Citation

Lim S, Ahn H, Shin S, Lee D, Kim YH. Transp. Res. A Policy Pract. 2024; 181: e104002.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.tra.2024.104002

PMID

unavailable

Abstract

Designing public transit services that meet the needs of night shift workers requires understanding their commuting patterns. However, traditional survey methods have faced challenges in contacting and interviewing night shift workers with changing work-sleep schedules. This study aims to investigate night shift workers' commuting patterns by identifying night shift workers with heterogeneous working patterns in passive mobility data. First, we identify workers and their workplaces from the mobility data using a rule-based method. Subsequently, we cluster individual workers' workplace-staying records using DBSCAN to find regular working patterns and segment workers with diverse working patterns. We applied the method to the smart card data of Seoul, South Korea, and identified 37,448 night shift workers with six different working patterns. The proportion of night shift workers among presumed workers was 8.9%, which was slightly higher than the 7.2% reported in a national survey. Workers who exclusively worked at nighttime worked near night markets, while three-shift workers' workplaces mainly appeared near areas with large general hospitals with emergency care centers. The proportion, regular working patterns, and workplace location distribution of the identified night shift workers were generally consistent with the existing survey-based knowledge, suggesting that night shift workers were accurately discovered from the mobility data. The finding suggested that policies to extend transit service time were beneficial for three-shift and 24-hour duty workers. With the proposed method, night shift workers' regular and essential mobility needs can be identified and provided to cities' transit authorities to help assess and improve transit services for these workers.

Language: en

Keywords

Commuting pattern; Data mining; DBSCAN; Night shift worker; Passive mobility data; Transportation equity; Travel behavior