Browsing by Author "Mudgal, Abhisek"
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Item Developing and Validating a Model of Left-Turn Crashes to Support Safer Design and Operations(Center for Transportation Studies, University of Minnesota, 2018-09) Davis, Gary; Gao, Jingru; Mudgal, AbhisekThis report documents work done to advance the state of art in crash simulation. This includes: (1) A field study to collect data on drivers’ left-turn gap acceptance and turning times, and development of statistical models that can be incorporated into a crash simulation model; (2) The use of Markov Chain Monte Carlo computational tools to quantify uncertainty in planar impact reconstruction of two-vehicle crashes; (3) A method for combing the results from planar impact reconstruction with event data recorder pre-crash data to estimate descriptive features of actual left-turn crashes. This is applied to several left-turn crashes from the National Highway Traffic Safety Administration’s NASS/CDS database; (4) A left-turn crash simulation model incorporating the above results. Initial model checking is performed using estimates from the reconstructed NASS/CDS cases as well as results from a previous study on left-turn crash risk. Also described is a method for simulating crash modification effects without having to first simulate crashes as rare outcomes in very large numbers of gap acceptances.Item Field Study of Driver Behavior at Permitted Left-Turn Indications(Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2013-03) Davis, Gary A.; Mudgal, AbhisekA digital video camera was used to record left-turning vehicles and through vehicles at an urban intersection. A total of 39 left-turn events, with a total of 195 gap decisions, were identified and vehicle trajectories corresponding to those were extracted from the video and transformed into real coordinates using photogrammetry. Bayes estimates of each opposing vehicle’s distance, speed, and time-to-arrival were then computed from the trajectories and used as predictors in logit models of acceptance/rejection decisions. It was found, when models are penalized for the numbers of their parameters, that arrival time, the ratio of initial distance to initial speed, was best predictor. This contrasts with an earlier study that found distance clearly superior to arrival time. This may be due to the fact that in the current study, speeds and initial distances were substantially higher than in the earlier study.