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

The current paper concentrates on the examination of extensive datasets derived from public transportation networks, specifically addressing the prediction of urban bus passenger demand. The approach involves a series of steps designed to enhance the comprehension of passenger demand. Initially, due...

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Detalhes bibliográficos
Autores: Mariñas Collado, Irene, Sipols, Ana E., Santos Martín, M. Teresa, Frutos Bernal, Elisa
Formato: artículo
Fecha de publicación:2022
País:España
Recursos:Universidad Rey Juan Carlos
Repositorio:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
OAI Identifier:oai:burjcdigital.urjc.es:10115/28340
Acesso em linha:https://hdl.handle.net/10115/28340
Access Level:acceso abierto
Palavra-chave:forecasting
time series models
Big Data
Clustering
Cointegration
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, M. TeresaFrutos Bernal, Elisaforecastingtime series modelsBig DataClusteringCointegrationCombinationThe current paper concentrates on the examination of extensive datasets derived from public transportation networks, specifically addressing the prediction of urban bus passenger demand. The approach involves a series of steps designed to enhance the comprehension of passenger demand. Initially, due to the substantial number of bus stops in the network, they are categorized into clusters, and distinct models are subsequently developed for a representative from each cluster. The objective is to compare and integrate predictions generated by conventional methods like exponential smoothing or ARIMA with those from machine learning techniques, such as support vector machines or artificial neural networks. Furthermore, the accuracy of support vector machine predictions is refined by incorporating explanatory variables with temporal structures and moving averages. Ultimately, through cointegration techniques, the outcomes obtained for the representative of each group are extrapolated to the remaining series within the same cluster. The paper illustrates the application of these methods through a case study conducted in the city of Salamanca, Spain.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/articleapplication/pdfhttps://hdl.handle.net/10115/28340reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlosinstname:Universidad Rey Juan CarlosInglésAttribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:burjcdigital.urjc.es:10115/283402026-06-24T12:48:17Z
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
forecasting
time series models
Big Data
Clustering
Cointegration
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, M. Teresa
Frutos Bernal, Elisa
author Mariñas Collado, Irene
author_facet Mariñas Collado, Irene
Sipols, Ana E.
Santos Martín, M. Teresa
Frutos Bernal, Elisa
author_role author
author2 Sipols, Ana E.
Santos Martín, M. Teresa
Frutos Bernal, Elisa
author2_role author
author
author
dc.subject.none.fl_str_mv forecasting
time series models
Big Data
Clustering
Cointegration
Combination
topic forecasting
time series models
Big Data
Clustering
Cointegration
Combination
description The current paper concentrates on the examination of extensive datasets derived from public transportation networks, specifically addressing the prediction of urban bus passenger demand. The approach involves a series of steps designed to enhance the comprehension of passenger demand. Initially, due to the substantial number of bus stops in the network, they are categorized into clusters, and distinct models are subsequently developed for a representative from each cluster. The objective is to compare and integrate predictions generated by conventional methods like exponential smoothing or ARIMA with those from machine learning techniques, such as support vector machines or artificial neural networks. Furthermore, the accuracy of support vector machine predictions is refined by incorporating explanatory variables with temporal structures and moving averages. Ultimately, through cointegration techniques, the outcomes obtained for the representative of each group are extrapolated to the remaining series within the same cluster. The paper illustrates the application of these methods through a case study conducted in the city of Salamanca, Spain.
publishDate 2022
dc.date.none.fl_str_mv 2022
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10115/28340
url https://hdl.handle.net/10115/28340
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
instname:Universidad Rey Juan Carlos
instname_str Universidad Rey Juan Carlos
reponame_str BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
collection BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812455

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