Validation of a Driving Simulator Study on Driver Behavior at Passive Rail Level Crossings

OBJECTIVE

The behavioral validation of an advanced driving simulator for its use in evaluating passive level crossing countermeasures was performed for stopping compliance and speed profile.

BACKGROUND

Despite the fact that most research on emerging interventions for improving level crossing safety is conducted in a driving simulator, no study has validated the use of a simulator for this type of research.

METHOD

We monitored driver behavior at a selected passive level crossing in the Brisbane region in Australia for 3 months ( N = 916). The level crossing was then replicated in an advanced driving simulator, and we familiarized participant drivers ( N = 54) with traversing this crossing, characterized by low road and rail traffic.

RESULTS

We established relative validity for the stopping compliance and the approach speed.

CONCLUSION

This validation study suggests that driving simulators are an appropriate tool to study the effects of interventions at passive level crossing with low road and rail traffic, which are prone to reduced compliance due to familiarity.

APPLICATION

This study also provides support for the findings of previous driving simulator studies conducted to evaluate compliance and approach speeds of passive level crossings.

Systems-based approach to investigate unsafe pedestrian behaviour at level crossings

Crashes at level crossings are a major issue worldwide. In Australia, as well as in other countries, the number of crashes with vehicles has declined in the past years, while the number of crashes involving pedestrians seems to have remained unchanged. A systematic review of research related to pedestrian behaviour highlighted a number of important scientific gaps in current knowledge. The complexity of such intersections imposes particular constraints to the understanding of pedestrians' crossing behaviour. A new systems-based framework, called Pedestrian Unsafe Level Crossing framework (PULC) was developed. The PULC organises contributing factors to crossing behaviour on different system levels as per the hierarchical classification of Jens Rasmussen's Framework for Risk Management. In addition, the framework adapts James Reason's classification to distinguish between different types of unsafe behaviour. The framework was developed as a tool for collection of generalizable data that could be used to predict current or future system failures or to identify aspects of the system that require further safety improvement. To give it an initial support, the PULC was applied to the analysis of qualitative data from focus groups discussions. A total number of 12 pedestrians who regularly crossed the same level crossing were asked about their daily experience and their observations of others' behaviour which allowed the extraction and classification of factors associated with errors and violations. Two case studies using Rasmussen's AcciMap technique are presented as an example of potential application of the framework. A discussion on the identified multiple risk contributing factors and their interactions is provided, in light of the benefits of applying a systems approach to the understanding of the origins of individual's behaviour. Potential actions towards safety improvement are discussed.