Data-driven modeling method for analyzing grade crossing safety
A grade crossing is defined as an intersection between a roadway and a railway at the same elevation or grade. Multiple new prevention measures have been implemented to reduce the number of train-vehicle collisions; however, crossing safety is still a major issue as accidents still frequently occur. The push for data-driven models to evaluate risks at grade crossings has also increased to keep up with the changing technologies. There are many different protection types (gates with bells, cross-buck, stop-sign, mirrors and etc.) that serve to warn or stop oncoming traffic. Many attributes have an inherent impact on accident frequency; including the protection type, train speed, traffic volume and e.t.c. To address which factors are most important, we propose a data-driven modeling method to effectively analyze the impact of multiple factors that affect crossing safety and subsequently provide scientific insight for key factors for enhancing crossing safety. In this work, the Canadian crossing accident database for the years of 2004 - 2013 was used with additional generated features to enhance the scope of the study. These include features that were computed using GIS and sightline measurements. Data-driven modeling using RandomForests were used to rank and analyze 21 attributes for each protection type. From the analysis results it is possible to identify which key factors have the highest influence on improving safety and collision prediction at grade crossings.