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Using temporal public transport demand profiles to reveal urban spatial patterns

  • Alonso Espinosa Mireles de Villafranca
  • , Zhiren Huang
  • , Charalampos (Haris) Sipetas
  • Tampere University

Research output: Contribution to conference › Paper › Scientific › peer-review

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Abstract

We present a versatile method, inspired by computational neuroscience, for reconstructing smooth demand profiles from sparse timestamp data for public transport boardings. We show how areas can be clustered based on the similarity of their temporal demand profiles to reveal urban spatial patterns. We use the Helsinki metropolitan region to showcase the method using data on boarding events from the TravelSense data from HSL (the Helsinki region transport authority) collected through their mobile ticketing app. Our results show the show the dependence of travel demand on available public transit and modes and supply volume. Furthermore, the clusters align with extremely well with the types of urban areas in the region. Due to the high supply and even frequency of transit options, the differences in demand profiles are due to mode availability and land-use features rather than frequency patterns.
Original languageEnglish
Number of pages9
Publication statusPublished - 2024
MoE publication typeNot Eligible
EventSymposium of the European Association for Research in Transportation - Aalto University, Espoo, Finland
Duration: 18 Jun 2024 → 20 Jun 2024
Conference number: 12
https://heart2024.aalto.fi/

Conference

ConferenceSymposium of the European Association for Research in Transportation
Abbreviated titlehEART
Country/TerritoryFinland
CityEspoo
Period18/06/2024 → 20/06/2024
Internet address

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

  • Demand Patterns
  • Mobile Data
  • Multi-modal Transport
  • Public Transport

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