Show simple item record

dc.contributor.authorCortez Ordóñez, Alexandra Piedad
dc.contributor.authorVázquez Alcocer, Pere Pau
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Estadística i Investigació Operativa
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2022-03-02T12:52:52Z
dc.date.issued2021
dc.identifier.citationCortez, A.; Vazquez, P. Analysis and visual exploration of prediction algorithms for public bicycle sharing systems. A: International Conference on Computer Graphics, Visualization, Computer Vision and image Processing. "International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing (CGVCVIP2021), Connected Smart Cities (CSC2021), and Big Data Analytics, Data Mining and Computational Intelligence (BIGDACI 2021): held at the 15th Multi-Conference on Computer Science and Information Systems (MCCSIS 2021): online, 20-23 July 2021". New York: Curran associates, 2021, p. 61-70. ISBN 978-1-7138-3578-3.
dc.identifier.isbn978-1-7138-3578-3
dc.identifier.urihttp://hdl.handle.net/2117/363301
dc.description.abstractPublic bicycle sharing systems have become an increasingly popular means of transportation in many cities around the world. However, the information shown in mobile apps or websites is commonly limited to the system’s current status and is of little use for both citizens and responsible planning entities. The vast amount of data produced by these managing systems makes it feasible to elaborate and present predictive models that may help its users in the decision-making process. For example, if a user finds a station empty, the application could provide an estimation of when a new bicycle would be available. In this paper, we explore the suitability of several prediction algorithms applied to this case of bicycle availability, and we present a web-based tool to visually explore their prediction errors under different time frames. Even though a quick quantitative analysis may initially suggest that Random Forest yields a lower error, our visual exploration interface allows us to perform a more thorough analysis and detect subtle but relevant differences between algorithms depending on variables such as the station’s behavior, hourly intervals, days, or types of days (weekdays and weekends). This case illustrates the potential of visual representation together with quantitative metrics to compare prediction algorithms with a higher level of detail, which can, in turn, assist application designers and decision-makers to dynamically adjust the best model for their specific scenarios.
dc.description.sponsorshipPartially supported by project TIN2017-88515-C2-1-R(GEN3DLIVE), from the Spanish Ministerio de Economía y Competitividad, by 839 FEDER (EU) funds.
dc.format.extent10 p.
dc.language.isoeng
dc.publisherCurran associates
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Anàlisi numèrica
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa::Programació matemàtica
dc.subject.lcshNumerical analysis
dc.subject.lcshProgramming (Mathematics)
dc.subject.otherVisualization systems and tools
dc.subject.otherVisual analytics
dc.subject.otherBike Sharing Systems
dc.subject.otherForecasting algorithms
dc.titleAnalysis and visual exploration of prediction algorithms for public bicycle sharing systems
dc.typeConference lecture
dc.subject.lemacAnàlisi numèrica
dc.subject.lemacProgramació (Matemàtica)
dc.contributor.groupUniversitat Politècnica de Catalunya. ViRVIG - Grup de Recerca en Visualització, Realitat Virtual i Interacció Gràfica
dc.description.peerreviewedPeer Reviewed
dc.subject.amsClassificació AMS::65 Numerical analysis::65K Mathematical programming, optimization and variational techniques
dc.subject.amsClassificació AMS::90 Operations research, mathematical programming::90C Mathematical programming
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac31971922
dc.description.versionPostprint (author's final draft)
dc.date.lift10000-01-01
local.citation.authorCortez, A.; Vazquez, P.
local.citation.contributorInternational Conference on Computer Graphics, Visualization, Computer Vision and image Processing
local.citation.pubplaceNew York
local.citation.publicationNameInternational Conference on Computer Graphics, Visualization, Computer Vision and Image Processing (CGVCVIP2021), Connected Smart Cities (CSC2021), and Big Data Analytics, Data Mining and Computational Intelligence (BIGDACI 2021): held at the 15th Multi-Conference on Computer Science and Information Systems (MCCSIS 2021): online, 20-23 July 2021
local.citation.startingPage61
local.citation.endingPage70


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record