Li, Jieping

The rising prevalence of private cars in the developing world is causing serious congestion and pollution. In China, private cars started to emerge as an important travel mode in the past decade. Prospective research on the relationship between urban form and car ownership is relatively uncommon in the developing world, and China offers a unique study opportunity, given the tremendous increases in private cars and fast-paced urbanization over the past decade. This study investigates the influence of urban form on car ownership as well as the impact of other socioeconomic and demographic factors on private car ownership across megacities in China. Analysis was conducted through the use of data from 36 megacities and two household survey data sets collected in Beijing and the city of Chengdu, China. Ordinary least squares regression and discrete choice models were employed to execute the aggregate and disaggregate analysis of the urban form impact on private car ownership across cities. The statistical model results demonstrate that urban affluence, urban scale, and road infrastructure supply factors have significant positive effects on the city level of private car ownership across cities. Population density calculated at the subdistrict level, however, had a significant negative effect on private car ownership across cities. Households with private cars were found to prefer to live close to urban centers where amenities were readily available. The results provide evidence for urban planners and policy makers.

While transport modelers in developed countries are accustomed to working with relatively rich datasets including transport networks and land use data, such databases are rarely available in developing countries. However, developing countries such as China with its immense rate of economic growth are, arguably, most in need of demand models. The research addressed in this paper is how to develop mode choice models for planning and policy analysis when high quality level of service data are not available. The research makes use of a 1,001 household travel and activity survey from Chengdu collected by the China Project at Harvard University in 2005. Chengdu has an urban population of over 3 million and a GDP growth rate of over 20% per year. The survey contains a rich array of self-assessed information on available modes and accessibility and also includes a number of attitudinal questions. The approach taken here is to treat level of service as a latent (i.e., unobservable) variable. Measurement equations (from the structural equation model paradigm) are used to infer latent level of service, and these equations are integrated with the mode choice model. Our initial results indicate that models that do not correct for measurement error may significantly underestimate travelers' values of time. The methodological approach employed has potential for improving models estimated with higher quality network data, because it can correct for measurement error that exists, for example, in network-derived level of service variables.