- Title
- SOM clustering and modelling of Australian railway drivers' sleep, wake, duty profiles
- Creator
- Hudson, Irene L.; Leemaqz, Shalem Y.; Kim, Susan W.; Darwent, David; Roach, Greg; Dawson, Drew
- Relation
- Artificial Neural Network Modelling p. 235-279
- Relation
- Studies in Computational Intelligence 628
- Publisher Link
- http://dx.doi.org/10.1007/978-3-319-28495-8_11
- Publisher
- Springer
- Resource Type
- book chapter
- Date
- 2016
- Description
- Two SOM ANN approaches were used in a study of Australian railway drivers (RDs) to classify RDs' sleep/wake states and their sleep duration time series profiles over 14 days follow-up. The first approach was a feature-based SOM approach that clustered the most frequently occurring patterns of sleep. The second created RD networks of sleep/wake/duty/break feature parameter vectors of between-states transition probabilities via a multivariate extension of the mixture transition distribution (MTD) model, accommodating covariate interactions. SOM/ANN found 4 clusters of RDs whose sleep profiles differed significantly. Generalised Additive Models for Location, Scale and Shape of the 2 sleep outcomes confirmed that break and sleep onset times, break duration and hours to next duty are significant effects which operate differentially across the groups. Generally sleep increases for next duty onset between 10 am and 4 pm, and when hours since break onset exceeds 1 day. These 2 factors were significant factors determining current sleep, which have differential impacts across the clusters. Some drivers groups catch up sleep after the night shift, while others do so before the night shift. Sleep is governed by the RD's anticipatory behaviour of next scheduled duty onset and hours since break onset, and driver experience, age and domestic scenario. This has clear health and safety implications for the rail industry.
- Subject
- SOM; Australian railway drivers; sleep patterns
- Identifier
- http://hdl.handle.net/1959.13/1322516
- Identifier
- uon:24594
- Identifier
- ISBN:9783319284958
- Language
- eng
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