A scalable platform for big data analysis in public transport

Abstract

Summary 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.

Digital twin-driven prognostics and health management for industrial assets

“…The connection between these functional models and data in a DT model can be established by Apache Beam based on constrained application protocol (CoAP). Apache Beam 78 handles stream and batch data efficiently. CoAP 79 supports fast response and real-time communication.…”

Special issue on High Performance Computing Conference (BASARIM‐2020)

“…In their contribution to this special issue, Ucak et al 3 present a scalable public transport analysis platform named Cermoni while applying the Apache Beam programming model. All possible events and actions may be taken as potential data sources to be analyzed, and this is true for public transport, as well, where analysis of this type is of critical importance in the design of urban transport and efforts to adapt to changes.…”