A scalable platform for big data analysis in public transport

Uçak E., Karagümüş E., ŞENER C.

Concurrency and Computation: Practice and Experience, vol.34, no.9, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Abstract
  • Volume: 34 Issue: 9
  • Publication Date: 2022
  • Doi Number: 10.1002/cpe.6534
  • Journal Name: Concurrency and Computation: Practice and Experience
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Keywords: Apache Beam, big data analysis, Google Cloud Dataflow, public transport, scalability, DATA ANALYTICS
  • Middle East Technical University Affiliated: Yes


© 2021 John Wiley & Sons Ltd.Any life event or action can be seen as a potential source of data to analyze. By analyzing such data, we can gain insights into the facts. The situation is no different in public transport. Researchers working in the fields of transport and traffic have stated that such an analysis would be invaluable in designing urban transport and particularly in adapting to current changes. In this study, a scalable public transport analysis platform named Cermoni is developed using the Apache Beam programming model. It can analyze in near-real-time smart card and vehicle location data collected, classified as big data with its high production speed. The performance of the platform was tested on Google Cloud Dataflow service using real-world data gathered from Konya, one of the largest metropolitan cities in Turkey, and the results are discussed in detail.