The adoption of electric vehicles (EVs) have proven to be a crucial factor to decreasing the emission of greenhouse gases (GHG) into the atmosphere. However, there are various hurdles that impede people from purchasing EVs. For example, long charging time, short driving range, cost and insufficient charging infrastructures available, etc. This article reports the public perception of EV-adoption using statistical analyses and proposes some recommendations for improving EV-adoption in Qatar. User perspectives on EV-adoption barriers in Qatar were investigated based on survey questionnaires. The survey questionnaires were based on similar studies done in other regions of the world. The study attempted to look at different perspectives of the adoption of EV, when asked to a person who is aware of EVs (technical respondents—people studying/working at universities/research centers and policy makers) or a person who may or may not be aware of EVs (non-technical respondents—people working in banks, governments and private non-academic organizations, etc.). Cumulative survey responses from the two groups were compared and analyzed using two-sample t-test statistical analysis. Detailed analyses showed that—among various major hindrances—raising of public awareness of such greener modes of transportation, the availability of charging options in more places and policy incentives towards EVs would play a major role in EV-adoption. The authors provide recommendations that—along with government incentives—could help make a gradual shift to a greater number of EVs convenient for people of Qatar. The proposed systematic approach for such a study and analysis may help in streamlining research on policies, infrastructures and technologies for efficient penetration of EVs in Qatar.

Smart transportation cities are based on intelligent systems and data sharing while human drivers generally have limited capabilities and imperfect observations in traffics. The perception of Connected and Autonomous Vehicle (CAV) utilizes data sharing through Vehicle-To-Vehicle (V2V) and Vehicle-To-Infrastructure (V2I) communications to improve driving behaviors and reduce traffic delays and fuel consumption. This paper proposes a Double Agent (DA) intelligent traffic signal module based on the Reinforcement Learning (RL) method where the first agent named as Velocity Agent (VA) aims to minimize the fuel consumption by controlling the speed of platoons and single CAVs crossing a signalized intersection, while the second agent named as Signal Agent (SA) proceeds to efficiently reduce traffic delays through signal sequencing and phasing. Several simulation studies are conducted for a signalized intersection with different traffic flows and the performance of a single-agent with only the VA, DA with both VA and SA, and Intelligent Driver Model (IDM) are compared. It is shown that the proposed DA solution improves the average delay by 47.3% and the fuel efficiency by 13.6% compared to the Intelligent Driver Model (IDM).

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