroborates importance of the FOW angle as PSF. Other factors, e.g.

crowding at the back of the train and a closely located mirror can contribute to workload increase too. However, it is unknown to what extent each of these factors influence the usability of the mirror.

Similarly to the arrivals, results for the running signal at Heworth are indicative of stress-inducing PSFs. Compared to other stations, it is located the furthest from the drivers. Assuming that the low TFC/high AFD suggests better usability of an element, there is a negative correlation between the distance to a running signal and signal’s usability.

7.4.5 Failures to check signals during departures

As it was mentioned before, start against a signal SPaDs are rather common in railway industry, especially on DOO commuter routes, and Tyne & Wear Metro is no exception. According to RSSB (2013), there were at least 39 SASSPaDs with

passenger trains in the UK between 2010 and 2013. Moreover, SASSPaD risk is higher in DOO systems according to Basacik et al. (2009). As presented in Precursor Incident Model by RSSB (2011b), many of driver-related accidents stem from

smaller incident acting as precursors. In this particular situation, failures to check a signal aspect before departure can lead to SASSPaD under certain circumstances.

However, it is necessary to assess how real is such proportion of violations on departure.

The percentage of the violations found in trials with less than 50% sampling measure is out of proportion. All of the registered violations in those trials are recorded at stations with an island platform. Hence it is possible that the drivers were using side gazes to check a signal due to mainly focusing on a mirror. Such side gazes are rarely recorded, especially if an eye-tracking set is a little bit disturbed on a

participant’s head. The same issue was observed by RSSB (2005) who reported a lot of lost data due to glares, equipment failures and side gazes. Drivers’ main areas of attention are located on the same side on arrivals. Hence no difference in descriptive statistics has been found when the 3 trials with low sampling quality are excluded from the analysis. This could be a reason for low TFC on arrival signals. However, this theory would mean Heworth having significantly higher signal metrics on arrivals, which is not the case.

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Apart from technical issues embedded in methodology, it is known for people visual attention to travel ahead of fixations by about 0.25 seconds (Deubel, 2008; Holmqvist et al., 2011). Hence if participants wanted to fixate on a signal right before departure, the actual fixation could have happened after a train started moving (not analysed).

Considering that there is a slight lag between applying a power and start of motion, it is possible to claim that the lag between visual attention and fixations is negligible for this experiment.

Most importantly, whatever the reason for equipment not registering a fixation on a signal, even if one of these cases is a genuine violation or a lapse, this creates a significant risk factor for metro operations. This is especially important when lack of fixations on a running signal on arrival is taken into account. Even checking a signal 10 seconds before departing, if other tasks are done in-between, cannot be safe.

O'Connell et al. (2015) claim that dynamic occurrence of task demands can impact on individual’s ability to recall previous observation.

Furthermore, the participants do not differ in ways how they interacted with the

signals, especially on departure, meaning that if there is a problem, it is wide-spread.

After the questionable violations are removed, the seven violations left are at stations where a running signal is located at least 30 metres away from a stopping position. It takes 7 to 10 seconds to cover 32m and 45m respectively metres from a complete stop. Hence the drivers, especially experienced drivers, might believe that they have time to check a signal and react even if they do it only after departure. However, Multer et al. (2015) claim that very responsive controls of electric trains mean more in-cab gazes for speed information monitoring and subsequent higher risk of SPaDs.

If the drivers decide to divide the two tasks (safe dispatch and signal checking), they might find themselves in situations when other driving tasks draw their attention when a train is already in motion. It is also possible that the station stops and concurrent PTI demands simply cause a memory lapse, as station stops can cause

disengagement from a driving task (Naweed and Rainbird, 2014).

RSSB (2005) found that drivers’ experience does not affect visual behaviour on signals but only between station signals were checked. Tripathi and Borrion (2016) discovered that emphasising on-time performance affects metro drivers’ safety and security related performance. It also means that other competing goals can have similar effect on metro drivers’ performance. RSSB (2007b) claim that drivers are

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highly motivated to check signals on departure and non-compliance is highly unlikely.

On the other hand, another RSSB report highlights a risk of drivers departing a station without fully checking all doors closed due to time pressures (Basacik et al., 2009). In DOO systems, where most risks are related to the PTI, the drivers might prioritise DOO equipment and skip signals due to other pressures. This demonstrates why same findings cannot always be applied to mainline railways and metro systems as a combination of design features and risk profiles can significantly affect drivers’

priorities. This is further supported by five out seven violations happening at the stations with significantly higher passenger loadings.

As bigger distances do not correlate with increased number of failures to check a signal, it is possible that another design-related factor can have influence on failure propagation. This factor is the FOW angle. Felling and Pelaw, which have relatively high angles and distances, show the highest proportion of violations. In comparison, Heworth, where the distance is also high but the angle is significantly lower, does not have as many failures to check a signal.