@article {47456,
	title = {Travel demand models in the developing world: Correcting for measurement errors},
	journal = {Transportation Letters},
	volume = {2},
	year = {2010},
	note = {<p>This paper is based on data from the Project{\textquoteright}s household survey in Chengdu, Sichuan.</p>},
	pages = {231-243},
	abstract = {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{\textquoteright} 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.},
	url = {https://www.researchgate.net/publication/228644124_Travel_demand_models_in_the_developing_world_Correcting_for_measurement_errors},
	author = {Walker, Joan L and Li, Jieping and Srinivasan, Sumeeta and Bolduc, Denis}
}
