Connected and Multimodal Passenger Transport Through Big Data Analytics: Case Tampere City Region, Finland
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
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Connected and Multimodal Passenger Transport Through Big Data Analytics : Case Tampere City Region, Finland. / Viri, Riku; Aunimo, Lili; Aramo-Immonen, Heli.
Collaborative Networks and Digital Transformation - 20th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2019, Proceedings. ed. / Luis M. Camarinha-Matos; Hamideh Afsarmanesh; Dario Antonelli. Springer New York LLC, 2019. p. 527-538 (IFIP Advances in Information and Communication Technology; Vol. 568).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
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TY - GEN
T1 - Connected and Multimodal Passenger Transport Through Big Data Analytics
T2 - Case Tampere City Region, Finland
AU - Viri, Riku
AU - Aunimo, Lili
AU - Aramo-Immonen, Heli
N1 - jufoid=84293
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Analytics
KW - API
KW - Big data
KW - Collaborative network
KW - Cyber-physical social system
KW - Mobility
KW - Open data
KW - Passenger transport
U2 - 10.1007/978-3-030-28464-0_46
DO - 10.1007/978-3-030-28464-0_46
M3 - Conference contribution
SN - 9783030284633
T3 - IFIP Advances in Information and Communication Technology
SP - 527
EP - 538
BT - Collaborative Networks and Digital Transformation - 20th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2019, Proceedings
A2 - Camarinha-Matos, Luis M.
A2 - Afsarmanesh, Hamideh
A2 - Antonelli, Dario
PB - Springer New York LLC
ER -