Hierarchical Linear Model for Investigating Effect of Built Environment on Bus Transit
Publication: Journal of Urban Planning and Development
Volume 146, Issue 2
Abstract
Because proper urban planning and design can improve transit sharing, the impact of the built environment on transit use has attracted the attention of many researchers. Factors with different geographical scales such as individual characteristics, neighborhood built environment, and regional features can influence transit use. However, the nested structure of geographical scales has not been considered in previous research studies. By considering the nested geographic features, a bilevel hierarchical linear model (HLM) is established in this study to explore the combined influence of the surrounding built-environment factors at the neighborhood level and the socioeconomic variables at the regional level on bus transit trip rates. The model is implemented based on land-use data and bus smart card data of approximately 3 million records of 9 districts and more than 1,600 bus stops in Wuhan, China. The results present evident differences in the effect of the built environment on transit use in different spatial districts. The population and public transportation investment at the regional level are important factors leading to these differences and influence transit use. Based on the geographically nested features in the data, the proposed model reveals the impacts of the built environment on transit use and investigates the causality of the reported discrepancies. Furthermore, combined with the model results, optimization strategies for improving bus transit use are proposed for a region in Wuhan. The proposed method offers theoretical and technical support for transit-oriented urban development.
Get full access to this article
View all available purchase options and get full access to this article.
References
Antipova, A., F. Wang, and C. Wilmot. 2011. “Urban land uses, socio-demographic attributes and commuting: A multilevel modeling approach.” Appl. Geogr. 31 (3): 1010–1018.
Bagchi, M., and P. R. White. 2005. “The potential of public transport smart card data.” Transp. Policy 12 (5): 464–474.
Boarnet, M., and R. Crane. 2001. “The influence of land use on travel behavior: Specification and estimation strategies.” Transp. Res. Part A: Policy Pract. 35 (9): 823–845.
Boarnet, M. G., and S. Sarmiento. 1998. “Can land-use policy really affect travel behaviour? A study of the link between non-work travel and land-use characteristics.” Urban Stud. 35 (35): 1155–1169.
Cervero, R. 2002. “Built environments and mode choice: Toward a normative framework.” Transp. Res. Part D: Transp. Environ. 7 (4): 265–284.
Cervero, R., and K. Kockelman. 1997. “Travel demand and the 3Ds: Density, diversity, and design.” Transp. Res. Part D: Transp. Environ. 2 (3): 199–219.
Ding, C., S. Mishra, G. Lu, J. Yang, and C. Liu. 2017. “Influences of built environment characteristics and individual factors on commuting distance: A multilevel mixture hazard modeling approach.” Transp. Res. Part D: Transp. Environ. 51: 314–325.
Ewing, R., and R. Cervero. 2001. “Travel and the built environment: A synthesis.” Transp. Res. Rec. 1780 (1): 87–114.
Ewing, R., and R. Cervero. 2010. “Travel and the built environment: A meta-analysis.” J. Am. Plann. Assoc. 76 (3): 265–294.
Ewing, R., G. Tian, and T. Lyons. 2018. “Does compact development increase or reduce traffic congestion?” Cities 72: 94–101.
Frank, L., and G. Pivo. 1995. “Impacts of mixed use and density on utilization of three modes of travel: Single occupant vehicle, transit, and walking.” Transp. Res. Rec. 1466: 44–52.
Gschwender, A., M. Munizaga, and C. Simonetti. 2016. “Using smart card and GPS data for policy and planning: The case of Transantiago.” Res. Transp. Econ. 59: 242–249.
Handy, S. L., M. G. Boarnet, R. Ewing, and R. E. Killingsworth. 2002. “How the built environment affects physical activity: Views from urban planning.” Am. J. Preventive Med. 23: 64–73.
Handy, S. L., X. Cao, and P. Mokhtarian. 2005. “Correlation or causality between the built environment and travel behavior? Evidence from northern California.” Transp. Res. Part D: Transp. Environ. 10 (6): 427–444.
Hong, J., Q. Shen, and L. Zhang. 2014. “How do built-environment factors affect travel behavior? A spatial analysis at different geographic scales.” Transportation 41 (3): 419–440.
Huang, Z., Y. D. Wei, C. He, and H. Li. 2015. “Urban land expansion under economic transition in China: A multi-level modeling analysis.” Habitat Int. 47: 69–82.
James, G., D. Witten, T. Hastie, and R. Tibshirani. 2013. An introduction to statistical learning. New York: Springer.
Kim, J., J. Corcoran, and M. Papamanolis. 2017. “Route choice stickiness of public transport passengers: Measuring habitual bus ridership behaviour using smart card data.” Transp. Res. Part C: Emerging Technol. 83: 146–164.
Kockelman, K. 1997. “Travel behavior as function of accessibility, land use mixing, and land use balance: Evidence from San Francisco Bay area.” Transp. Res. Rec. 1607 (1607): 116–125.
Kusakabe, T., and Y. Asakura. 2014. “Behavioural data mining of transit smart card data: A data fusion approach.” Transp. Res. Part C: Emerging Technol. 46: 179–191.
Long, Y., and J. C. Thill. 2015. “Combining smart card data and household travel survey to analyze jobs–housing relationships in Beijing.” Comput. Environ. Urban Syst. 53: 19–35.
Ma, X., C. Liu, H. Wen, Y. Wang, and Y. J. Wu. 2017. “Understanding commuting patterns using transit smart card data.” J. Transp. Geogr. 58: 135–145.
Mokhtarian, P. L., X. Cao, and S. L. Handy. 2007. “Cross-sectional and quasi-panel explorations of the connection between the built environment and auto ownership.” Environ. Plann. A 39 (4): 830–847.
Pan, H. X., Q. Shen, and M. Zhang. 2009. “Influence of urban form on travel behaviour in four neighborhoods of shanghai.” Urban Stud. 46 (2): 275–294.
Pelletier, M. P., M. Trépanier, and C. Morency. 2011. “Smart card data use in public transit: A literature review.” Transp. Res. Part C: Emerging Technol. 19 (4): 557–568.
Stevens, M. R. 2017. “Does compact development make people drive less?” J. Am. Plann. Assoc. 83 (1): 7–18.
Tao, S., D. Rohde, and J. Corcoran. 2014. “Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap.” J. Transp. Geogr. 41 (41): 21–36.
Woltman, H., A. Feldstain, J. C. MacKay, and M. Rocchi. 2012. “An introduction to hierarchical linear modeling.” Tutorials Quant. Methods Psychol. 8: 52–69.
Yu, C., and Z. C. He. 2017. “Analysing the spatial-temporal characteristics of bus travel demand using the heat map.” J. Transp. Geogr. 58: 247–255.
Zhang, L., J. H. Hong, A. Nasri, and Q. Shen. 2012. “How built environment affects travel behavior: A comparative analysis of the connections between land use and vehicle miles traveled in US cities.” J. Transp. Land Use 5 (3): 40–52.
Information & Authors
Information
Published In
Journal of Urban Planning and Development
Volume 146 • Issue 2 • June 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Feb 21, 2019
Accepted: Oct 15, 2019
Published online: Mar 27, 2020
Published in print: Jun 1, 2020
Discussion open until: Aug 27, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.