Planning of urban public transportation networks in a smart city

Jonathan Frez, Nelson Baloian, Jose A. Pino, Gustavo Zurita, FRANCO FABIAN BASSO SOTZ

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Planning efficient public transport is a key issue in modern cities. When planning a route for a bus or a line for a tram or subway, it is necessary to consider people's demand for this service. In this work we present a method to use existing crowdsourced data (like Waze and OpenStreetMap) and cloud services (like Google Maps) to support a transportation network decision making process. The method is based on the Dempster-Shafer Theory to model transportation demand. It uses data from Waze to provide a congestion probability and data from OpenStreetMap to provide information about location of facilities such as shops, in order to predict where people may need to start or end their trips using public transportation vehicles. The paper also presents an example using this method with real data. The example shows an analysis of the current availability of public transportation stops in order to discover its weak points.

Original languageEnglish
Pages (from-to)946-966
Number of pages21
JournalJournal of Universal Computer Science
Volume25
Issue number8
StatePublished - 2019
Externally publishedYes

Keywords

  • Dempster-Shafer theory
  • Origin-Destination planning problem
  • Smart cities
  • Transportation networks

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