Blue collar laborers' travel pattern recognition: Machine learning classifier approach. (December 2021)

• Record Type: Journal Article
• Authors: Alkhereibi, Aya Hasan; Tahmasseby, Shahram; Mohammed, Semira; Muley, Deepti
• Is Part Of: Transportation research interdisciplinary perspectives. Volume 12(2022)
• Publication Date: 2021-12
• Subjects: Travel behavior -- Transportation planning -- Activity-based model -- Machine learning -- Blue collar travel diary -- Travel Pattern; Transportation -- Periodicals; 388.05
• DOI: 10.1016/j.trip.2021.100506
• Languages: English
• ISSNs: 2590-1982

Abstract

Highlights: A novel comprehensive activity-based travel model for blue-collar workers in the State of Qatar. The models' Results show significant alterations amongst the clusters in terms of trip purpose, modal split, destination choice, and occupation. The results showed that the cluster analysis techniques are mathematically efficient and can classify individuals such as blue-collars into groups and consequently scrutinize travel behavior. The Bagged Clusters and PamkBest Clusters techniques on the three attributes yielded similar results, the Cmeans Clusters differed significantly in a number of the clusters. Applying pattern recognition models on big activity datasets could assist transport planners to understand the travel needs of segments of the population well and formulating better-informed strategies. Abstract: 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.