Journal of Transportation Research

Journal of Transportation Research

Predicting the Impact of Shared Autonomous Vehicle Deployment on Daily Travel Mode Choice of Mobility-Impaired Groups

Document Type : Original Article

Authors
Mohammadamin Ebrahimzadeh 1
Shahriar Afandizadeh 2
Seyed Ebrahim Abdolmanafi 1
1 Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 Professor, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran.
10.22034/tri.2025.529835.3355
Abstract
This study investigates the impact of the deployment of shared autonomous vehicles on daily travel mode choice among mobility-impaired groups, including the elderly, children, and individuals with disabilities. Data were collected through field surveys and analyzed using logistic regression modeling. Model evaluation, based on indicators such as the area under the ROC curve (0.975) and the Cox & Snell R² (0.291), demonstrates high predictive accuracy and reliability. The findings reveal that variables such as education level, frequency of using shared autonomous vehicles, social acceptance, perceived safety, and accessibility significantly influence vehicle choice among mobility-impaired individuals. Results emphasize that the introduction of shared autonomous vehicles can substantially enhance equity and fairness in transportation access for vulnerable groups, increasing their participation in urban mobility systems. The main innovation of this study lies in its specific focus on mobility-impaired groups and the use of field data to model vehicle choice using logistic regression. This approach provides a practical tool for policymakers to design strategies aimed at improving quality of life and mobility access for these populations. Moreover, this research offers a solid foundation for future studies and paves the way for developing more advanced models to explore in greater depth the effects of emerging transportation technologies on mobility-impaired users.
Keywords
Shared Autonomous Vehicles
Mobility-Impaired Groups
Logistic Regression Model
Travel Mode Choice Prediction
Subjects
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