A
Abolfazl Mohammadian
Researcher at University of Illinois at Chicago
Publications - 213
Citations - 4347
Abolfazl Mohammadian is an academic researcher from University of Illinois at Chicago. The author has contributed to research in topics: Travel behavior & Mode choice. The author has an hindex of 30, co-authored 191 publications receiving 3084 citations. Previous affiliations of Abolfazl Mohammadian include Sharif University of Technology & University of Toronto.
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How is COVID-19 reshaping activity-travel behavior? Evidence from a comprehensive survey in Chicago.
TL;DR: In this paper, the authors investigate how and to what extent people's mobility-styles and habitual travel behaviors have changed during the COVID-19 pandemic and explore whether these changes will persist afterward or will bounce back to the pre-pandemic situation.
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Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis.
TL;DR: EXtreme Gradient Boosting (XGBoost) can detect accidents robustly with an accuracy, detection rate, and a false alarm rate of 99 %, 79 %, and 0.16 %, respectively.
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Shared versus private mobility: Modeling public interest in autonomous vehicles accounting for latent attitudes
TL;DR: This paper aims to jointly model public interest in private AVs and multiple SAV configurations in daily and commute travels with explicit treatment of the correlations across the (S)AV types with multivariate ordered outcome models with latent variables.
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An Automated GPS-Based Prompted Recall Survey with Learning Algorithms
TL;DR: A new household activity survey is presented which uses automated data reduction methods to determine activity and travel locations based on a series of heuristics developed from land-use data and travel characteristics.
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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.