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Connectivity Forecast and Performance of Urban Road Networks Using Computational Analysis

EasyChair Preprint no. 10775

14 pagesDate: August 25, 2023


Emerging computational techniques allow precision measurements of the performance of urban road networks. These measurements help analyze the levels of connectivity and intermediation of growing urban networks. Consequently, in several experiments carried out, we have managed to detect a pattern of avenues that are highly used by drivers and that are scarce in each city. Therefore, these avenues are primary candidates for having a large influx of automobiles that congest traffic. The common factor of this inconvenience is that the cities are not planned in the long term with wide avenues to meet the growth of the cities. Where, the few existing avenues built randomly and do not support the density. Also, there is the concentration of commercial, educational and other establishments, clustered in the central area of the city. This leads to the concentration of human mobility and motorized vehicles in these areas. Also, the creation of urban or rural neighborhoods that do not meet urban standards, accessibility and basic development services has been observed. Here we present techniques to identify the routes with the greatest connectivity and compare trip averages on various major routes in the city. This with the objective of proposing strategies and public policies to: reduce the flow of vehicles, reduce interruptions, optimal maintenance of these roads and recommendation to build new roads in the convenient place.

Keyphrases: Accessibility, Congestion, Mobility, networks, planning, urban

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Guillermo Rodríguez López},
  title = {Connectivity Forecast and Performance of Urban Road Networks Using Computational Analysis},
  howpublished = {EasyChair Preprint no. 10775},

  year = {EasyChair, 2023}}
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