Developing Safe and Efficient Driving and Routing Strategies at Railroad Grade Crossings Based on Highway-Railway Connectivity

The University of Kentucky will analyze the economic implications of temporal variation on the effectiveness of grade crossing safety treatments/countermeasures that will inevitably be caused by connected and autonomous vehicles. This will be accomplished by developing a data driven tool to study implications for all levels of connected and autonomous vehicles through market penetration and effectiveness. This tool can be adjusted to add implications of railroad crossings and other countermeasures to change the effectiveness. In traditional benefit cost analysis, rail safety countermeasure crash modification factors (CMFs), are assumed to remain constant over time, which can be 10 or 20 years or longer. The safety benefits are estimated assuming today’s vehicle fleet and yesteryear’s crash performance. Connected and autonomous vehicles will safety benefits not attributable to present day countermeasure investments. Therefore, excessive “credit” for the benefits of those savings accrue, as CAVs are not likely to benefit equally from these countermeasures. Thus, B/C may be significantly overestimated. In the aggregate, safety funds compete against other needs, and getting B/C estimates right is important from an efficiency standpoint. More importantly to the subject of grade crossing safety, there is almost certainly a differential benefit of these countermeasures to the different levels of CAVs, an in particular level 3.0 CAVs (those that only work or work best on some parts of the highway system). The proposed tool can be used to quantify these implications. It will be driven by actual and simulated crash data, as well as surrogate safety measures from the NDS database. The task deliverable will assist decision makers in making more informed and accurate grade crossing safety countermeasure investment decisions.