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Despite occupying essential roles in healthcare, warehouses, and manufacturing, night shift workers remain underserved by transportation systems designed around standard daytime schedules. Using the Baltimore Metropolitan Area as a case study, this study provides an in-depth examination of night shift workers’ travel behavior and commute wellbeing. Survey data on commute choices, commute durations, travel costs, and perceived perspectives were collected from workers employed in night shift-based occupations. Structural equation modeling was then used to examine the relationships between travel behavior and perceived inconvenience by constructing a latent variable of commute burden. The results show that longer commute times and reliance on public transit are significant indicators of commute burden among this population. To further investigate why commute burden is associated with public transit, we conducted spatial visualizations and K-means clustering based on origin-destination nighttime trips derived from anonymized mobile phone data. Land use maps and nighttime public transit networks were referenced to identify travel flows and mobility patterns between high-flow zones, revealing the need to expand nighttime transit services in those areas. These findings reflect limited transit accessibility affecting night shift workers and offer actionable insights for nighttime transit planning and public service improvements in similar metropolitan contexts.
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Estimating road traffic impacts of commute mode shifts
This work considers the sensitivity of commute travel times in US metro areas due to potential changes in commute patterns, for example caused by events such as pandemics. Permanent shifts away from transit and carpooling can add vehicles to congested road networks, increasing travel times. Growth in the number of workers who avoid commuting and work from home instead can offset travel time increases. To estimate these potential impacts, 6-9 years of American Community Survey commute data for 118 metropolitan statistical areas are investigated. For 74 of the metro areas, the average commute travel time is shown to be explainable using only the number of passenger vehicles used for commuting. A universal Bureau of Public Roads model characterizes the sensitivity of each metro area with respect to additional vehicles. The resulting models are then used to determine the change in average travel time for each metro area in scenarios when 25% or 50% of transit and carpool users switch to single occupancy vehicles. Under a 25% mode shift, areas such as San Francisco and New York that are already congested and have high transit ridership may experience round trip travel time increases of 12 minutes (New York) to 20 minutes (San Francisco), costing individual commuters $1065 and $1601 annually in lost time. The travel time increases and corresponding costs can be avoided with an increase in working from home. The main contribution of this work is to provide a model to quantify the potential increase in commute travel times under various behavior changes, that can aid policy making for more efficient commuting.
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- Award ID(s):
- 2033580
- PAR ID:
- 10437407
- Editor(s):
- Jin, Sheng
- Date Published:
- Journal Name:
- PLOS ONE
- Volume:
- 18
- Issue:
- 1
- ISSN:
- 1932-6203
- Page Range / eLocation ID:
- e0279738
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1371/journal.pone.0279738
