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This study examines the effects of land use and street characteristics on household car ownership and use, based on a travel survey of 2540 households in 104 neighborhoods of Jinan, China. We performed two-step instrumental variable models, composed of a multinomial logistic model predicting the car ownership in the first step and a double-hurdle model predicting the car traveling distances in the second step with the use of car ownership as a mediating variable. It is found that parking supply, neighborhood permeability and street design influence car ownership. Most land use and street variables along dimensions of density, diversity and design, as well as the proximity to regional transport infrastructures, influence either the participation probability or the distance travelled by car. Overall, our study points to the importance of strategic urban planning favoring bus rapid transit development, mixed land uses, human friendly streets and restrictive parking to reduce car dependency in rapidly motorizing Chinese cities. [ABSTRACT FROM AUTHOR]

Rapidly increasing transportation energy use in China poses challenges to national energy security and the mitigation of greenhouse gas emissions. Meanwhile, the development of automobile oriented neighborhood structures, such as superblock housing, currently dominates urban expansion, and construction in Chinese cities. This research takes an empirical approach to understanding the relationship between neighborhood type and household travel energy use in Jinan, China, by examining nine neighborhoods that represent the four types of urban community commonly found in Chinese cities: traditional, grid, enclave, and superblock. After conducting a survey, we derive disaggregate household transport energy uses from the' self-reported weekly travel diaries. Comparative analysis and two-step instrumental variable models are employed. Results show that, all else being equal, households located in superblock neighborhoods consume more transportation energy than those in other neighborhood types, because such households tend to own more cars and travel longer distances. Proximity to transit corridors and greater distance from the city center are also associated with higher household transport energy use in these neighborhoods, although both impacts are minor, partially because of the offsetting effects of car ownership. Overall, the analysis suggests that, to help chart a more energy-efficient future in urban China, policymakers should (1) examine past neighborhood designs to find alternatives to the superblock, (2) focus on strategic infill development, (3) encourage greater use of bicycles and e-bikes as a substitute for larger motorized vehicles, (4) improve the efficiency of public transportation, and (5) consider ways to shape citizens' preferences for more energy-efficient modes of travel. [ABSTRACT FROM AUTHOR]

Abstract: This paper examines BRT station walk access patterns in rapidly urbanizing China and the relationship between bus rapid transit (BRT) station context and corridor type and the distance people will walk to access the system (i.e., catchment area). We hypothesize that certain contextual built environment features and station and right-of-way configurations will increase the walk-access catchment area; that is, that urban design influences users’ willingness to walk to BRT. We base our analysis on 1233 user surveys, conducted at 19 BRT stations along three existing (as of summer 2009) BRT corridors in the city of Jinan. Ordinary least squares regression is applied to estimate the relationship between walk access distances and aggregate station- and corridor-area characteristics, controlling for individual- and trip-specific attributes. The results suggest that people walk farther to BRT stations when the walking environment has certain features (median transit-way station location, shaded corridors, busy and interesting). Trip and trip maker characteristics play a relatively minor role in defining BRT walk access distance. Implications include the need for flexible transit station catchment area definitions in identifying transit-oriented development opportunities and estimating system demand. [Copyright &y& Elsevier]

This paper presents a direct modelling approach to evaluate household travel energy consumption and CO 2 emissions based on the spatial form of neighborhoods and streets. It integrates one multinomial logit model and four double hurdle models to predict travel outcomes: vehicle ownership portfolio choice, car travel distance, bus travel distance, motorcycle travel distance and ebike travel distance. The energy consumption and CO 2 emissions are then estimated using these travel outcomes. A full application of the modelling approach is demonstrated through a pilot project in Jinan, China. A holdout validation is also performed to address the over-fitting problem of models calibrated from the training set data. Results show that the proposed approach is operational and appropriate for sketch planning applications to promote clean energy and low carbon city planning in urbanizing China. • A direct modelling approach to evaluate household travel energy consumption and CO 2 emissions is presented. • The approach assesses the impact of the spatial form of neighborhoods and streets. • A holdout validation is performed to address the over-fitting problem of calibrated models. [ABSTRACT FROM AUTHOR]

Residential self-selection (RSS) confounds the connection between the built environment and travel behavior. Existing studies have used endogenous switching regression models to quantify the proportions of the built environment itself and RSS in the observed behavioral difference between different environments. However, the models are sensitive to model specification and assume pre-defined (mostly linear) relationships among variables. This study applies a double machine learning approach to fill the gap. The empirical context is to jointly model residential choice of Bus Rapid Transit (BRT) neighborhoods and weekly driving distance of household owning cars in Jinan, China. The results showed that the RSS effect accounts for about 40% of the observed difference in driving distance between the households living inside and outside of BRT neighborhoods. This results also emphasizes the necessity of relaxing the linearity assumption in the research on the relationships among the built environment, RSS, and travel behavior. [ABSTRACT FROM AUTHOR]