CITY TRANSPORT ANALYZER: A POWERFUL QGIS PLUGIN FOR PUBLIC TRANSPORT ACCESSIBILITY AND INTERMODALITY ANALYSIS
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License | CC Attribution 3.0 Unported: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor. | |
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00:00
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Transcript: English(auto-generated)
00:01
Hello, here is Carlo Andrei Viraghi from IMM Design Lab of Politecnico di Milano, and I'm here to present you the paper, City Transport Analyzer, a powerful QGIS plugin for public transport accessibility and intermodality analysis, written with Gianfran Conaro and Demilia Lindsay. The purpose of this research is to develop a tool for analyzing areas from the perspective of public transportation.
00:23
This will help to show how an area is connected with others via public transportation, and also how it is served, not just in terms of the presence of stops, but also classifying the roads based on their potential usage for moving from one mode to another.
00:42
The tool first allows to gather and organize the data with the possibility of combining input provided by the user and available open dataset. Then it passed to build the graph structure for running the analysis, thanks to the use of the GTFS format.
01:01
And finally, it allows to run intermodality and accessibility analysis on public transportation networks. This plugin has been tested on two different case studies, the city of Rio de Janeiro in Brazil and the city of Milan in Italy. Here you can see the consistency of the public transportation and pedestrian graph.
01:21
With the word intermodality, we intend to study the diversity and quantity of public transportation options in an area and to evaluate the potential of streets being used specifically for model change. We initially identify some target stops in an area and we connect them only with those within a given distance,
01:41
not sharing any transportation line with the source one. Finally, we look at the street network to understand how people are supposed to move from one stop to another, developing the faster walkable connection between non-connected stops. For the city of Rio, the analysis was performed on the Ciudad de Nova Bayro.
02:04
The source stops are identified by the presence of a yellow circle around them, and the size of both source and target stops is proportional to the number of lines passing through them. Target stops have been identified by drawing a circular buffer or nearly one kilometer starting from the source stops.
02:23
Darker lines correspond to the street segments with the higher probability of being taken in multimodal trips, passing so from one stop to another that are not connected via public transportation using the shortest walkable path.
02:41
For Milan, the central area of Piazza Duomo was taken as a case study. We can notice that with respect to Rio, we have much more stops with much less line per stop. It's interesting to notice that the number of shortest paths count is overall similar between the two case studies.
03:03
Here, more than in Rio, it is possible to observe the emergence of some areas that we could define as intermodal areas of diffuse public transportation accessibility nodes with the high concentration of darker segments.
03:22
Accessibility analysis instead wish to show how an area is connected with the rest of the city via public transportation, drawing isochrones, combining walking and all the existing public transportation lines. For both cases, the analysis was tested on two time threshold, 15 and 30 minutes.
03:42
In Rio, one single point was used as an input but testing two different GTFS, one with the sole buses and BRT modes, the other enriched with metro, BLT and train. For Milan, one point for each of the munichipi was used as a starting point.
04:06
The results clearly show how the overall presence of public transportation and also the precise location of the points can affect significantly the results. This tool also supports the analysis of GTFS data within the QGIS environment
04:25
using Python libraries as NetworkX and OSM-NX. In the future, we wish to improve the overall performances of the plugin, trying to generalize some function to different kind of source points and also implementing some features for generating GTFS from other layers.
04:46
Many thanks for your attention. I hope you will find this tool useful for your work.