Identifying Public Transit Commuters Based on Both the Smartcard Data and Survey Data: A Case Study in Xiamen, China

Abstract

Understanding the travel patterns of public transit commuters was important to the efforts towards improving the service quality, promoting public transit use, and better planning the public transit system. Smartcard data, with its wide coverage and relative abundance, could provide new opportunities to study public transit riders’ behaviors and travel patterns with much less cost than conventional data source. However, the major limitation of smartcard data is the absence of social attributes of the cardholders, so that it cannot clearly extract public transit commuters and explain the mechanism of their travel behaviors. This study employed a machine learning approach called Naive Bayesian Classifier (NBC) to identify public transit commuters based on both the smartcard data and survey data, demonstrated in Xiamen, China. Compared with existing methods which were plagued by the validation of the accuracy of the identification results, the adopted approach was a machine learning algorithm with functions of accuracy checking. The classifier was trained and tested by survey data obtained from 532 valid questionnaires. The accuracy rate for identification of public transit commuters was 92% in the test instances. Then, under a low calculation load, it identified the objectives in smartcard data without requiring travel regularity assumptions of public transit commuters. Nearly 290,000 cardholders were classified as public transit commuters. Statistics such as average first boarding time and travel frequency of workdays during peak hours were obtained. Finally, the smartcard data were fused with bus location data to reveal the spatial distributions of the home and work locations of these public transit commuters, which could be utilized to improve public transit planning and operations.

Individual Travel Knowledge Graph-Based Public Transport Commuter Identification: A Mixed Data Learning Approach

“…Moreover, they proposed a Naive Bayesian classifier model to identify PT commuters. e results showed that the model can identify the objectives using smart card data without requiring travel regularity assumptions of PT commuters [20]. Bösehans and Walker utilized the centroid clustering algorithm and k-means procedure to cluster the staff and students; then, the main travel mode of staff commuters and student commuters was identified and analyzed [21].…”

Using Smart Card Data of Metro Passengers to Unveil the Urban Spatial Structure: A Case Study of Xi’an, China

“…In addition to taxi trajectory data and mobile phone data, smart card data are also recognized as a promising data source to provide insights on the identification of urban spatial structure [16,17]. It is because smart card data can provide rich and high-quality check-in records of public transport passengers [18][19][20][21][22], and mostly important, these passengers' riderships constitute a crucial part of urban spatial movements [21,23,24]. Compared with other data sources, smart card data are accessible with less cost, and the data are refined in spatial and temporal granularity [25].…”

“…Compared with other data sources, smart card data are accessible with less cost, and the data are refined in spatial and temporal granularity [25]. In addition, the coverage of smart card data is relatively wide both in space and in population [23]. Tang et al [26] proposed a clustering refinement approach to investigate the agglomeration pattern of passenger flows by using smart card data and then elaborated five clusters of metro stations to represent the underlying structure.…”

Artificial intelligence for improving public transport: a mapping study

“…;Jung and Sohn 2017;Kedia et al 2017;Sun and Yang 2018;Tang et al 2020;Xue et al 2014;Yu et al 2015;Zhang et al 2019;Shalit et al 2020). • Understanding/predicting the travellers' mode choice(Chapleau et al 2019;Ferrara et al 2019;Hagenauer and Helbich 2017;Lazar et al 2019;Liang et al 2019;Niklas et al 2020;Tu et al 2016;Victoriano et al 2020;Zhou et al 2019).…”