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The use of naturalistic driving studies (NDSs) for driver behavior research has skyrocketed over the past two decades. Intersections are a key target for traffic safety, with up to 25-percent of fatalities and 50-percent injuries from traffic crashes in the United States occurring at intersections. NDSs are increasingly being used to assess driver behavior at intersections and devise strategies to improve intersection safety. A common challenge in NDS intersection research is the need for to combine spatial locations of driver-visited intersections with concurrent video clips of driver trajectories at intersections to extract analysis variables. The intersection identification and driver trajectory video clip extraction process are generally complex and repetitive. We developed a novel R package called ndsintxn to streamline this process and automate best practices to minimize computational time, cost, and manual labor. This paper provides details on the methods and illustrative examples used in the ndsintxn R package.
Process data refer to data recorded in the log files of computer-based items. These data, represented as timestamped action sequences, keep track of respondents response processes of solving the items. Process data analysis aims at enhancing educatio
Naturalistic driving data (NDD) is an important source of information to understand crash causation and human factors and to further develop crash avoidance countermeasures. Videos recorded while driving are often included in such datasets. While the
R is a programming language and environment that is a central tool in the applied sciences for writing program. Its impact on the development of modern statistics is inevitable. Current research, especially for big data may not be done solely using R
We present and describe the GPFDA package for R. The package provides flexible functionalities for dealing with Gaussian process regression (GPR) models for functional data. Multivariate functional data, functional data with multidimensional inputs,
The R package quantreg.nonpar implements nonparametric quantile regression methods to estimate and make inference on partially linear quantile models. quantreg.nonpar obtains point estimates of the conditional quantile function and its derivatives ba