Complex networks provide a framework for modelling real-world systems. Based on a set of data on mobility by car between different urban areas of the city of Rome, we represent and analyze these mobility data coupled with urban public transport networks, augmenting the network nodes with data on commercial, economic, service and tourist activity in the city. In order to unravel the complex interdependencies of all these data, we propose a multiplex network consisting of four layers in which the nodes are defined by an urban grid subdividing the city into $1\times 1$ km cells. Network centrality measures are then used to determine the most influential nodes or prominent areas of the city. In particular, we propose an adaptation of the APA centrality algorithm for multiplex networks. This adaptation of the algorithm for multiplex networks offers the possibility to assign the importance given to node data relative to the network topology in each layer when computing the centrality. This allows a wider control in studying the mobility network, particularly generating different centrality maps according to the choice of this control parameter in each layer. We carry out experiments and present the results of a study of the network centralities considering different choices of the parameter.

复杂的网络提供了用于对现实世界系统进行建模的框架。基于罗马市不同市区之间的汽车出行数据，我们对这些出行数据与城市公共交通网络相结合进行了表示和分析，并利用该市的商业，经济，服务和旅游活动数据扩充了网络节点。城市。为了揭示所有这些数据的复杂相互依存关系，我们提出了一个由四层组成的多路复用网络，其中节点由城市网格划分，并细分为城市$\mathrm{1个}\times \mathrm{1个}$公里细胞。然后使用网络集中度度量来确定城市中最具影响力的节点或突出区域。尤其是，我们提出了APA中心性算法适用于多路复用网络的建议。复用网络算法的这种适应性提供了以下可能性：在计算中心度时，可以相对于每一层中的网络拓扑来分配节点数据的重要性。这允许在研究移动网络时进行更广泛的控制，尤其是根据每个层中该控制参数的选择生成不同的中心图。我们进行实验并提出考虑参数不同选择的网络中心性研究结果。