Blue collar laborers’ travel pattern recognition: Machine learning classifier approach

This paper proposes a pattern recognition model to develop clusters of homogenous activities for blue-collar workers in the State of Qatar. The activity-based data from the travel diary of 1051 blue-collar workers collected by the Ministry of Transportation and Communication (MoTC) in Qatar was used...

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Autores principales:	Aya Hasan Alkhereibi, Shahram Tahmasseby, Semira Mohammed, Deepti Muley
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Travel behavior Transportation planning Activity-based model Machine learning Blue collar travel diary Travel Pattern Transportation and communications HE1-9990
Acceso en línea:	https://doaj.org/article/d6eaea27e6b84cdeab7c61f8a5e8d0ad
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id	oai:doaj.org-article:d6eaea27e6b84cdeab7c61f8a5e8d0ad
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spelling	oai:doaj.org-article:d6eaea27e6b84cdeab7c61f8a5e8d0ad2021-12-02T05:03:38ZBlue collar laborers’ travel pattern recognition: Machine learning classifier approach2590-198210.1016/j.trip.2021.100506https://doaj.org/article/d6eaea27e6b84cdeab7c61f8a5e8d0ad2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2590198221002116https://doaj.org/toc/2590-1982This paper proposes a pattern recognition model to develop clusters of homogenous activities for blue-collar workers in the State of Qatar. The activity-based data from the travel diary of 1051 blue-collar workers collected by the Ministry of Transportation and Communication (MoTC) in Qatar was used for analysis. A pattern recognition model is applied to a revealed preference (RP) survey obtained from the Ministry of Transportation and Communication (MoTC) in Qatar for the travel diary for blue-collar workers. Raw data preprocessing and outliers detection and filtering algorithms were applied at the first stage of the analysis, and consequently, an activity-based travel matrix was developed for each household. The research methodology undertaken in this paper comprises a combination of different machine learning techniques, predominantly by applying clustering and classification methods. A bagged Clustering algorithm was employed to identify the number of clusters, then the C-Means algorithm and the Pamk algorithm were implemented to validate the results. Meanwhile, the interdependencies between the resulted clusters and the socio-demographic attributes for the households were examined using crosstabulation analysis. The study results show significant diversity amongst the clusters in terms of trip purpose, modal split, destination choice, and occupation. Furthermore, whilst the Bagged Clusters and Pamk Clusters techniques on the three attributes yielded similar results, the Cmeans Clusters differed significantly in a number of the clusters. Applying such pattern recognition models on big and complex activity datasets could assist transport planners to understand the travel needs of segments of the population well and formulating better-informed strategies.Aya Hasan AlkhereibiShahram TahmassebySemira MohammedDeepti MuleyElsevierarticleTravel behaviorTransportation planningActivity-based modelMachine learningBlue collar travel diaryTravel PatternTransportation and communicationsHE1-9990ENTransportation Research Interdisciplinary Perspectives, Vol 12, Iss , Pp 100506- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Travel behavior Transportation planning Activity-based model Machine learning Blue collar travel diary Travel Pattern Transportation and communications HE1-9990
spellingShingle	Travel behavior Transportation planning Activity-based model Machine learning Blue collar travel diary Travel Pattern Transportation and communications HE1-9990 Aya Hasan Alkhereibi Shahram Tahmasseby Semira Mohammed Deepti Muley Blue collar laborers’ travel pattern recognition: Machine learning classifier approach
description	This paper proposes a pattern recognition model to develop clusters of homogenous activities for blue-collar workers in the State of Qatar. The activity-based data from the travel diary of 1051 blue-collar workers collected by the Ministry of Transportation and Communication (MoTC) in Qatar was used for analysis. A pattern recognition model is applied to a revealed preference (RP) survey obtained from the Ministry of Transportation and Communication (MoTC) in Qatar for the travel diary for blue-collar workers. Raw data preprocessing and outliers detection and filtering algorithms were applied at the first stage of the analysis, and consequently, an activity-based travel matrix was developed for each household. The research methodology undertaken in this paper comprises a combination of different machine learning techniques, predominantly by applying clustering and classification methods. A bagged Clustering algorithm was employed to identify the number of clusters, then the C-Means algorithm and the Pamk algorithm were implemented to validate the results. Meanwhile, the interdependencies between the resulted clusters and the socio-demographic attributes for the households were examined using crosstabulation analysis. The study results show significant diversity amongst the clusters in terms of trip purpose, modal split, destination choice, and occupation. Furthermore, whilst the Bagged Clusters and Pamk Clusters techniques on the three attributes yielded similar results, the Cmeans Clusters differed significantly in a number of the clusters. Applying such pattern recognition models on big and complex activity datasets could assist transport planners to understand the travel needs of segments of the population well and formulating better-informed strategies.
format	article
author	Aya Hasan Alkhereibi Shahram Tahmasseby Semira Mohammed Deepti Muley
author_facet	Aya Hasan Alkhereibi Shahram Tahmasseby Semira Mohammed Deepti Muley
author_sort	Aya Hasan Alkhereibi
title	Blue collar laborers’ travel pattern recognition: Machine learning classifier approach
title_short	Blue collar laborers’ travel pattern recognition: Machine learning classifier approach
title_full	Blue collar laborers’ travel pattern recognition: Machine learning classifier approach
title_fullStr	Blue collar laborers’ travel pattern recognition: Machine learning classifier approach
title_full_unstemmed	Blue collar laborers’ travel pattern recognition: Machine learning classifier approach
title_sort	blue collar laborers’ travel pattern recognition: machine learning classifier approach
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/d6eaea27e6b84cdeab7c61f8a5e8d0ad
work_keys_str_mv	AT ayahasanalkhereibi bluecollarlaborerstravelpatternrecognitionmachinelearningclassifierapproach AT shahramtahmasseby bluecollarlaborerstravelpatternrecognitionmachinelearningclassifierapproach AT semiramohammed bluecollarlaborerstravelpatternrecognitionmachinelearningclassifierapproach AT deeptimuley bluecollarlaborerstravelpatternrecognitionmachinelearningclassifierapproach
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Blue collar laborers’ travel pattern recognition: Machine learning classifier approach

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