Clustering and Forecasting Urban Bus Passenger Demand with a Combination of Time Series Models

[EN]The present paper focuses on the analysis of large data sets from public transport networks, more specifically, on how to predict urban bus passenger demand. A series of steps are proposed to ease the understanding of passenger demand. First, given the large number of stops in the bus network, t...

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Authors: Mariñas-Collado, Irene, Sipols, Ana E., Santos Martín, María Teresa, Frutos Bernal, Elisa
Format: article
Status:Published version
Publication Date:2022
Country:España
Institution:Universidad de Salamanca (USAL)
Repository:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/161132
Online Access:http://hdl.handle.net/10366/161132
Access Level:Open access
Keyword:Time series models
Big data
Clustering
Cointegration
Forecasting
Combination
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spelling Clustering and Forecasting Urban Bus Passenger Demand with a Combination of Time Series ModelsMariñas-Collado, IreneSipols, Ana E.Santos Martín, María TeresaFrutos Bernal, ElisaTime series modelsBig dataClusteringCointegrationForecastingCombination[EN]The present paper focuses on the analysis of large data sets from public transport networks, more specifically, on how to predict urban bus passenger demand. A series of steps are proposed to ease the understanding of passenger demand. First, given the large number of stops in the bus network, these are divided into clusters and then different models are fitted for a representative of each of the clusters. The aim is to compare and combine the predictions associated with traditional methods, such as exponential smoothing or ARIMA, with machine learning methods, such as support vector machines or artificial neural networks. Moreover, support vector machine predictions are improved by incorporating explanatory variables with temporal structure and moving averages. Finally, through cointegration techniques, the results obtained for the representative of each group are extrapolated to the rest of the series within the same cluster. A case study in the city of Salamanca (Spain) is presented to illustrate the problem.MDPI202420242022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10366/161132reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)Inglésinfo:eu-repo/semantics/openAccessoai:gredos.usal.es:10366/1611322026-06-07T06:28:51Z
dc.title.none.fl_str_mv Clustering and Forecasting Urban Bus Passenger Demand with a Combination of Time Series Models
title Clustering and Forecasting Urban Bus Passenger Demand with a Combination of Time Series Models
spellingShingle Clustering and Forecasting Urban Bus Passenger Demand with a Combination of Time Series Models
Mariñas-Collado, Irene
Time series models
Big data
Clustering
Cointegration
Forecasting
Combination
title_short Clustering and Forecasting Urban Bus Passenger Demand with a Combination of Time Series Models
title_full Clustering and Forecasting Urban Bus Passenger Demand with a Combination of Time Series Models
title_fullStr Clustering and Forecasting Urban Bus Passenger Demand with a Combination of Time Series Models
title_full_unstemmed Clustering and Forecasting Urban Bus Passenger Demand with a Combination of Time Series Models
title_sort Clustering and Forecasting Urban Bus Passenger Demand with a Combination of Time Series Models
dc.creator.none.fl_str_mv Mariñas-Collado, Irene
Sipols, Ana E.
Santos Martín, María Teresa
Frutos Bernal, Elisa
author Mariñas-Collado, Irene
author_facet Mariñas-Collado, Irene
Sipols, Ana E.
Santos Martín, María Teresa
Frutos Bernal, Elisa
author_role author
author2 Sipols, Ana E.
Santos Martín, María Teresa
Frutos Bernal, Elisa
author2_role author
author
author
dc.subject.none.fl_str_mv Time series models
Big data
Clustering
Cointegration
Forecasting
Combination
topic Time series models
Big data
Clustering
Cointegration
Forecasting
Combination
description [EN]The present paper focuses on the analysis of large data sets from public transport networks, more specifically, on how to predict urban bus passenger demand. A series of steps are proposed to ease the understanding of passenger demand. First, given the large number of stops in the bus network, these are divided into clusters and then different models are fitted for a representative of each of the clusters. The aim is to compare and combine the predictions associated with traditional methods, such as exponential smoothing or ARIMA, with machine learning methods, such as support vector machines or artificial neural networks. Moreover, support vector machine predictions are improved by incorporating explanatory variables with temporal structure and moving averages. Finally, through cointegration techniques, the results obtained for the representative of each group are extrapolated to the rest of the series within the same cluster. A case study in the city of Salamanca (Spain) is presented to illustrate the problem.
publishDate 2022
dc.date.none.fl_str_mv 2022
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10366/161132
url http://hdl.handle.net/10366/161132
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname:Universidad de Salamanca (USAL)
instname_str Universidad de Salamanca (USAL)
reponame_str GREDOS. Repositorio Institucional de la Universidad de Salamanca
collection GREDOS. Repositorio Institucional de la Universidad de Salamanca
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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