A dynamic evaluation of an underground transportation system using image processing and centrality index computation

The study of transportation networks covers a wide range of topics related to managing services and maintaining structures. In order to represent the topological structure of such a network, we associated a graph to a network. Then some different properties or attributes of the network can be managed as weights to be applied to the graph itself. In addition, different centrality indices can be computed in order to identify nodes or edges that are more important or more exposed to capacity changes (such as, for example, disruptive events). The purpose of this research is to provide a methodology for dynamically evaluating the significance of stations in a transportation network. This is accomplished through two distinct phases of activity. Firstly, we propose a dynamic analysis of the underground transportation system of the city of Milan, Italy. This transportation system is the mobility backbone of the city and counts four lines with 110 links and 107 stations, including 21 junctions (the transfer stations that connect the lines). Two data sets about passenger flows (both entering and leaving stations) are used to calculate the flows on links with a resolution of 1 min. The data sets refer to a week in 2018, without the changes in demand due to the pandemic scenario. Data are processed through an ad hoc written assignment procedure. Secondly, a centrality index is calculated by using passenger flows (entering, exiting, and on-segments) as weights of the underground graph. After maps reporting the outcomes of those calculations are drawn, an image comparison is carried out by using image processing tools and different aggregation intervals, in order to investigate how the importance or exposure of each station changes over time. The findings demonstrate that over time, indices vary by station, with junction stations naturally having the highest values. By increasing the observation interval from 1 min up to 30 min, index changes become progressively smoother, like an application of a low pass filter, suggesting that for certain applications (e.g. those concerning security), aggregating data could lead to misleading conclusions.