Public Perception of the Introduction of Autonomous Vehicles

Aldakkhelallah, Abdulaziz; Alamri, Abdulrahman S.; Georgiou, Stelios; Simic, Milan

doi:10.3390/wevj14120345

Cited by 2 publications

(2 citation statements)

References 35 publications

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“…Extending the application of multi-agent reinforcement learning to optimize shared mobility systems, such as ride-sharing or electric vehicle fleet management, presents an intriguing research avenue. This extension can help reduce congestion, improve resource utilization, and enhance the overall efficiency of transportation systems [73][74][75][76][77][78].…”

Section: Discussionmentioning

confidence: 99%

Optimizing Electric Vehicle Charging Recommendation in Smart Cities: A Multi-Agent Reinforcement Learning Approach

Suanpang,

Jamjuntr

2024

WEVJ

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As global awareness for preserving natural energy sustainability rises, electric vehicles (EVs) are increasingly becoming a preferred choice for transportation because of their ability to emit zero emissions, conserve energy, and reduce pollution, especially in smart cities with sustainable development. Nonetheless, the lack of adequate EV charging infrastructure remains a significant problem that has resulted in varying charging demands at different locations and times, particularly in developing countries. As a consequence, this inadequacy has posed a challenge for EV drivers, particularly those in smart cities, as they face difficulty in locating suitable charging stations. Nevertheless, the recent development of deep reinforcement learning is a promising technology that has the potential to improve the charging experience in several ways over the long term. This paper proposes a novel approach for recommending EV charging stations using multi-agent reinforcement learning (MARL) algorithms by comparing several popular algorithms, including the deep deterministic policy gradient, deep Q-network, multi-agent DDPG (MADDPG), Real, and Random, in optimizing the placement and allocation of the EV charging stations. The results demonstrated that MADDPG outperformed other algorithms in terms of the Mean Charge Waiting Time, CFT, and Total Saving Fee, thus indicating its superiority in addressing the EV charging station problem in a multi-agent setting. The collaborative and communicative nature of the MADDPG algorithm played a key role in achieving these results. Hence, this approach could provide a better user experience, increase the adoption of EVs, and be extended to other transportation-related problems. Overall, this study highlighted the potential of MARL as a powerful approach for solving complex optimization problems in transportation and beyond. This would also contribute to the development of more efficient and sustainable transportation systems in smart cities for sustainable development.

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Section: Discussionmentioning

confidence: 99%

Optimizing Electric Vehicle Charging Recommendation in Smart Cities: A Multi-Agent Reinforcement Learning Approach

Suanpang,

Jamjuntr

2024

WEVJ

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“…Furthermore, the scope of the research is restricted to the features and specifications that were monitored. The research by Aldakkhelallah [24] used logistic regression modeling to investigate the Saudi public's sentiments toward AVs. This study utilizes logistic regression analysis to examine public perceptions of AVs in Saudi Arabia.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Vehicles Perception, Acceptance, and Future Prospects in the GCC: An Analysis Using the UTAUT-Based Model

Hafeez,

Mas’ud,

Al-Shammari

et al. 2024

WEVJ

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The emergence of Autonomous Vehicles (AVs) marks a significant advancement in the automotive industry, transitioning from driver-assistance technologies to fully autonomous systems. This change is particularly impactful in the Gulf Cooperation Council (GCC) region, which is a significant automotive market and technological hub. However, the adoption of AVs in the GCC faces unique challenges due to the influence of cultural norms and geographical characteristics. Our research utilizes a customized framework of the Unified Theory of Acceptance and Use of Technology (UTAUT), which is adapted to include cultural and geographical factors. This approach fills a gap in the existing literature by identifying and analyzing the key factors affecting the adoption of AVs in the GCC. Our findings indicate a difference in the receptiveness towards AVs among different demographics. Younger participants displayed a more favorable attitude towards AVs compared to older individuals. Additionally, gender and educational attainment play significant roles in the acceptance of AVs. Specifically, our results suggest that there are variations in acceptance rates among genders and individuals with varying levels of education. The United Arab Emirates (UAE) has a relatively high acceptance rate of AVs due to its advanced infrastructure and openness to technological innovations. Our study identifies facilitating conditions and performance expectancy as crucial determinants of intention to use AVs in the GCC. It emphasizes the importance of infrastructure readiness and the perceived advantages of AVs in promoting their adoption.

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Public Perception of the Introduction of Autonomous Vehicles

Cited by 2 publications

References 35 publications

Optimizing Electric Vehicle Charging Recommendation in Smart Cities: A Multi-Agent Reinforcement Learning Approach

Optimizing Electric Vehicle Charging Recommendation in Smart Cities: A Multi-Agent Reinforcement Learning Approach

Autonomous Vehicles Perception, Acceptance, and Future Prospects in the GCC: An Analysis Using the UTAUT-Based Model

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