Multilayer networks are a type of complex network that consist of multiple layers, where each layer represents a different type of connection or interaction between the same set of nodes. These networks are used to model systems where entities are connected in multiple ways simultaneously, capturing the complexity of real-world relationships better than traditional single-layer networks. Through a particular interlayer structure, the dynamical evolution of a complex system over time can be represented. Computing the centrality of a temporal network can improve our understanding of how the most important nodes in a network change over time. Our focus is centered on the computation of the centralities of a multilayer temporal network whose modifications over time consist of low-rank updates of the edge adjacency matrix of a transport network. Using Krylov subspace methods for matrix function approximations, we will exploit the particular structure of the problem to gain some computational advantages and modeling insights.