Understanding the impact of urban form attributes on household vehicle ownership and choice in metro Manila: Modeling, simulation, and application
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Published:
Sep 12, 2019
Keywords:
Multinomial logit model Household vehicle ownership Accessibility Public transport CO2 emission Metro Manila
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Abstract
Projected economic growth is expected to further increase vehicle ownership among households in Metropolitan Manila. This increase is likely to translate to higher energy requirements, elevated greenhouse gas emissions and air pollution, as well as a worsening of traffic congestion. A multinomial logit-based household vehicle ownership model was developed taking into account household characteristics and urban form peculiarities that are hypothesized to affect the level of vehicle ownership among households. The model utilized data gathered from a survey of 2,300 households from various areas of Metropolitan Manila. Results indicated that flooding susceptibility of communities does not affect vehicle ownership and type among its residents. Higher public transport density and closer proximity to essential facilities and services were found to be strong determinants that discourage vehicle ownership. Higher population density, contrary to findings in most studies, tends to reinforce vehicle ownership due to the inadequacy of public transport service, especially in crowded areas. The model was used to simulate “what if” shares of levels of vehicle ownership and apply the model under scenarios of 1) access to essential facilities, 2) improved road public transport line density, and 3) their combination. The results indicated that these interventions, relative to the baseline scenario, could respectively reduce vehicles owned by 26.63%, 35.02%, and 59.61% among the households surveyed and CO2 emission by 1.33 million tonnes, 1.63 million tonnes, and 2.69 million tonnes.
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Rith, M., Abad, R. P., Fillone, A. M., Doi, K., & Biona, J. B. M. (2019). Understanding the impact of urban form attributes on household vehicle ownership and choice in metro Manila: Modeling, simulation, and application. Engineering and Applied Science Research, 46(3), 238–247. Retrieved from https://ph01.tci-thaijo.org/index.php/easr/article/view/184437
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ORIGINAL RESEARCH
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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[15] Long S, Klungboonkrong P, Chindaprasirt P. Impacts of urban transit system development on modal shift and greenhouse gas (GHG) emission reduction: A Khon Kaen, Thailand case study. Eng Appl Sci Res. 2018;45(1):8-16.
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[19] Liu Y, Tremblay J, Cirillo C. An integrated model for discrete and continuous decisions with application to vehicle ownership, type and usage choices. Transport Res Pol Pract. 2014;69(2014):315-28.
[20] Brownstone D, Fang HA. A vehicle ownership and utilization choice model with endogenous residential density. J Transp Land Use. 2014;7(2):135-51.
[21] Bhat CR, Sen S, Eluru N. The impact of demographics, built environment attributes, vehicle characteristics, and gasoline prices on household vehicle holdings and use. Transport Res B-Meth. 2009;43(2009):1-18.
[22] Spissu E, Pinjari AR, Pendyala RM, Bhat CR. A copula-based joint multinomial discrete–continuous model of vehicle type choice and miles of travel. Transport. 2009;36(2009):403-22.
[23] Potoglou D, Kanaroglou PS. Modelling car ownership in urban areas: a case study of Hamilton, Canada. J Transport Geogr. 2008;16(2008):42-54.
[24] Feng Y, Fullerton D, Gan L. Vehicle choices, miles driven, and pollution policies. J Regul Econ. 2013;44(2013):4-29.
[25] West SE. Distributional effects of alternative vehicle pollution control policies. J Publ Econ. 2004;88(2004):735-57.
[26] Magno-Ballesteros M. Land use planning in Metro Manila and the urban fringe: implications on the land and real estate market. Makati City: Philippine Institute for Deveopment Studies (PIDS); 2000.
[27] Hickman R, Lopez N, Cao M, Lira BM, Biona JB. “I Drive outside of Peak Time to Avoid Traffic Jams-Public Transport Is Not Attractive Here” Challenging Discourses on Travel to the University Campus in Manila. Sustainability. 2018;10:1-21.
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[32] Villaraza C. Multi-criteria accessibility index development [dissertation]. United Kingdom: The Univesity of Leeds; 2018.
[33] MUCEP. The project for capacity development on transportation planning and database management in the republic of the Philippines [Internet]. 2015 [Cited 2018 Sept 1]. Available from: https://open_jicareport.jica.go.jp/pdf/12247623.pdf.
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[39] Train K. Discrete choice methods with simulation. New York: Cambridge University Press; 2003.
[40] Y. Croissant. Estimation of multinomial logit models in R: The mlogit package [Internet]. 2011 [Cited 2018 Feb 1]. Available from: https:// citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.303.7401&rep=rep1&type=pdf.
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[42] Fang HA. A discrete-continuous model of households' vehicle choice and usage, with an application to the effects of residential density. Transport Res B-Meth. 2008;42(2008):736-58.
[43] Wang X, Shao C, Yin C, Zhuge C. Exploring the influence of built environment on car ownership and use with a spatial multilevel model: A case study of Changchun, China. Int J Environ Res Publ Health. 2018;15(9):1-14.
[44] Philippine Statistics Authority. Family Income and Expenditure Survey (FIES) 2017 download area [Internet]. 2015 [cited 2019 Apr 15]. Available from: https://psa.gov.ph/iuse/fies-2017.
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