Measuring job-housing balance is an important part of job-housing related research, and the dataset applied in previous researches is expanded from survey and census data to LBS data. However, current research lacks comparative studies between different data sources. Beijing urban area is taken as an example to measure and analyze job-housing balance spatial-temporally from different aspects, using different kinds of LBS data, which including heatmap data, Point-of-interest data and Weibo-checkin data. This could provide decision-making reference to improve the job-housing balance. The authors compare the differences in the results of LBS data with the traditional population and economic census data, discusses the causes of the differences, and provides suggestions for further improving the research of LBS data in job-housing relations.

Based on the questionnaire of 10 cities and towns in China, this research has found that there are significant differences between urban and rural area in China through the data analyzing. The survey mainly includes five aspects: energy consumption for heating and cooling, lighting energy consumption, household electricity appliances’ energy consumption, and the energy consumption for cooking. The findings show that the main energy resource are electricity, natural gas and coal and the main energy consuming activities are heating, cooking and household electricity appliances’ consumption. In addition, the results of survey reflect the difference in energy source and consumption structure between urban and rural area. Generally, the per capita energy consumption in urban is 3.2 times of rural life. Gas and electricity are the main energy source in urban area while electricity power and coal have a high proportion in rural residents. The survey results provide important reference for China to implement energy saving policy.

This study targets on the practical process of travel survey in Qianmen, Beijing and examines the problems derived from the survey. Their characteristics and the reasons of being generated are stated. The paper focuses on survey organization and its institutional obstacles, the survey design, survey sampling techniques, the choice and training of surveyors and the survey timing. Based on the theoretical researches, the advices towards the innovation of travel survey methods are proposed.

The authors proposed a new travel mode choice model to overcome the limitation of traditional logit model. Combining generalized extreme value model and latent class model, the authors present a modeling methodology capable of accounting for individual preference heterogeneity and correlation across choice alterna- tives. Travel cost, travel time, parking fee, and waiting time are defined as utility variables for mode choice, while individual income, travel purpose, and travel distance are selected as variables of segment membership function. This model can depict the correlation among choice alternatives and individual preference heterogeneity simultaneously. Using Beijing traffic survey data of 2005, the model parameters are estimated. Estimation results show that the latent class paired nested logit model outperforms the traditional models. Most travelers are cost-sensitive to travel modes, and thus strategies that reduce the travel cost can be more effective than reducing the travel time.