Blue collar laborers’ travel pattern recognition: Machine learning classifier approach
نویسندگان
چکیده
This paper proposes a pattern recognition model to develop clusters of homogenous activities for blue-collar workers in the State Qatar. The activity-based data from travel diary 1051 collected by Ministry Transportation and Communication (MoTC) Qatar was used analysis. A is applied revealed preference (RP) survey obtained workers. Raw preprocessing outliers detection filtering algorithms were at first stage analysis, consequently, an matrix developed each household. research methodology undertaken this comprises combination different machine learning techniques, predominantly applying clustering classification methods. bagged Clustering algorithm employed identify number clusters, then C-Means Pamk implemented validate results. Meanwhile, interdependencies between resulted socio-demographic attributes households examined using crosstabulation study results show significant diversity amongst terms trip purpose, modal split, destination choice, occupation. Furthermore, whilst Bagged Clusters techniques on three yielded similar results, Cmeans differed significantly clusters. Applying such models big complex activity datasets could assist transport planners understand needs segments population well formulating better-informed strategies.
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ژورنال
عنوان ژورنال: Transportation research interdisciplinary perspectives
سال: 2021
ISSN: ['2590-1982']
DOI: https://doi.org/10.1016/j.trip.2021.100506