The use of real-time data in logistics is an important topic. Every day, logistics produces a large quantity of data, which is mainly produced by monitoring and controlling the enormous flow of goods. The last-mile delivery market is expanding at a rapid pace through large-and small-scale consumer platforms, but the economic drivers to create more sustainable systems are weak. Therefore, cities are facing the potential downside of this "Uberisation" of logistics. Urban and city planners, city administrators, and business stakeholders need a new adaptive approach such as the usage of digital twinning solutions of urban logistics. This to help in the interpretation of the dynamics of logistics networks in the city and the consequences of the introduction of particular innovations. The challenge is to predict the most likely developments for the coming years and propose feasible policies on that basis. This research work aims to advance research in the field of digital twins applied to city logistics, by proposing a framework enabling new applications for designing and assessing targeted urban logistics policies and to develop a range of logistics solutions for shared, connected, and low-emission logistics operations, empowered by an adaptive modeling approach.