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：由于商业和工业运输带来的城市交通，因其引发的污染和拥堵等外部效应，被观察到在很大程度上影响城市的生活水平。为应对这一问题，智慧城市通过部署技术工具来实现可持续性。这类工具包括数字孪生（DT），它是现实物理系统的虚拟副本。研究表明，数字孪生通过持续优化物理系统的性能，对其控制非常有益。该概念已在制造业等其他技术驱动型行业中得到广泛研究。然而，关于其在城市物流中的应用研究却很少。在本文中，我们试图提供一个框架，使数字孪生能够轻松适配城市物流网络。为此，我们提出了城市物流中用于动态决策的关键因素的特征描述。我们还调查了先前关于数字孪生在城市物流中应用的研究，因为发现缺乏一个全面的概述。结合这一知识与特征描述，我们构建了一个概念模型，通过其定量模型描述了城市物流数字孪生的本体论、学习能力和优化能力。最后，我们基于先前研究和自身实践经验，讨论了潜在的研究益处与局限性。