Electric mobility is an emerging market around the world. Electric vehicles have remarkable environmental gains compared to conventional vehicles, they contribute to decreasing fossil energy dependence, but they need huge investments in charging infra-structure and their market price is much above regular ones. The conditions for market penetration are generally critical in emerging economies, where the purchasing power is far below that of developed economies. In addition, further technical and regulatory barriers hinder the market uptake and augmentation. This paper aims to discover and analyze the current market conditions of electric vehicles in selected emerging economies and to set up augmentation pathways, based on experiences in developed and in developing countries.

The congestion charging scheme is regarded as a successful measure to reduce traffic-related problems. However, low general acceptability of the public for implementing such a scheme is a barrier against its success. In this research, an online pre-designed survey was conducted in five capitals (Budapest, Tunis, Amman, Ulaanbaatar, and Damascus) to define the factors that affect congestion charging acceptability the most. The results of relationships between the studied factors like travel behavior and acceptance of the congestion charging scheme show an irregular pattern in each city. It indicates that the identity of each city and its general policy implications determine which factors significantly affect the public acceptability of congestion charging scheme. In Amman and Budapest, most of the predictors have no statistical effect on the schemes’ public acceptability. Consistent with previous researches, on the other hand, the results demonstrate that the schemes’ effectiveness is crucial and affects the acceptability significantly in all cities. At the same time, it shows that the “prior scheme knowledge” factor has a significant direct effect on the acceptability level in three cities (Damascus, Tunis, and Ulaanbaatar).

The introduction of autonomous vehicles (AVs) and shared autonomous vehicles (SAVs) is projected to enhance network performance and accessibility. The future share distribution of AV and SAV is not yet apparent, nor is which of these two future transport modes will become dominant. Therefore, this research deploys a simulation-based dynamic traffic assignment using Visum software to investigate the impact of varying the share distribution of AVs and SAVs on Budapest’s network performance and consumer surplus in three projected future traffic scenarios for the years 2030 and 2050 compared to the Base scenario for 2020. The three future scenarios are presented and characterized by different penetration rates of AVs and SAVs to reflect the uncertainty in the market share of these future cars as follows: Mix-Traffic scenario for 2030, and AV-Focused and SAV-Focused scenarios for 2050. The results revealed that the emergence of AVs and SAVs would improve the overall network performance, and better performance was observed with increasing the share distribution of SAVs. Similarly, the consumer surplus increased in all future scenarios, especially with increasing the share distribution of AVs. Consequently, the advent of AVs and SAVs will improve traffic performance and increase consumer surplus, benefiting road users and authorities.

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The accelerating emergence of vehicle automation and the anticipation of the advent of shared mobility through fully autonomous vehicles indicate the beginning of a new era of mobility which has the potential to reshape the future of transport in urban areas. In light of such developments, it is important that communities prepare to adapt to the changes they might entail. Therefore, in this paper, traffic flow theory, simulation-based dynamic traffic assignment, and a computer experiment using PTV Visum software were employed to study the impact of different market penetration rates of shared autonomous vehicles (SAVs) on a city-size traffic system. The city of Budapest during morning peak period was chosen as a case study, and a simulation model was created by incorporating SAV elements and their interrelationships into the existing traffic model of the case study city; three alternative future penetration rates were examined in relation to five key performance indicators (KPIs). The simulation results indicated that the implementation of the SAV system has a positive effect on traffic performance. Based on the relationships between the modeled SAV demand shares and the network’s KPIs in the designed scenarios, the overall network performance showed improvement along with an increase in the SAV demand share.