查看更多>>摘要：Artificial intelligence (AI) has the potential to analyze mobility data and make mobility systems smarter by leveraging diverse data sources such as geospatial data, transportation logs, and real-time sensor data to optimize traffic flow, enhance public transportation systems, and support the development of autonomous vehicles. With the newly emerged generative AI paradigm, exemplified by large language models (LLMs), there is great potential to transform the current AI applications in mobility, transportation, and urban domains. This article provides an overview of recent efforts and aims to shed light on the challenges and future opportunities to facilitate the adaptation of LLMs for smarter mobility systems.
查看更多>>摘要：Agentic artificial intelligence (AI) represents a transformative leap in AI, evolving beyond reactive systems to autonomous, goal-oriented agents capable of learning, adapting, and making independent decisions. As it presents immense opportunities, its adoption is growing across industries. However, its rise introduces critical challenges. Responsible adoption requires robust governance, transparency, and a well-defined regulatory framework. This article explores agentic AI’s defining characteristics, real-world applications, and transformative potential, and examines its societal and business implications. To shape agentic AI as a trusted, transformative force for responsible innovation and meaningful progress, we propose research directions and offer stakeholder recommendations.
NETL
NSTL
IEEE