Speaker
Description
Mobility challenges faced by people with disabilities are becoming increasingly prominent in many cities worldwide, particularly in developing countries. Promoting the travel behavior of people with disabilities plays a decisive role in maintaining social equity and shaping inclusive cities. While many governments have implemented measures to create accessible cities, there remains a gap in understanding the unique travel demands and behavioral patterns of people with disabilities within public transportation systems. Metro networks, known for their safety, comfort, and convenience, have become a preferred mode of transport for this demographic. However, recent studies often focus on passenger volumes at individual stations, neglecting the flow networks between metro stations across different regions. Few studies adopt a network perspective to reveal the spatiotemporal heterogeneity of metro travel among people with disabilities and the formation of distinct travel communities, hindering a comprehensive understanding of the diverse factors influencing these patterns.
To address this gap, this study utilizes smart card data from Nanjing to construct a metro travel network for people with disabilities and analyze its spatiotemporal distribution characteristics. Using community detection algorithms, we identify travel communities on weekdays and weekends and employ Tobit regression models to examine how built environment factors influence metro travel flows across different contexts. The results show that people with disabilities exhibit an earlier peak travel period and more localized travel activity spaces on weekdays. Spatially, their metro travel communities are concentrated in core urban areas, such as old towns and central districts, indicating limited mobility and interaction intensity in suburban areas. Regarding the mechanisms influencing metro travel, the study identifies three types of built environment factors shaping metro travel: facilitators (e.g., metro accessibility, population density), inhibitors (e.g., lack of accessible facilities), and factors with variable effects across travel communities (e.g., street density, the number of healthcare facilities, and recreational facilities).
This study highlights the importance of embedding accessibility and inclusivity into urban planning. By offering a nuanced understanding of the metro travel patterns and networks of people with disabilities, it provides actionable insights to better address their mobility needs, enhance accessible infrastructure, and advance equitable transportation systems.
References
Li, S., Lyu, D., Huang, G., Zhang, X., Gao, F., Chen, Y., & Liu, X. (2020). Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China. Journal of Transport Geography, 82, 102631. https://doi.org/10.1016/j.jtrangeo.2019.102631
Liu, X., Pei, T., Wang, X., Liu, T., Fang, Z., Jiang, L., Jiang, J., Yan, X., Wu, M., Peng, Y., Ge, D., Gao, X., Song, C., & Chen, J. (2024). Travel flow patterns of diverse population groups and influencing built environment factors: A case study of Beijing. Cities, 151, 105096. https://doi.org/10.1016/j.cities.2024.105096
Wei, J., Kan, Z., Kwan, M.-P., Liu, D., Su, L., & Chen, Y. (2024). Uncovering travel communities among older and younger adults using smart card data. Applied Geography, 173, 103453. https://doi.org/10.1016/j.apgeog.2024.103453
Yang, H., Peng, J., Lu, Y., Wang, J., & Yan, X. (2024). Nonlinear impact of built environment on people with disabilities’ metro use behavior. Applied Geography, 169, 103323. https://doi.org/10.1016/j.apgeog.2024.103323
| Keywords | People with disability; Travel Community; Smart card data; Metro ridership |
|---|---|
| Best Congress Paper Award | Yes |
