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CITY TRANSPORT ANALYZER: A POWERFUL QGIS PLUGIN FOR PUBLIC TRANSPORT ACCESSIBILITY AND INTERMODALITY ANALYSIS

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CITY TRANSPORT ANALYZER: A POWERFUL QGIS PLUGIN FOR PUBLIC TRANSPORT ACCESSIBILITY AND INTERMODALITY ANALYSIS
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Mobility is one of the main factors affecting urban environmental performances. Car dependency is still widespread worldwide and integrated planning approaches are needed to exploit the potential of active and shared mobility solutions, making them an effective alternative to the use of private vehicles. The analysis and optimization of public transportation (PT) services have so become increasingly important in the planning and management of urban infrastructure. This work aims to develop and implement a QGIS plug-in for analyzing urban PT networks, assessing the accessibility and intermodality dimensions, relying on General Transit Feed Specification (GTFS) data as source of information. GTFS is a standardized format for PT schedules and geographic information. It defines a common format for transit agencies to share their data, making it possible for developers to create applications that provide accurate and up-to-date information about services. This standard was chosen because it is one of the most popular and widely used, especially when the data are used for static type analysis. The information extracted mainly concerns PT stops, routes and nodes preparatory to route construction and connection. All data belonging to the geospatial standard, in order to be usable by GIS software, must be extracted, interpreted and converted to a GIS layer. Specifically, all information regarding stops and routes was extracted to obtain a vector layer for each type of data. Going deeper, one of the most important layers concerns that of the PT routes, as it shows the entire urban network, obtained by converting the data within a graph data structure using NetworkX, a library for the creation, management and manipulation of complex networks, including graphs. This graph was created following a personal interpretation with the aim of facilitating the achievement of our purpose. to facilitate the achievement of our purpose, it was decided to model the edges of the graph in such a way that an edge is only used by one PT route. If two public vehicles use the same edge, there will be two different overlapping edges. It is also important to emphasise that each edge in the graph shows the type of means of transport using it (underground, train, bus, ...), the average travel time of that edge, and the length of the edge itself. The creation of the graph is fundamental to carry out two types of analysis. The accessibility analysis is conducted to determine which areas are reachable within the specified time frames via all the possible combinations of PT lines. Starting from any point in the city, it provides service areas combining PT and walking within a given time interval defined by the user up to a maximum of 60 minutes. The outputs are both lines, all the edges of the network that can be travelled, and polygons, convex hulls built on them. This analysis, already available only within proprietary software ArcGIS, is extremely useful to provide very detailed information about the potential of each PT stop and its surrounding urban area. The second analysis concerns PT interoperability and introduces some elements of novelty. It is intended to assess intermodality beyond the PT nodes (hubs), exploring which paths in the street network have the higher probability of being taken to change from one line/mode to another. The evaluation is purely physical and only considers network distance. Its results are expected to be integrated with complementary dimensions as proximity to Point of Interests, street comfort and safety for a holistic planning approach. Starting from any PT stop, a circular catchment area is drawn using a user-defined distance and the PT stops within it are selected. Among them, those with at least one PT line in common with the departure stop are discarded, the remainder being selected. This is done assuming that PT is generally faster than walking and so, when the PT alternative is available, walking is less attractive. It is then shown how the starting stop is connected to the other stops via the most direct pedestrian path. Finally, once drawn all the pedestrian paths, the number of times that each street segment is used is also calculated, providing a classification according to their potential use for modal change. The pedestrian graph is obtained through OSMnx, a library for retrieving, processing, and visualizing road network data from OpenStreetMap. The plugin was tested on two different case studies, Milan and Rio de Janeiro, producing significant results highlighting the created plug-in's utility and application in the context of GTFS data-driven studies of urban public transportation networks. The outcomes of both analyses were consistent, demonstrating the plugin's applicability in comprehending the dynamics of ...
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Transcript: English(auto-generated)
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.
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.
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.
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.
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,
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.
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.
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.
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.
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.
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.
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.
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
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.
Many thanks for your attention. I hope you will find this tool useful for your work.