Understanding the mobility patterns of Mass Rapid Transit ( MRT ) passengers amid Covid ‐19 in Singapore using smart card data
Bibliographic Data
| ID | 20153151 |
|---|---|
| Authors | Mingjia Chen (Department of Geography National University of Singapore Singapore), Yingwei Yan (0000-0002-3487-9792, Department of Geography National University of Singapore Singapore, corresponding author), Chen-Chieh Feng (0000-0003-0410-714X, National University of Singapore), Chen‐chieh Feng (Department of Geography National University of Singapore Singapore), Shuting Chen (0000-0002-8899-1931, Department of Architecture National University of Singapore Singapore), Jing Wang (0000-0002-7594-8539, Department of Geography National University of Singapore Singapore), Mengbi Ye (Department of Architecture National University of Singapore Singapore) |
| Year | 2023 |
| Volume | 44 |
| Issue | 3 |
| Pages | 414-437 |
| Publication date | 2023-09-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Singapore Journal of Tropical Geography (JOURNAL) |
| Journal identifiers | ISSN: 0129-7619 • E-ISSN: 1467-9493 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/sjtg.12509 |
| OpenAlex | W4386330133 |
| Language | EN |
| References cited | 37 |
The Mass Rapid Transit (MRT) is one of the major modes of public transportation in Singapore. Understanding the mobility patterns of MRT passengers has implications for improving transportation efficiency. As a city‐state with a high population density, Singapore provides a representation of balanced urban dynamics that informs smart urban planning. In this paper, we investigated and visualized (using both static maps and dynamic web map applications) the spatiotemporal characteristics of Singapore's MRT commuting patterns before the COVID‐19 pandemic (January 2020) and during the first outbreak (May 2020) and the Omicron wave of the pandemic (February 2022), using MRT smart card data. We also investigated the relationship between the passenger flows of individual MRT stations and the nearby land use types. Our results showed that the spatial patterns of Singapore's MRT commuters match the polycentric urban structure. In addition to central areas, several regional centres were identified as passenger hotspots in multiple time periods. Furthermore, during the outbreak of the pandemic, especially in the period of the ‘circuit breaker’, there was a major decline in MRT passenger flows and a decrease in average MRT commuting distances during weekend/holiday peak hours. Lastly, correlations between passenger flows of MRT stations and the proportion of nearby land use types have been identified
Cartography · Computer security · Coronavirus disease 2019 (COVID-19) · Geography · Outbreak · Pandemic · Population · Public transport · Smart card · Sociology · Transit (satellite) · Transport engineering · Travel behavior · Computer Science · Demography · Engineering · Human Mobility and Location-Based Analysis · Transportation Planning and Optimization · Urban Transport and Accessibility
Rail-based transit-oriented development
Application of geographically weighted regression to the direct forecasting of transit ridership at station-level
Half-Mile Circle
Simulating the urban spatial structure with spatial interaction
Polycentric urban regions
Probabilistic model for destination inference and travel pattern mining from smart card data
Understanding commuting patterns using transit smart card data
Day-to-day variation in excess commuting
The ridership performance of the built environment for BRT systems
Impacts of land use and amenities on public transport use, urban planning and design
What influences Metro station ridership in China? Insights from Nanjing
Changes in local travel behaviour before and during the Covid-19 pandemic in Hong Kong
Featured Graphic. Visualizing Commuting in Singapore
| Citation velocity | historical |
|---|---|
| Highly cited | No |