Despite the technological advancements in the transportation sector, the industry continues to grapple with increasing energy consumption and vehicular emissions, which intensify environmental degradation and climate change. The inefficient management of traffic flow, the underutilization of transport network interconnectivity, and the limited implementation of artificial intelligence (AI)-driven predictive models pose significant challenges to achieving energy efficiency and emission reduction. Thus, there is a timely and critical need for an integrated, sophisticated approach that leverages intelligent transportation systems (ITSs) and AI for energy conservation and emission reduction. In this paper, we explore the role of ITSs and AI in future enhanced energy and emission reduction (EER). More specifically, we discuss the impact of sensors at different levels of ITS on improving EER. We also investigate the potential networking connections in ITSs and provide an illustration of how they improve EER. Finally, we discuss potential AI services for improved EER in the future. The findings discussed in this paper will contribute to the ongoing discussion about the vital role of ITSs and AI applications in addressing the challenges associated with achieving energy savings and emission reductions in the transportation sector. Additionally, it will provide insights for policymakers and industry professionals to enable them to develop policies and implementation plans for the integration of ITSs and AI technologies in the transportation sector.

翻译：尽管交通运输领域取得了技术进步，但该行业仍面临能耗与车辆排放持续增长的挑战，这加剧了环境退化与气候变化问题。交通流管理效率低下、运输网络互联互通性未充分利用，以及人工智能（AI）驱动预测模型应用有限，已成为实现能效提升与减排目标的主要障碍。因此，亟需一种融合智能交通系统（ITSs）与AI的综合化先进方案，以推进节能减排出成效。本文探讨了ITSs与AI在未来强化型能效与减排（EER）中的关键作用。具体而言，我们分析了不同层级ITS传感器对改善EER的影响机制，研究了ITS系统间潜在的网络连接模式及其优化EER的路径，并展望了未来面向EER提升的AI服务发展方向。本文的研究成果将丰富关于ITSs与AI技术在交通运输领域节能减排出挑战中重要作用的学术讨论，同时为政策制定者与行业从业者提供将ITSs与AI技术整合至交通系统的政策制定与实施规划参考依据。