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An introduction to urban mobility: Data, visualization, artificial intelligent approaches, and its foundations

  • Hugo Alatrista-Salas
  • , Erick Cuenca
  • , Rigoberto Fonseca-Delgado
  • , Saba Infante
  • , Raúl Manzanilla
  • , Diego Morales-Navarrete
  • , Aracelis Hernandez
  • , Miguel Nunez-Del-prado
  • , Israel Pineda
  • , Pascal Poncelet
  • , Arnaud Sallaberry
  • Universidad Yachay Tech
  • Universidad de Carabobo
  • Universidad Andina del Cusco
  • Universidad San Francisco de Quito
  • Institut de Biologie Computationnelle

Research output: Contribution to journalArticlepeer-review

Abstract

In the last years, the scientific community has increasingly studied urban mobility since around 55% of the world population live in urban areas. Thus, individuals living in urban areas have to deal with phenomena like traffic jams, commute time, pollution, among others, which are difficult to understand and solve. Therefore, new innovative approaches such as mobility models, artificial intelligence, or visualization applied to urban mobility analysis problems shed new light on understanding cities’ behavior. In this work, we survey the current state of the mathematical and computational tools we have at our disposal to better understand the current situation of urban areas. Our work presents datasets, discusses relevant artificial intelligence and visualization techniques, and reviews mathematical tools to analyze urban data. We hope our work offers a valuable summary of these ideas and provides the base for future investigations.

Original languageEnglish
Pages (from-to)119-143
Number of pages25
JournalBulletin of Computational Applied Mathematics
Volume12
Issue number1
StatePublished - 2024

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

Keywords

  • Data adjustment
  • Data interpolation
  • Data visualization
  • Urban mobility

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