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Connected and Multimodal Passenger Transport Through Big Data Analytics: Case Tampere City Region, Finland

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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 proceedingConference contributionScientificpeer-review

Harvard

Viri, R, Aunimo, L & Aramo-Immonen, H 2019, Connected and Multimodal Passenger Transport Through Big Data Analytics: Case Tampere City Region, Finland. in LM Camarinha-Matos, H Afsarmanesh & D Antonelli (eds), Collaborative Networks and Digital Transformation - 20th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2019, Proceedings. IFIP Advances in Information and Communication Technology, vol. 568, Springer New York LLC, pp. 527-538, IFIP Working Conference on Virtual Enterprises, Turin, Italy, 23/09/19. https://doi.org/10.1007/978-3-030-28464-0_46

APA

Viri, R., Aunimo, L., & Aramo-Immonen, H. (2019). Connected and Multimodal Passenger Transport Through Big Data Analytics: Case Tampere City Region, Finland. In L. M. Camarinha-Matos, H. Afsarmanesh, & D. Antonelli (Eds.), Collaborative Networks and Digital Transformation - 20th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2019, Proceedings (pp. 527-538). (IFIP Advances in Information and Communication Technology; Vol. 568). Springer New York LLC. https://doi.org/10.1007/978-3-030-28464-0_46

Vancouver

Viri R, Aunimo L, Aramo-Immonen H. Connected and Multimodal Passenger Transport Through Big Data Analytics: Case Tampere City Region, Finland. In Camarinha-Matos LM, Afsarmanesh H, Antonelli D, editors, Collaborative Networks and Digital Transformation - 20th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2019, Proceedings. Springer New York LLC. 2019. p. 527-538. (IFIP Advances in Information and Communication Technology). https://doi.org/10.1007/978-3-030-28464-0_46

Author

Viri, Riku ; Aunimo, Lili ; Aramo-Immonen, Heli. / Connected and Multimodal Passenger Transport Through Big Data Analytics : Case Tampere City Region, Finland. Collaborative Networks and Digital Transformation - 20th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2019, Proceedings. editor / Luis M. Camarinha-Matos ; Hamideh Afsarmanesh ; Dario Antonelli. Springer New York LLC, 2019. pp. 527-538 (IFIP Advances in Information and Communication Technology).

Bibtex - Download

@inproceedings{c9635e265d5e4837a3814d7353e18eca,
title = "Connected and Multimodal Passenger Transport Through Big Data Analytics: Case Tampere City Region, Finland",
abstract = "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.",
keywords = "Analytics, API, Big data, Collaborative network, Cyber-physical social system, Mobility, Open data, Passenger transport",
author = "Riku Viri and Lili Aunimo and Heli Aramo-Immonen",
note = "jufoid=84293",
year = "2019",
doi = "10.1007/978-3-030-28464-0_46",
language = "English",
isbn = "9783030284633",
series = "IFIP Advances in Information and Communication Technology",
publisher = "Springer New York LLC",
pages = "527--538",
editor = "Camarinha-Matos, {Luis M.} and Hamideh Afsarmanesh and Dario Antonelli",
booktitle = "Collaborative Networks and Digital Transformation - 20th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2019, Proceedings",

}

RIS (suitable for import to EndNote) - Download

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 -