N
Nima Golshani
Researcher at University of Illinois at Chicago
Publications - 33
Citations - 679
Nima Golshani is an academic researcher from University of Illinois at Chicago. The author has contributed to research in topics: Travel behavior & Copula (probability theory). The author has an hindex of 11, co-authored 33 publications receiving 459 citations. Previous affiliations of Nima Golshani include State University of New York System & Georgia Institute of Technology.
Papers
More filters
Journal ArticleDOI
Eliciting preferences for adoption of fully automated vehicles using best-worst analysis
TL;DR: In this paper, a new approach for modeling the adoption behavior of fully autonomous vehicles using the profile-case best-worst scaling model is presented, where an AV profile which is characterized in terms of the main vehicle attributes and their associated levels is presented to the decision maker and he/she is asked to select the most and the least attractive attributes.
Journal ArticleDOI
Grouped random parameters bivariate probit analysis of perceived and observed aggressive driving behavior: A driving simulation study
TL;DR: In this paper, the authors used driving simulation data and surveys conducted in 2014 and 2015 in Buffalo, NY, to study the factors that affect perceived (self-reported, based on surveys) and observed (as measured based on driving simulation experiments) aggressive driving behavior.
Journal ArticleDOI
Analysis of telecommuting behavior and impacts on travel demand and the environment
TL;DR: In this paper, the authors developed an integrated framework to provide the empirical evidence of the potential impacts of home-based telecommuting on travel behavior, network congestion, and air quality.
Journal ArticleDOI
Modeling travel mode and timing decisions: Comparison of artificial neural networks and copula-based joint model
TL;DR: Comparisons of discrete, continuous, and joint discrete-continuous statistical models with the performance of the neural networks indicate that beside superior prediction accuracy, the NN is capable of capturing nonlinearities in travel demand, which suggests that it can also be more accurate to capture asymmetrical and non-linear responses for policy analysis purposes.
Journal ArticleDOI
Wrong-way driving crashes: A random-parameters ordered probit analysis of injury severity
TL;DR: A random-parameters ordered probit model is used to determine the features that best describe wrong-way driving crashes and to evaluate the severity of injuries in WWD crashes, and factors such as driver age, driver condition, roadway surface conditions, and lighting conditions significantly contribute to the injury severity of WWD crashed.