Electric vehicles (EVs) are rapidly becoming a sustainable and viable mode of future transportation due to their multitude of advantages, such as reduced CO₂ emissions, local air pollutants, and vehicular noise. This study aims to identify and analyze the scientific literature using bibliometric analysis to determine the main topics of authors, their sources, and the most-cited articles, countries, journals, and institutes in the literature on EVs. This bibliometric analysis included scientific work that was published from 2011 to 2022 to provide a thorough analysis of EVs, which will assist researchers and policymakers in understanding the most current global EV advancements. This analysis extracted all bibliometric information about EVs from the Scopus database, collecting 17,150 articles published between 2011 and 2022. The data were sorted for analysis by publication year, document type, author, institute, country, cited author, keyword, and keyword co-occurrence of the EVs. The VOSviewer software was employed to examine the sorted data due to its excellent analysis and visualization capabilities. We used VOSviewer to graphically represent the density, co-occurrence, trends, and linkage of the aforementioned data comprehensibly. The publishing patterns of EVs indicate that the research field is evolving, with a yearly increase in the number of publications. The analysis showed that China, the United States, and the United Kingdom are leading in EV research and large-scale applications. Furthermore, China is the leading country in terms of research institutions and authors involved in EVs. The journal Energies is the most prominent publication periodical. Keyword analysis revealed that during the past decade, EV research has concentrated on battery-management systems, energy storage, charging infrastructure, environmental concerns, etc. The bibliometric study offered pertinent details on the main themes explored concerning EVs and current technological developments.

Transportation demand management is a successful complement to urban infrastructure. The emergence of shared mobility strategies such as car sharing offers sustainable mobility in urban areas. Car sharing has launched in different cities worldwide to mitigate severe transportation problems such as traffic congestion, air pollution, and traffic safety. Therefore, this study aims to investigate the intentions and preferences of travelers toward car sharing services in Djibouti, Africa. The data was collected through an online stated preference (SP) survey. The SP survey included the awareness of car sharing services, attributes related to transport modes, and demographic characteristics. A total of 600 respondents were received. In this study, we employed the multinomial logit (MNL) model to travel mode choice modeling and compared the results with the AdaBoost algorithm. The MNL model results showed that generic attributes such as travel time, travel cost, maintenance charges, and membership fees were found significant. In addition, several demographic characteristics like gender, education, and income were also found significant. The modeling and prediction performances of the MNL model and AdaBoost algorithm were compared using multi-class predictive errors. According to the goodness-of-fit results, the AdaBoost algorithm achieved overall higher prediction accuracy than the MNL model. This study could be helpful to transport planners and policymakers for the implementation of car-sharing services in urban areas.