Phase 1 - Data Collection

The data collection focussed on understanding the existing rail level crossing system from multiple perspectives using multiple methods. Semi-naturalistic on-road studies of driver behaviour were conducted, combining the collection of:

  • • Vehicle measures and video recordings of the driving environment to understand objective driver behaviour.
  • • Eye tracking to understand where visual attention was directed within the road and in-vehicle environments.
  • • Verbal protocol analysis (VPA), analysed using network metrics, to understand drivers’ situation awareness when traversing crossings.
  • • Cognitive task analysis interviews using the Critical Decision Method (CDM) approach to explore driver decision-making around whether to ‘stop or go’ at the crossing.

This mix of methods included both objective and subjective data, telling us both what drivers were doing and what they were thinking while negotiating the crossing.

Although these on-road studies provided in-depth data, being largely naturalistic it was clearly not possible to control the environment or manipulate driver exposure to specific conditions such as whether a train was present during participants’ encounters with rail level crossings. This approach therefore yielded more data on situations when there was no train present, rather than situations in which the road user had to make a decision involving an approaching train. To address this, we also conducted a selfreport diary study to increase the data available on decision-making at rail level crossings, using an adapted CDM approach. Importantly, this study also provided insight into the decision-making processes of cyclist, pedestrian and motorcyclist users at rail level crossings.

Finally, to gain a broader understanding of the whole system and its functioning, we interviewed subject-matter experts, analysed key system documentation and rode in train cabs during normal passenger operations.

See Chapter 2 for an overview of the data collection methods and Chapter 4 for more information about their application in this research.

< Prev   CONTENTS   Source   Next >