Citation

Tamakloe R, Park D. Transp. Policy 2023; 139: 1-20.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.tranpol.2023.06.001

PMID

unavailable

Abstract

Autonomous Vehicle (AV) technology is a disruptive transportation technology that promises to revolutionize how people travel. Due to their potential mobility benefits and associated impacts on public health and the environment, they have received massive attention from transportation experts. To date, there have been numerous academic research conducted regarding AVs, and many attempts have been made to summarize these research documents by manually reviewing them. Nevertheless, due to the vast nature of the existing literature, it is challenging to synthesize the themes in AV-related research and to explore their trends to inform future research direction. This study aims to utilize an advanced natural language processing technique to provide a comprehensive overview of the recent developments in AV-related research. The abstracts of 3292 articles published in transportation-based journals from January 2016 to October 2022 were collected and used for this study. A Structural Topic Model (STM) was employed to explore the dominant research themes hidden in the extant literature on AV research, examine their evolution over time, and determine the topics highly associated with developing and developed economies. Overall, the study highlighted that the least common themes in the literature are in the areas of the development of complex vehicle designs, safety, environmental benefits, and the impact of AVs on transportation infrastructure. Nevertheless, themes concerning AV safety and the environment are emerging as hot topics since late 2021. Topics related to investigating user perceptions, policies/approaches to increase user demand, and AV control/stability are highly studied. Besides, they predominantly originated from developed countries. The research themes, their associated trends, and the key policy suggestions drawn from the study are potentially useful for making well-informed research funding decisions, setting research priorities for countries, and guiding the future research endeavours of researchers.

Language: en

Keywords

Autonomous vehicle; Environment; Machine learning; Structural topic model; Transport policy; Transportation