Predicting car park occupancy rates in smart cities

Daniel H. STOLFI; Enrique ALBA; Xin YAO

doi:10.1007/978-3-319-59513-9_11

Predicting car park occupancy rates in smart cities

Daniel H. STOLFI^*
, Enrique ALBA
, Xin YAO

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

71 Citations (Scopus)

Abstract

In this article we address the study of parking occupancy data published by the Birmingham city council with the aim of testing several prediction strategies (polynomial fitting, Fourier series, k-means clustering, and time series) and analyzing their results. We have used cross validation to train the predictors and then tested them on unseen occupancy data. Additionally, we present a web page prototype to visualize the current and historical parking data on a map, allowing users to consult the occupancy rate forecast to satisfy their parking needs up to one day in advance. We think that the combination of accurate intelligent techniques plus final user services for citizens is the direction to follow for knowledge-based real smart cities.

Original language	English
Title of host publication	Smart Cities : Second International Conference, Smart-CT 2017, Málaga, Spain, June 14-16, 2017, Proceedings
Editors	Enrique ALBA, Francisco CHICANO, Gabriel LUQUE
Publisher	Springer
Pages	107-117
Number of pages	11
ISBN (Electronic)	9783319595139
ISBN (Print)	9783319595122
DOIs	https://doi.org/10.1007/978-3-319-59513-9_11
Publication status	Published - 2017
Externally published	Yes
Event	2nd International Conference on Smart Cities - Málaga, Spain Duration: 14 Jun 2014 → 16 Jun 2014

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	10268
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Name	Information Systems and Applications, incl. Internet/Web, and HCI
Publisher	Springer
ISSN (Print)	2946-1634
ISSN (Electronic)	2946-1642

Conference

Conference	2nd International Conference on Smart Cities
Abbreviated title	Smart-CT 2017
Country/Territory	Spain
City	Málaga
Period	14/06/14 → 16/06/14

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

Funding

This research is partially funded by the Spanish MINECO project TIN2014-57341-R (http://moveon.lcc.uma.es). Daniel H. Stolfi is supported by a FPU grant (FPU13/00954) from the Spanish Ministry of Education, Culture and Sports. University of Malaga. International Campus of Excellence Andalucia TECH.

Keywords

K-means
Machine learning
Parking
Smart city
Smart mobility
Time series

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-59513-9_11

Cite this

STOLFI, D. H., ALBA, E., & YAO, X. (2017). Predicting car park occupancy rates in smart cities. In E. ALBA, F. CHICANO, & G. LUQUE (Eds.), Smart Cities : Second International Conference, Smart-CT 2017, Málaga, Spain, June 14-16, 2017, Proceedings (pp. 107-117). (Lecture Notes in Computer Science; Vol. 10268), (Information Systems and Applications, incl. Internet/Web, and HCI). Springer. https://doi.org/10.1007/978-3-319-59513-9_11

STOLFI, Daniel H. ; ALBA, Enrique ; YAO, Xin. / Predicting car park occupancy rates in smart cities. Smart Cities : Second International Conference, Smart-CT 2017, Málaga, Spain, June 14-16, 2017, Proceedings. editor / Enrique ALBA ; Francisco CHICANO ; Gabriel LUQUE. Springer, 2017. pp. 107-117 (Lecture Notes in Computer Science). (Information Systems and Applications, incl. Internet/Web, and HCI).

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title = "Predicting car park occupancy rates in smart cities",

abstract = "In this article we address the study of parking occupancy data published by the Birmingham city council with the aim of testing several prediction strategies (polynomial fitting, Fourier series, k-means clustering, and time series) and analyzing their results. We have used cross validation to train the predictors and then tested them on unseen occupancy data. Additionally, we present a web page prototype to visualize the current and historical parking data on a map, allowing users to consult the occupancy rate forecast to satisfy their parking needs up to one day in advance. We think that the combination of accurate intelligent techniques plus final user services for citizens is the direction to follow for knowledge-based real smart cities.",

keywords = "K-means, Machine learning, Parking, Smart city, Smart mobility, Time series",

author = "STOLFI, \{Daniel H.\} and Enrique ALBA and Xin YAO",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 2nd International Conference on Smart Cities, Smart-CT 2017 ; Conference date: 14-06-2014 Through 16-06-2014",

year = "2017",

doi = "10.1007/978-3-319-59513-9\_11",

language = "English",

isbn = "9783319595122",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "107--117",

editor = "ALBA, \{Enrique \} and CHICANO, \{Francisco \} and LUQUE, \{Gabriel \}",

booktitle = "Smart Cities : Second International Conference, Smart-CT 2017, M{\'a}laga, Spain, June 14-16, 2017, Proceedings",

}

STOLFI, DH, ALBA, E & YAO, X 2017, Predicting car park occupancy rates in smart cities. in E ALBA, F CHICANO & G LUQUE (eds), Smart Cities : Second International Conference, Smart-CT 2017, Málaga, Spain, June 14-16, 2017, Proceedings. Lecture Notes in Computer Science, vol. 10268, Information Systems and Applications, incl. Internet/Web, and HCI, Springer, pp. 107-117, 2nd International Conference on Smart Cities, Málaga, Spain, 14/06/14. https://doi.org/10.1007/978-3-319-59513-9_11

Predicting car park occupancy rates in smart cities. / STOLFI, Daniel H.; ALBA, Enrique; YAO, Xin.
Smart Cities : Second International Conference, Smart-CT 2017, Málaga, Spain, June 14-16, 2017, Proceedings. ed. / Enrique ALBA; Francisco CHICANO; Gabriel LUQUE. Springer, 2017. p. 107-117 (Lecture Notes in Computer Science; Vol. 10268), (Information Systems and Applications, incl. Internet/Web, and HCI).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

TY - GEN

T1 - Predicting car park occupancy rates in smart cities

AU - STOLFI, Daniel H.

AU - ALBA, Enrique

AU - YAO, Xin

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - In this article we address the study of parking occupancy data published by the Birmingham city council with the aim of testing several prediction strategies (polynomial fitting, Fourier series, k-means clustering, and time series) and analyzing their results. We have used cross validation to train the predictors and then tested them on unseen occupancy data. Additionally, we present a web page prototype to visualize the current and historical parking data on a map, allowing users to consult the occupancy rate forecast to satisfy their parking needs up to one day in advance. We think that the combination of accurate intelligent techniques plus final user services for citizens is the direction to follow for knowledge-based real smart cities.

AB - In this article we address the study of parking occupancy data published by the Birmingham city council with the aim of testing several prediction strategies (polynomial fitting, Fourier series, k-means clustering, and time series) and analyzing their results. We have used cross validation to train the predictors and then tested them on unseen occupancy data. Additionally, we present a web page prototype to visualize the current and historical parking data on a map, allowing users to consult the occupancy rate forecast to satisfy their parking needs up to one day in advance. We think that the combination of accurate intelligent techniques plus final user services for citizens is the direction to follow for knowledge-based real smart cities.

KW - K-means

KW - Machine learning

KW - Parking

KW - Smart city

KW - Smart mobility

KW - Time series

UR - https://www.scopus.com/pages/publications/85020907711

U2 - 10.1007/978-3-319-59513-9_11

DO - 10.1007/978-3-319-59513-9_11

M3 - Conference paper (refereed)

SN - 9783319595122

T3 - Lecture Notes in Computer Science

SP - 107

EP - 117

BT - Smart Cities : Second International Conference, Smart-CT 2017, Málaga, Spain, June 14-16, 2017, Proceedings

A2 - ALBA, Enrique

A2 - CHICANO, Francisco

A2 - LUQUE, Gabriel

PB - Springer

T2 - 2nd International Conference on Smart Cities

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ER -

STOLFI DH, ALBA E, YAO X. Predicting car park occupancy rates in smart cities. In ALBA E, CHICANO F, LUQUE G, editors, Smart Cities : Second International Conference, Smart-CT 2017, Málaga, Spain, June 14-16, 2017, Proceedings. Springer. 2017. p. 107-117. (Lecture Notes in Computer Science). (Information Systems and Applications, incl. Internet/Web, and HCI). Epub 2017 May 26. doi: 10.1007/978-3-319-59513-9_11

Predicting car park occupancy rates in smart cities

Abstract

Publication series

Conference

Bibliographical note

Funding

Keywords

UN SDGs

Access to Document

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Cite this