ATSB – RO-2018-004
opportunity to have an afternoon nap as he was in the habit of doing. Prophylactic napping has been shown to be beneficial in supplementing sleep time and reducing the effects of fatigue.[1]
Sydney Trains’ rostering, fatigue-management and health policies were examined to determine if they contributed to the incident. The investigation found that while Sydney Trains had policies and systems in place to ensure that drivers were fit for duty and rostered in a manner to manage their fatigue levels, there were some inconsistencies with the rostering of this driver and Sydney Trains’ operating procedure for managing shift work and rostering.
Sydney Trains’ operating procedure was to ‘make sure there are adequate breaks between shift cycles. Days off should be a minimum of two consecutive days’. Prior to the incident the driver had worked 5 consecutive days and then had a break of one day, worked another day, then had a one day break.
Another rostering principle inconsistent with the driver’s roster was that start times should be consistent and move in a forward rotation. This driver had start times that moved from a regular morning start (0600-0700), to a single overtime shift that started at 1500, then to a very early morning start of 0312.
Sydney Trains’ management systems provide guidance for management and employees to ensure there is an awareness of countermeasures in this area. The procedures are detailed and cover eventualities such as shift changes.
Sydney Trains use a bio-mathematical fatigue modelling program known as the Fatigue Audit Interdyne (FAID) to assess the suitability of the roster for managing fatigue risk. Bio-mathematical models attempt to predict the effects of different working patterns on subsequent job performance, with regard to the scientific relationships between work hours, sleep and performance.[2] The FAID score predicts the risk of fatigue associated with the opportunities provided by the organisation for an individual to obtain restorative sleep.[3] Guidance suggests that scores between 80 and 100 have a high fatigue likelihood. The FAID score for the driver on the day of the incident was 51. Other bio-mathematical scores were also calculated for the driver’s shift. These also indicated that the driver was in the low range for being at risk of fatigue. However, any bio-mathematical model cannot account for the hours of sleep actually achieved by individuals, nor for the quality of that sleep.
Driver distraction and inattention are major contributing factors in accidents. While there are many differing definitions for distraction and inattention, for the purposes of this investigation the following definitions will apply:
It may be possible to be inattentive even where there is no distracting event. The driver said that he was not distracted by any radio calls or external events and was paying attention as he approached Richmond Station. He said that he had driven into this station on
hundreds of occasions and knew that the approach to the buffer stop was a critical time to apply
- ↑ Folkard S, Robertson KA, Spenser MB (2006). The development of a fatigue / risk index for shiftworkers. p.39.
- ↑ Dawson D., Noy YI, Harma M, Akerstedt T, Belenky G. (2011). Modelling fatigue and the use of fatigue models in work settings. Accident Analysis and Prevention, 43, p. 551.
- ↑ Ibid., p. 553.
- ↑ Senders JW. (2013) Driver distraction and inattention: a queueing theory approach. Driver distraction and inattention. Advances in Research and Countermeasures. Ashgate. p.55
- ↑ Regan MA, Strayer DL. (2014) Towards an understanding of driver inattention: taxonomy and theory. Journal of the Association for the Advancement of Automotive Medicine.
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