Crashes are one of the main types of traffic accidents at railway level crossings. Although such crashes are rare, they incur large human and material losses. According to the Australasian Railway Association (ARA), 14 major level crossing crashes causing more than $100 million in damage occurred in 2012. In order to reduce or prevent crashes at railway crossings, many studies have been undertaken worldwide. Studies, ranging from historical crash data analysis to the use of high-end technology, have helped reduce the potential for crashes.
As most crash prediction models rely on historical crash data, it becomes difficult to measure major factors due to underestimated recorded data. To deal with this shortcoming, some studies have suggested using surrogate crash measurements as they have been found to have a strong correlation with real accidents. Thus surrogate crash measurements become good alternatives for crash prediction models. Recently, the use of traffic simulation and driving simulators has played an important role in the traffic safety field in conjunction with surrogate measurements.
Although emerging technologies, knowledge, and innovative interventions have been introduced to change driver behaviour, there is a lack of research on the impact of integrating Intelligent Transport Systems (ITS) technologies and transportation simulation.
In this research, the integration of traffic simulation and driving simulators has been implemented in order to accomplish a better understanding of how to reduce crashes. Through experiments using driving simulator, various ITS (eg: smart phone, audio warnings, and on-road flashing markers) have been identified in order to find the most effective safety system. Traffic simulation has been integrated with driving simulator in order to develop an improved evaluation tool to analyse traffic phenomenon at/nearby railway crossings.
The outcome of using ITS technologies, complemented with traditional signage, has been statistically compared with current safety systems (passive and active) at railway crossings in terms of compliance rate and vehicle speed profiles. In addition, a modified form of traffic simulation at railway crossings has been developed to obtain driving behaviour on a driving simulator to investigate how such behaviour could influence traffic flows.
Overall, this research provides a promising methodology for combining driving simulator and traffic simulation to evaluate railway crossing safety. In addition, the way in which the road network is impacted by driving behaviour at crossings has been analysed in detail using the purpose-built traffic simulation module. Thus, several scenarios were evaluated. For example, a range of traffic flows, train headways and local topography were able to be assessed.
In conclusion, each ITS intervention has the potential to increase safety at railway crossings if specific issues are taken into consideration. Results show positive impacts of introducing ITS complementary measures that ensure a better safety outcome at both active and passive crossings. Passive crossings are normally implemented in rural areas where low traffic volumes and low train frequencies are expected, ITS devices in this case improve traffic performance by showing less delay time and less stops as well as a higher compliance rate.