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 ProceedingsConference paper (refereed)Researchpeer-review

64 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. © Springer International Publishing AG 2017.
Original languageEnglish
Title of host publicationSmart Cities : Second International Conference, Smart-CT 2017, Málaga, Spain, June 14-16, 2017, Proceedings
EditorsEnrique ALBA, Francisco CHICANO, Gabriel LUQUE
PublisherSpringer
Pages107-117
Number of pages11
ISBN (Electronic)9783319595139
ISBN (Print)9783319595122
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event2nd International Conference on Smart Cities - Málaga, Spain
Duration: 14 Jun 201416 Jun 2014

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10268
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameInformation Systems and Applications, incl. Internet/Web, and HCI
PublisherSpringer
ISSN (Print)2946-1634
ISSN (Electronic)2946-1642

Conference

Conference2nd International Conference on Smart Cities
Abbreviated titleSmart-CT 2017
Country/TerritorySpain
CityMálaga
Period14/06/1416/06/14

Bibliographical note

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

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