McPhee, Brendan L.2023-03-012023-03-012017https://unbscholar.lib.unb.ca/handle/1882/13281The use of video monitoring equipment was explored to determine its effectiveness at collecting open source data from passing rail cars, to provide emergency organizations with an enhanced resolution of data on the movement of dangerous goods (DG) by rail. A camera was installed at a former train station in Sussex, New Brunswick, where it collected video data 24 hours a day from July 2016 until January 2017. The first 3 months of video data were manually transcribed to capture detailed rail car information, obtaining important attributes such as the container code, weight limits, and dangerous good placard. A total of 17,864 rail cars were identified; 94% of container codes and 87% of weight limits were legible from the video data, and only 3 of the 10,339 DG placards were unidentifiable. National accident rates were used due to the absence of local data to obtain estimates of the probability of a rail-related accident occurring anywhere along the Sussex subdivision line. The use of a video camera was determined to be an effective method of collecting rail cargo data to supplement emergency organizations with detailed historic data in addition to the current data sources provided through railway companies. The manual transcription process has potential to become automated, which could allow real-time rail data to be provided to communities. This study highlighted the lack of rail metrics, such as gross tone miles and number of DG carloads, available at local rail subdivision levels, which are ultimately obtainable through the use of open source data.text/xmlx, 116 pageselectronicen-CAhttp://purl.org/coar/access_right/c_abf2master thesis2017-08-16Hanson, TrevorCivil Engineering