Connected and Multimodal Passenger Transport Through Big Data Analytics: Case Tampere City Region, Finland
2019 (English)In: Collaborative Networks and Digital Transformation / [ed] Camarinha-Matos, L.M.; Afsarmanesh, H.; Antonelli, D., Springer, 2019, p. 527-538Conference paper, Published paper (Refereed)
Abstract [en]
Passenger transport is becoming more and more connected and multimodal. Instead of just taking a series of vehicles to complete a journey, the passenger is actually interacting with a connected cyber-physical social (CPS) transport system. In this study, we present a case study where big data from various sources is combined and analyzed to support and enhance the transport system in the Tampere region. Different types of static and real-time data sources and transportation related APIs are investigated. The goal is to find ways in which big data and collaborative networks can be used to improve the CPS transport system itself and the passenger satisfaction related to it. The study shows that even though the exploitation of big data does not directly improve the state of the physical transport infrastructure, it helps in utilizing more of its capacity. Secondly, the use of big data makes it more attractive to passengers.
Place, publisher, year, edition, pages
Springer, 2019. p. 527-538
Series
IFIP Advances in Information and Communication Technology, ISSN 1868-4238, E-ISSN 1868-422X ; 568
Keywords [en]
Big data, Analytics, Collaborative network, API, Cyber-physical social system, Passenger transport, Mobility, Open data
National Category
Information Systems, Social aspects
Identifiers
URN: urn:nbn:se:oru:diva-85441DOI: 10.1007/978-3-030-28464-0_46ISI: 000560401400046Scopus ID: 2-s2.0-85072972298ISBN: 978-3-030-28464-0 (electronic)ISBN: 978-3-030-28463-3 (print)OAI: oai:DiVA.org:oru-85441DiVA, id: diva2:1464111
Conference
20th IFIP WG 5.5 Working Conference on Virtual Enterprises (PRO-VE 2019), Turin, Italy, September 23-25, 2019
2020-09-042020-09-042020-09-04Bibliographically approved