Car ownership and commuting mode of the “original” residents in a high-density city center: A case study in Shanghai
Keywords:Vehicle Ownership, Commuting Behavior, City Center Residents, TOD
As a result of rapid urbanization and motorization in China, numerous mega-cities have emerged, and large numbers of people live and work in the city centers. Consequently, developing a public transport-oriented urban structure and promoting sustainable development are major planning strategies for the country. To understand the impact of rail transit on motorization in a high-density city center, we conduct a household travel survey in three neighborhoods around metro stations in the central area of Shanghai. We examine the car buying and commuting behavior of those Shanghai “original” residents who lived there when the city began growing, engulfing them in the center.
Studies have shown that 40 percent of commuters in the city center commute outward, following a virtually reversed commute pattern, and the factors significantly affecting their car purchasing choice include their attitude toward cars and transit, household incomes, ownership of the apartments they live in, and the distance between family members’ workplaces and nearest metro stations. Despite easy access to the metro from their home in the city center, those who purchase their apartment units also likely own a car, while those who rent their apartment units are less likely to own a car; however, these odds are still higher than for those who live in an apartment unit inherited from their relatives or provided by their company. In the city center, if a family owns a car, then that car would almost certainly be used for daily commuting.
A multinomial logistic model is applied to examine the factors influencing the tendency for using cars. The results show that people’s choices of commuting by alternative modes rather than cars are also shaped by their attitude toward public transportation, but other factors can also subtly change people’s commuting behavior under certain conditions. The commuting distance discourages people from walking and taking buses (but not metro). As the egress distance to the workplace increases, the metro becomes less appealing than cars. Mixed land use encourages people to walk or take buses instead of driving. Older people prefer riding buses and walking to driving, and female respondents tend to prefer walking, cycling, and riding the metro to driving compared to male respondents. These findings contribute to understanding the behavior of people who are familiar with public transportation and how to encourage them to switch from driving cars to alternative transport modes.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
Beige, S., & Axhausen, K. W. (2017). The dynamics of commuting over the life course: Swiss experiences. Transportation Research Part A: Policy and Practice, 104, 179–194.
Ben-Dor, G., Ben-Elia, E., & Benenson, I. (2018). Assessing the impacts of dedicated bus lanes on urban traffic congestion and modal split with an agent-based model. Procedia Computer Science, 130, 824–829.
Bwire, H., & Zengo, E. (2020). Comparison of efficiency between public and private transport modes using excess commuting: An experience in Dar es Salaam. Journal of Transport Geography, 82, 102616.
Clark, B., Lyons, G., & Chatterjee, K. (2016). Understanding the process that gives rise to household car ownership level changes. Journal of Transport Geography, 55, 110–120.
Crane, R. (2007). Is there a quiet revolution in women's travel? Revisiting the gender gap in commuting. Journal of the American Planning Association, 7(3), 298–316.
Dai, D., Zhou, C., & Ye, C. (2016). Spatial-temporal characteristics and factors influencing commuting activities of middle-class residents in Guangzhou City, China. Chinese Geographical Science, 26(3), 410–428.
Dargay, J. M. (2001). The effect of income on car ownership: Evidence of asymmetry. Transportation Research Part A: Policy and Practice, 35(9), 807–821.
Ding, C., Wang, Y., Tang, T., Mishra, S., Liu, C. (2018). Joint analysis of the spatial impacts of built environment on car ownership and travel mode choice. Transportation Research Part D: Transport and Environment, 60, 28-40.
FORWARD Business Information Co. (n.d.) Report of market demand and investment strategy planning analysis on China urban rail transit information industry (2018-2023).
Ewing, R., & Cervero, R. (2010). Travel and the built environment, a meta-analysis. Journal of the American Planning Association, 76(3), 265–294.
Ewing, R., Tian, G., Goates, J. P., Zhang, M., Greenwald, M. J., Joyce, A., … Greene, W. (2015). Varying influences of the built environment on household travel in 15 diverse regions of the United States. Urban Studies, 52(13), 2330–2348.
Feng, J., & Dijst, M. (2017). Changing travel behavior in urban China: Evidence from Nanjing 2008–2011. Transport Policy, 53, 1–10.
Friman, M., Fujii, S., & Ettema, D. (2013). Psychometric analysis of the satisfaction with travel scale. Transportation Research Part A: Policy and Practice, 48, 132–145.
Guerra, E., Caudillo, C., Monkkonen, P., & Montejano, J. (2018). Urban form, transit supply, and travel behavior in Latin America: Evidence from Mexico's 100 largest urban areas. Transport Policy, 69, 98–105.
He, S. Y., & Thøgersen, J. (2017). The impact of attitudes and perceptions on travel mode choice and car ownership in a Chinese megacity: The case of Guangzhou. Research in Transportation Economics, 62, 57-67.
Hu, L., & Schneider, R. J. (2017). Different ways to get to the same workplace: How does workplace location relate to commuting by different income groups? Transport Policy, 59, 106–115.
Jiangping, Z., Chun, Z., Xiaojian, C., Wei, H., & Peng, Y. (2014). Has the legacy of Danwei persisted in transformations? The jobs-housing balance and commuting efficiency in Xi’an. Journal of Transport Geography, 40, 64–76.
Johnston-Anumonwo, I. (1992). The influence of household type on gender differences in work trip distance. Professional Geographer, 44(4), 161–169.
Kamruzzaman, Md., Shatu, F., Hine, J., & Turrell, G. (2015). Commuting mode choice in transit-oriented development: Disentangling the effects of competitive neighborhoods, travel attitudes, and self-selection. Transport Policy, 42, 187–196.
Kobayashi, K., & Do, M. (2005). The informational impacts of congestion tolls upon route traffic demands. Transportation Research Part A: Policy and Practice, 39(7), 651–670.
Korsu, E., & Néchet, F. (2017). Would fewer people drive to work in a city without excess commuting? Explorations in the Paris metropolitan area. Transportation Research Part A: Policy and Practice, 95, 259–274.
Morozova L., Stepanenkova L., & Malashkin A. (2016). Integration of a commuter rail in the transport system of the city. In M. Blinkin & E. Koncheva (Eds.), Transport systems of Russian cities (Transportation research, economics and policy series). Cham, Switzerland: Springer.
Redmond, L. S., & Mokhtarian, P. L. (2001). The positive utility of the commute: Modeling ideal commute time and relative desired commute amount. Transportation, 28(2),179–205.
Shen, Q., Chen, P., & Pan, H. (2016). Factors affecting car ownership and mode choice in rail transit-supported suburbs of a large Chinese city. Transportation Research Part A, 94, 31–44.
Stutzer, A., & Frey, B. S. (2008). Stress that doesn't pay: The commuting paradox. Journal of Economics, 110(2), 339–366.
Vale, D. S. (2013). Does commuting time tolerance impede sustainable urban mobility? Analyzing the impacts on commuting behavior as a result of workplace relocation to a mixed-use center in Lisbon. Journal of Transport Geography, 32, 38–48.
Xiaopei, Y. (1994). The satellite towns of metropolis Guangzhou: Evolution, inherent links with the central city and tendencies —a case study of Renhe town. Chinese Geographical Science, 4(1), 55–65.
Xu, Y., Zhang, Q., Zheng, S., Zhu, G. (2018). House age, price and rent: Implications from land-structure decomposition. The Journal of Real Estate Finance and Economics, 56(2), 303–324.
Yang, X., & Day, J. E. (2017). Commute responses to employment decentralization: Anticipated versus actual mode choice behaviors of new town employees in Kunming, China. Transportation Research Part D: Transport and Environment, 52(B), 454–470.
Zhang, C., & Man, J. (2015). Examining job accessibility of the urban poor by urban metro and bus: A case study of Beijing. Urban Rail Transit, 1(4), 183–193.
Zhang, P., Zhou, J., & Zhang, T. (2017). Quantifying and visualizing jobs-housing balance with big data: A case study of Shanghai. Cities, 66, 10-22.
Zhang, M., Chen, T., & Li, J. (2018). Impact of commuting time to the metropolitan periphery population activity: A case study in Huaqiao, a typical periphery community of Shanghai. In Innovation driven and smart development: Proceedings of Annual National Planning Conference. China Association of City Planning, Urban Transportation Planning Academic Committee, pp. 3533–3544 (in Chinese).
Zhao, P., Lü, B., & deRoo, G. (2011). Impact of the jobs-housing balance on urban commuting in Beijing in the transformation era. Journal of Transport Geography, 19(1), 59–69.
Zhu, J., & Fan, Y. (2018). Commute happiness in Xi’an, China: Effects of commute mode, duration, and frequency. Travel Behavior and Society, 11, 43–51.
How to Cite
Copyright (c) 2021 Tao Chen, Haixiao Pan, Yanbo Ge
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with JTLU agree to the following terms: 1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution-Noncommercial License 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. 2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. 3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.