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Reliability-based risk analyses of connected vehicles at highway-rail grade crossings
This dissertation performs reliability-based risk analyses of connected vehicles (CVs) at highway-rail grade crossings (HRGCs). Trains are assumed to have onboard units that transmit train location and speed information to CVs via dedicated short-range communications (DSRC). The stopping distance and time to collision of a vehicle are the demand function in the reliability-based risk analysis. The demand function consists of probability density functions of a vehicle’s initial speed, perception- reaction time, initial deceleration rate, final speed, and final deceleration rate. Train arrival time depending on the train speed and DSRC transmission range is the supply threshold for calculating the risk of collision at passive HRGCs. The supply threshold can also be the transmission range’s projected highway distance for CVs and sight distance for non-CVs at active HRGCs. After deriving probability density functions of demand variables from the published data, Monte Carlo simulations were used to calculate the probabilities that a CV would fail to stop within the transmission range or train arrival time. The risk of collision based on historical driving data is used to evaluate the safety levels of CV-equipped HRGCs. The result of the sensitivity analysis for the demand function shows that the initial speed is the most sensitive factor to the stopping distance. Also, the result of the reliability-based risk analysis shows that the risk of collision for CVs is lower than that for non-CVs. For passive HRGCs, the transmission range of an approaching train to CVs should be 600 meters for CVs. For active HRGCs, the transmission range of an approaching train to CVs should be 300 meters. The economic evaluation of whether RSUs are needed at active HRGCs shows that installing OBUs on trains is a more cost-effective and feasible approach to improvement the safety.
Statistics|Civil engineering|Electrical engineering
Hsu, Chung-Jen, "Reliability-based risk analyses of connected vehicles at highway-rail grade crossings" (2016). ETD collection for University of Nebraska - Lincoln. AAI10003789.