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. Two data sets about passenger flows (both entering and leaving stations) are used to calculate the flows on links with a resolution of 1 minute. 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 minute up to 30 minutes, index changes become progressively smoother suggesting that for certain applications (e.g., those concerning security), aggregating data could lead to misleading conclusions.
A dynamic evaluation of an underground transportation system using image processing and centrality index computation
L. Mussone;V. J. Aranda Salgado;R. Notari
2023-01-01
Abstract
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. Two data sets about passenger flows (both entering and leaving stations) are used to calculate the flows on links with a resolution of 1 minute. 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 minute up to 30 minutes, index changes become progressively smoother suggesting that for certain applications (e.g., those concerning security), aggregating data could lead to misleading conclusions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
