Electric vehicles (EVs) have gained increasing attention as a more sustainable and environmentally friendly mode of transportation. Many countries should include electrification of their transport networks in their future smart city plans to ensure environmentally sustainable growth. The number of EVs in metropolitan areas is expected to experience extensive growth. EVs are often considered an alternative to addressing the challenges posed by increasing carbon emissions and dependence on fossil fuels. However, the acceptance of EVs tends to be slow due to concerns such as range anxiety, prolonged charge times, inconvenient charging locations, and inadequate charging infrastructure. To address these challenges, this paper discusses the planning and optimization of EV charging infrastructure based on existing literature. In recent years, there has been a significant increase in the number of publications focusing on EV charging stations. This demonstrates the growing interest and research activity in the field of EV charging infrastructure. Furthermore, the literature is categorized into recurring topics, specifically EV charging planning and optimization for EV charging infrastructure. This particular review paper includes empirical work on charging network planning for EVs. Therefore, this analysis provides the latest trends and findings for EV charging infrastructure planning.

Shared autonomous vehicles (SAVs) are rapidly emerging as a viable alternative form of public transportation with the potential to provide adequate and user-friendly, on-demand services without having vehicle ownership. It has been argued that SAVs could revolutionize transportation systems and our current way of life. Although SAVs are likely to be introduced in developed countries first, there is little doubt that they would also have a significant effect and enormous market in developing nations. This study aimed to investigate the factors that influence public acceptance of SAVs, as well as the current public attitude toward SAVs, in two developing countries, namely, Pakistan and China. A stated preference survey was conducted to understand respondents’ travel patterns, preferences, and sociodemographic data. A total of 910 valid responses were gathered: 551 from Lahore, Pakistan, and 359 from Dalian, China. A multinomial logit model and a mixed multinomial logit model with panel effect were used for data analysis. The results suggested that generic attributes, such as respondents’ waiting time, travel time, and travel cost were found to be significant in both cities. The results indicate that sociodemographic characteristics, such as education, income, travel frequency in a week, and people who had driver’s licenses, are significantly correlated with respondents’ interest in using SAV in Lahore. The results also showed that people who had a private car indicated a greater interest in SAVs in Dalian. The study provides a new perspective to understand the public preferences toward SAVs in developing countries with different economies and cultures, as well as a benchmark for policymakers to make effective policies for the future implementation of SAVs.

Motorization has been escalating rapidly in developing countries, posing a severe challenge to sustainable urban mobility. In the past two decades, car-sharing has emerged as one of the most prominent alternatives to facilitate smart mobility solutions, thereby helping to reduce air pollution and traffic congestion. However, before its full-scale deployment, it is essential to understand the consumers’ acceptance of car-sharing systems. This study aimed to assess the public perception and acceptance of the car-sharing system through a stated preference (SP) questionnaire in the city of Lahore, Pakistan. The collected data contained detailed information on various service attributes of three alternative modes (car-sharing, private car, and taxi) in addition to the sociodemographic attributes of respondents. Data analysis and interpretation were performed using econometric models such as the Multinomial Logit Model (MNL), the Nested Logit Model (NL), and the Random Parameter Logit Model (RPL). Study findings revealed that several generic attributes such as travel time, travel cost, waiting time, and privacy were predicated as significant influential factors towards the adoption of car-sharing. Sociodemographic attributes, including age, education, monthly income, the individuals who had driver’s licenses, and frequency of travel in a week, were also found to be significant. The findings of the current study can provide valuable insights to stakeholders and transportation planners in formulating effective policies for car-sharing.

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.

Accessibility is the ease of reaching opportunities (goods, services, activities, and destinations). Accessibility of desirable locations such as households and commercial locations, is typically scaffolded by land use patterns and transportation infrastructure. It can reflect people’s travel convenience, cities’ viability, sustainability, and mitigate the negative effects on the environment and public safety. Consequently, it is recognized as a fundamental principle in urban sustainable development policies worldwide. In the literature, most of the studies have used a static or partially dynamic approach with a single mode such as a car or public transportation by using conventional models. These “static” models assume that household locations are static and that transportation supply and opportunities for social practice activities are fixed in time and space, which can lead to biased or even misleading assumptions in accessibility models. Therefore, the aim of this study is to evaluate the impact of dynamic spatial accessibility through Mode-Dependent Accessibility (MDA) on the location choice behaviors of urban activities such as households and commercialin the City of Wuhan, China. This study employed the Mode-Dependent Travel Demand Model (M-TDM) to measure the impact of short-term MDA on household and commercial activities for the years 2012 and 2015. Additionally, an integrated spatial economic (ISE) model such as PECAS (Production, Exchange, Consumption, Allocation, System) in order to investigate location preferences of urban activities over space and time. Regarding household and commercial location choice, the ISE modeling results revealed that households and commercial activities are sensitive to MDA, especially using transit. The ISE method predicted that the R² for household and commercial location choice models was 0.84 to 0.90 for transit-based accessibility, whereas the R² for logsum-based static models was 0.48 to 0.72. In addition, their findings suggest that highly accessible locations that are well served by auto are more appealing for household and commercial activities. The findings of this study will help urban planners, transportation planners, and policymakers take into account the dynamic nature of short-term MDA when zoning and allocating urban activities and public amenities, instead of using static accessibility.