Urban Mobility Patterns with Linguistic Summarization: Insights from Bicycle Data

Şener Fidan F., Aydoğan S., Akay D.

Erciyes Üniversitesi Fen Bilimleri Enstitüsü, vol.39, no.3, pp.538-547, 2023 (Peer-Reviewed Journal)

  • Publication Type: Article / Article
  • Volume: 39 Issue: 3
  • Publication Date: 2023
  • Journal Name: Erciyes Üniversitesi Fen Bilimleri Enstitüsü
  • Journal Indexes: TR DİZİN (ULAKBİM)
  • Page Numbers: pp.538-547
  • Abdullah Gül University Affiliated: Yes

Abstract

This study examines how urban mobility patterns might be analyzed using linguistic summarization, one of the descriptive data analytics tools. The investigation focuses on urban bicycle data, a rich source of knowledge for comprehending urban mobility patterns. The study uses a dataset with several variables: day, hour, station, and card type. The data is turned into linguistic descriptions that offer valuable insights into urban movement through the strength of the fuzzy set. The analysis of travel patterns includes identifying busy stations at various times of the day, user segment preferences (students vs. non-students), and changes in general mobility. The results of the linguistic summarization of the urban cycling data allow for a more thorough knowledge of urban travel patterns. Urban planners, decision-makers, and transportation authorities may now optimize the city's current infrastructure, increase accessibility, and meet its residents' wide range of needs thanks to the results that shed light on the dynamics of urban mobility. The study shows how practical descriptive data analytics can be in revealing information, mainly when used to examine travel patterns utilizing information from urban bicycles.

This study examines how urban mobility patterns might be analyzed using linguistic summarization, one of the descriptive data analytics tools. The investigation focuses on urban bicycle data, a rich source of knowledge for comprehending urban mobility patterns. The study uses a dataset with several variables: day, hour, station, and card type. The data is turned into linguistic descriptions that offer valuable insights into urban movement through the strength of the fuzzy set. The analysis of travel patterns includes identifying busy stations at various times of the day, user segment preferences (students vs. non-students), and changes in general mobility. The results of the linguistic summarization of the urban cycling data allow for a more thorough knowledge of urban travel patterns. Urban planners, decision-makers, and transportation authorities may now optimize the city's current infrastructure, increase accessibility, and meet its residents' wide range of needs thanks to the results that shed light on the dynamics of urban mobility. The study shows how practical descriptive data analytics can be in revealing information, mainly when used to examine travel patterns utilizing information from urban bicycles.