Forecasting Key Performance Indicators for Smart Connected Vehicles

David Skiöld; Shivani Arora; R.-C. Mihailescu; Ramtin Balaghi

Forecasting Key Performance Indicators for Smart Connected Vehicles

David Skiöld, Shivani Arora, R.-C. Mihailescu, Ramtin Balaghi

School of Mathematical & Computer Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

As connectivity has been introduced to the car industry, automotive companies have in-use cars which are connected to the internet. A key concern in this context represents the difficulty of knowing how the connection quality changes over time and if there are associated issues. In this work we describe the use of CDR data from connected cars supplied by Volvo to build and study forecasting models that predict how relevant KPIs change over time. Our experiments show promising results for this predictive task, which can lead to improving user experience of connectivity in smart vehicles.

Original language	English
Title of host publication	Advances in Artificial Intelligence. IBERAMIA 2022
Editors	Ana Cristina Bicharra Garcia, Mariza Ferro, Julio Cesar Rodríguez Ribón
Publisher	Springer
Pages	414-415
Number of pages	2
ISBN (Electronic)	9783031224195
ISBN (Print)	9783031224188
Publication status	Published - 3 Jan 2023

Publication series

Name	Lecture Notes in Computer Science
Volume	13788
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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Cite this

Skiöld, D., Arora, S., Mihailescu, R.-C., & Balaghi, R. (2023). Forecasting Key Performance Indicators for Smart Connected Vehicles. In A. C. Bicharra Garcia, M. Ferro, & J. C. Rodríguez Ribón (Eds.), Advances in Artificial Intelligence. IBERAMIA 2022 (pp. 414-415). (Lecture Notes in Computer Science; Vol. 13788). Springer. https://link.springer.com/content/pdf/bbm:978-3-031-22419-5/1?pdf=chapter%20toc

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title = "Forecasting Key Performance Indicators for Smart Connected Vehicles",

abstract = "As connectivity has been introduced to the car industry, automotive companies have in-use cars which are connected to the internet. A key concern in this context represents the difficulty of knowing how the connection quality changes over time and if there are associated issues. In this work we describe the use of CDR data from connected cars supplied by Volvo to build and study forecasting models that predict how relevant KPIs change over time. Our experiments show promising results for this predictive task, which can lead to improving user experience of connectivity in smart vehicles.",

author = "David Ski{\"o}ld and Shivani Arora and R.-C. Mihailescu and Ramtin Balaghi",

year = "2023",

month = jan,

day = "3",

language = "English",

isbn = "9783031224188",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "414--415",

editor = "\{Bicharra Garcia\}, \{Ana Cristina\} and Mariza Ferro and \{Rodr{\'i}guez Rib{\'o}n\}, \{Julio Cesar\}",

booktitle = "Advances in Artificial Intelligence. IBERAMIA 2022",

}

Skiöld, D, Arora, S, Mihailescu, R-C & Balaghi, R 2023, Forecasting Key Performance Indicators for Smart Connected Vehicles. in AC Bicharra Garcia, M Ferro & JC Rodríguez Ribón (eds), Advances in Artificial Intelligence. IBERAMIA 2022. Lecture Notes in Computer Science, vol. 13788, Springer, pp. 414-415. <https://link.springer.com/content/pdf/bbm:978-3-031-22419-5/1?pdf=chapter%20toc>

Forecasting Key Performance Indicators for Smart Connected Vehicles. / Skiöld, David; Arora, Shivani; Mihailescu, R.-C. et al.
Advances in Artificial Intelligence. IBERAMIA 2022. ed. / Ana Cristina Bicharra Garcia; Mariza Ferro; Julio Cesar Rodríguez Ribón. Springer, 2023. p. 414-415 (Lecture Notes in Computer Science; Vol. 13788).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Forecasting Key Performance Indicators for Smart Connected Vehicles

AU - Skiöld, David

AU - Arora, Shivani

AU - Mihailescu, R.-C.

AU - Balaghi, Ramtin

PY - 2023/1/3

Y1 - 2023/1/3

N2 - As connectivity has been introduced to the car industry, automotive companies have in-use cars which are connected to the internet. A key concern in this context represents the difficulty of knowing how the connection quality changes over time and if there are associated issues. In this work we describe the use of CDR data from connected cars supplied by Volvo to build and study forecasting models that predict how relevant KPIs change over time. Our experiments show promising results for this predictive task, which can lead to improving user experience of connectivity in smart vehicles.

AB - As connectivity has been introduced to the car industry, automotive companies have in-use cars which are connected to the internet. A key concern in this context represents the difficulty of knowing how the connection quality changes over time and if there are associated issues. In this work we describe the use of CDR data from connected cars supplied by Volvo to build and study forecasting models that predict how relevant KPIs change over time. Our experiments show promising results for this predictive task, which can lead to improving user experience of connectivity in smart vehicles.

M3 - Conference contribution

SN - 9783031224188

T3 - Lecture Notes in Computer Science

SP - 414

EP - 415

BT - Advances in Artificial Intelligence. IBERAMIA 2022

A2 - Bicharra Garcia, Ana Cristina

A2 - Ferro, Mariza

A2 - Rodríguez Ribón, Julio Cesar

PB - Springer

ER -

Forecasting Key Performance Indicators for Smart Connected Vehicles

Abstract

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