Urban traffic attributed to commercial and industrial transportation is observed to largely affect living standards in cities due to external effects pertaining to pollution and congestion. In order to counter this, smart cities deploy technological tools to achieve sustainability. Such tools include Digital Twins (DT)s which are virtual replicas of real-life physical systems. Research suggests that DTs can be very beneficial in how they control a physical system by constantly optimizing its performance. The concept has been extensively studied in other technology-driven industries like manufacturing. However, little work has been done with regards to their application in urban logistics. In this paper, we seek to provide a framework by which DTs could be easily adapted to urban logistics networks. To do this, we provide a characterization of key factors in urban logistics for dynamic decision-making. We also survey previous research on DT applications in urban logistics as we found that a holistic overview is lacking. Using this knowledge in combination with the characterization, we produce a conceptual model that describes the ontology, learning capabilities and optimization prowess of an urban logistics digital twin through its quantitative models. We finish off with a discussion on potential research benefits and limitations based on previous research and our practical experience.
翻译:据认为,由于污染和拥挤的外部影响,商业和工业运输导致的城市交通在很大程度上影响了城市的生活水平。为了应对这种情况,智能城市利用技术工具来实现可持续性。这些工具包括数字双体(DT),这些是实际物理系统的虚拟复制品。研究表明,DT通过不断优化其性能,对控制物理系统非常有益。这一概念在制造业等其他技术驱动的行业中已经进行了广泛研究。然而,在城市物流应用方面没有做多少工作。在本文件中,我们力求提供一个框架,使DT易于适应城市物流网络。为此,我们提供了城市物流关键因素的特点,以便进行动态决策。我们还调查了以前关于DT在城市物流中应用的研究,因为我们发现缺乏全面的概览。我们利用这一知识与特征的结合,产生了一个概念模型,说明城市物流数字双胞胎的本学、学习能力和优化能力。我们最后是在以前的研究和实际经验的基础上讨论潜在的研究效益和限制。