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Urban Transport and Land Use Planning: A Synthesis of Global Knowledge, Volume Nine in the Advances in Transport Policy and Planning series assesses practices and policies from around the world. Chapters in this updated release include TOD and travel behavior research: A bibliographical review, Mass transit investments and land use in Latin America: A review of recent developments and research findings, TODness and its impacts on TOD performance, Corridor and networked TODs: Concept and planning support tools, Rail-centered accessibility: Concept, policy, and practice, Smart growth and travel behavior: A synthesis, Advances in integrated land use transport modeling, and much more. Other sections cover Residential self-selection in the relationship between the built environment and travel behavior: a literature review and research agenda, Threshold and synergistic effects in land use-travel research, Parking requirements: How land use policy acts as transport policy, The shifting coalition for transportation/land-use policy reform, and Compact urban development in Norway: Spatial changes and underlying policies. Provides the authority and expertise of leading contributors from an international board of authors Presents the latest release in the Advances in Transport Policy and Planning series

Deep frying is one of the most popular culinary processes, which induces oils generating a large number of polar compounds (PC) with complex structure, resulting in the difficulty of isolation and identification. In this review, we aimed to discuss the components of PC and then summary highly selective and highly sensitive approaches to analysis PC. It is found that infrared is the most convenient method for qualitative analysis, and chromatography is suitable for quantitative analysis, and electron spin resonance is applicable for mechanism analysis. In the future, sophisticated forecast technologies and comprehensive analytical methods will characterize the development trends in PC analysis, which will be beneficial for a full understanding of deep-frying oil food safety and quality control.

The digestion behavior of epoxy triglyceride, the main cytotoxic product of deep-frying oil, remains unknown, which may affect its biosafety. In this study, epoxy triglyceride (EGT) and triglyceride (GT) were used to reveal the effect of epoxy group on digestion. Digestibility rate analysis showed that the free fatty acids release rate of EGT was slower. To clarify this phenomenon, binding ability with salt ions in digestive juice and particle size were also been studied. Cluster size analysis indicated that epoxy group increased triglyceride particle size, resulting in smaller contact area between EGT and lipase. Interface behaviors displayed EGT decreased binding ability with salt ions in digestive juice. Spectroscopic analysis showed EGT caused the red shift of lipase peak, indicating that epoxy group changed lipase structure. Molecular dynamics simulation suggested EGT leads to loosen lipase structure. In conclusion, this study highlights that epoxy group could weaken the triglyceride digestion.

Deep frying is one of the most popular culinary processes, which induces oils generating a large number of polar compounds (PC) with complex structure, resulting in the difficulty of isolation and identification. In this review, we aimed to discuss the components of PC and then summary highly selective and highly sensitive approaches to analysis PC. It is found that infrared is the most convenient method for qualitative analysis, and chromatography is suitable for quantitative analysis, and electron spin resonance is applicable for mechanism analysis. In the future, sophisticated forecast technologies and comprehensive analytical methods will characterize the development trends in PC analysis, which will be beneficial for a full understanding of deep-frying oil food safety and quality control.

The digestion behavior of epoxy triglyceride, the main cytotoxic product of deep-frying oil, remains unknown, which may affect its biosafety. In this study, epoxy triglyceride (EGT) and triglyceride (GT) were used to reveal the effect of epoxy group on digestion. Digestibility rate analysis showed that the free fatty acids release rate of EGT was slower. To clarify this phenomenon, binding ability with salt ions in digestive juice and particle size were also been studied. Cluster size analysis indicated that epoxy group increased triglyceride particle size, resulting in smaller contact area between EGT and lipase. Interface behaviors displayed EGT decreased binding ability with salt ions in digestive juice. Spectroscopic analysis showed EGT caused the red shift of lipase peak, indicating that epoxy group changed lipase structure. Molecular dynamics simulation suggested EGT leads to loosen lipase structure. In conclusion, this study highlights that epoxy group could weaken the triglyceride digestion.

The study on Magnetocardiography imaging of living rats using NV centers in Diamond, The 4th International Forum on Quantum Metrology and Sensing

Learning-based 3D shape segmentation is usually formulated as a semantic labeling problem, assuming that all parts of training shapes are annotated with a given set of tags. This assumption, however, is impractical for learning fine-grained segmentation. Although most off-the-shelf CAD models are, by construction, composed of fine-grained parts, they usually miss semantic tags and labeling those fine-grained parts is extremely tedious. We approach the problem with deep clustering, where the key idea is to learn part priors from a shape dataset with fine-grained segmentation but no part labels. Given point sampled 3D shapes, we model the clustering priors of points with a similarity matrix and achieve part segmentation through minimizing a novel low rank loss. To handle highly densely sampled point sets, we adopt a divide-and-conquer strategy. We partition the large point set into a number of blocks. Each block is segmented using a deep-clustering-based part prior network trained in a category-agnostic manner. We then train a graph convolution network to merge the segments of all blocks to form the final segmentation result. Our method is evaluated with a challenging benchmark of fine-grained segmentation, showing state-of-the-art performance.

Part mobility analysis is a significant aspect required to achieve a functional understanding of 3D objects. It would be natural to obtain part mobility from the continuous part motion of 3D objects. In this study, we introduce a self-supervised method for segmenting motion parts and predicting their motion attributes from a point cloud sequence representing a dynamic object. To sufficiently utilize spatiotemporal information from the point cloud sequence, we generate trajectories by using correlations among successive frames of the sequence instead of directly processing the point clouds. We propose a novel neural network architecture called PointRNN to learn feature representations of trajectories along with their part rigid motions. We evaluate our method on various tasks including motion part segmentation, motion axis prediction and motion range estimation. The results demonstrate that our method outperforms previous techniques on both synthetic and real datasets. Moreover, our method has the ability to generalize to new and unseen objects. It is important to emphasize that it is not required to know any prior shape structure, prior shape category information, or shape orientation. To the best of our knowledge, this is the first study on deep learning to extract part mobility from point cloud sequence of a dynamic object.

We interview 24 marketing professors to ask how they got the ideas for 64 of their papers. More than three-quarters of the papers were inspired by holes in the literature, by a “stylized fact” that the current literature cannot explain, or by an interaction with a manager. The rest fall into several smaller categories that to a large extent can be seen as special cases of the three big ones. We describe how papers from each of the three big categories help move the literature forward. We also illustrate the range of situations contained in each category by way of several examples. Among the authors we interview, most do not use a single source. As these authors become more senior, managerial contacts play an increasing role, while the balance between literature and stylized facts appears to be unchanged.

Li Ma,1 Xinyu Cao,2 Xiaotong Ye,2 Jianping Ye,2 Yongning Sun1,3 1Department of Traditional Chinese Medicine, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200233, People’s Republic of China; 2Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 201306, People’s Republic of China; 3Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200071, People’s Republic of ChinaCorrespondence: Yongning Sun; Jianping Ye Tel +86-18930177579; +86-13818929364Email ynsun2002@126.com; yejianping@sjtu.edu.cnPurpose: Glucagon-like peptide-1 (GLP-1) is secreted from the intestinal L-cells to stimulate insulin secretion in the blood glucose control. Our previous study indicates that Sennoside A (SA) can increase the plasma GLP-1 level in a mouse model of type 2 diabetes. However, the mechanism of SA activity remains largely unknown. This issue was explored in this study.Materials and Methods: C57BL/6 mice were randomly divided into four groups: a control group without drug treatment, and the other groups with different SA dosages, respectively. Blood glucose was assayed by oral glucose tolerance test (OGTT). Plasma GLP-1 and insulin were investigated. Colon tissues were collected for mRNA or Western blot analysis. Immunofluorescence staining assays were performed to evaluate the number of β-cells and L-cells. In NCI-H716 cells, extracellular signal-regulated kinase  1/2 (ERK1/2) inhibitors were employed to investigate the SA-induced GLP-1 secretion mechanism.Results: In this work, the SA was found to improve OGTT in mice. Plasma GLP-1 and insulin were markedly elevated by SA at the dosage of 45 mg/kg/day. Meanwhile, the increased phosphorylation status of EKR1/2 and prohormone convertase 1/3 (PC1/3) proteins were observed in the

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, 2018., Cataloged from PDF version of thesis., Includes bibliographical references (pages 203-210)., This dissertation investigates the implications of consumer inattention and uncertainty for firms' advertising and pricing decisions. The first chapter is an overview of the problems addressed in the dissertation and the main findings. The second chapter develops a theory-based, cost-effective method to estimate the demand for new products using choice experiments. The premise is that consumers are uncertain about their valuation of a new product and need to spend costly effort to learn their valuation. The effort consumers spend is affected by the probability of their choice being realized, and as a result will change the manifested demand curve derived from choice experiments. We run a large-scale choice experiment on a mobile game platform, where we randomize the price and realization probability of a new product. Data support our theoretical hypothesis. We then estimate a structural model of consumer decisions. The structural estimates allow us to accurately infer actual demand based on choice experiments of small to moderate realization probabilities. The third chapter examines firms' advertising strategy on social media under consumers' limited attention. Advertising on social media faces a new challenge as consumers can actively select which advertisers to follow. A Bayesian learning model suggests that consumers with limited attention may rationally choose to unfollow a firm. This happens if consumers already know about the firm's value well and if the firm advertises too intensely. However, we find that intensive advertising may still be the optimal strategy for firms. If a firm is perceived as providing low value, it will want to advertise aggressively to change consumers' mind; if a firm is perceived as providing higher value, it will also want to advertise intensively, but in an effort to crowd-out advertising messages from its competitors. Tracking company accounts of 49 TV shows on the most popular tweeting website in China provides empirical evidence, by Xinyu Cao., Ph. D.

Numerous studies have found that suburban residents drive more and walk less than residents in traditional neighborhoods. What is less well understood is the extent to which the observed patterns of travel behavior can be attributed to the residential built environment itself, as opposed to the prior self-selection of residents into a built environment that is consistent with their predispositions toward certain travel modes and land use configurations. To date, most studies addressing this attitudinal self-selection issue fall into nine categories: direct questioning, statistical control, instrumental variables models, sample selection models, propensity score, joint discrete choice models, structural equations models, mutually-dependent discrete choice models, and longitudinal designs. This report reviews and evaluates these alternative approaches. Virtually all of the 38 empirical studies reviewed found a statistically significant influence of the built environment remaining after self-selection was accounted for. However, the practical importance of that influence was seldom assessed. Although time and resource limitations are recognized, we recommend usage of longitudinal structural equations modeling with control groups, a design which is strong with respect to all causality requisites.

Numerous studies have found that suburban residents drive more and walk less than residents in traditional neighborhoods. What is less well understood is the extent to which the observed patterns of travel behavior can be attributed to the residential built environment itself, as opposed to the prior self-selection of residents into a built environment that is consistent with their predispositions toward certain travel modes and land use configurations. To date, most studies addressing this attitudinal self-selection issue fall into nine categories: direct questioning, statistical control, instrumental variables models, sample selection models, propensity score, joint discrete choice models, structural equations models, mutually-dependent discrete choice models, and longitudinal designs. This report reviews and evaluates these alternative approaches. Virtually all of the 38 empirical studies reviewed found a statistically significant influence of the built environment remaining after self-selection was accounted for. However, the practical importance of that influence was seldom assessed. Although time and resource limitations are recognized, we recommend usage of longitudinal structural equations modeling with control groups, a design which is strong with respect to all causality requisites.

Numerous studies have found that suburban residents drive more and walk less than residents in traditional neighborhoods. What is less well understood is the extent to which the observed patterns of travel behavior can be attributed to the residential built environment itself, as opposed to the prior self-selection of residents into a built environment that is consistent with their predispositions toward certain travel modes and land use configurations. To date, most studies addressing this attitudinal self-selection issue fall into nine categories: direct questioning, statistical control, instrumental variables models, sample selection models, propensity score, joint discrete choice models, structural equations models, mutually-dependent discrete choice models, and longitudinal designs. This report reviews and evaluates these alternative approaches. Virtually all of the 38 empirical studies reviewed found a statistically significant influence of the built environment remaining after self-selection was accounted for. However, the practical importance of that influence was seldom assessed. Although time and resource limitations are recognized, we recommend usage of longitudinal structural equations modeling with control groups, a design which is strong with respect to all causality requisites.

Auto ownership is a critical mediating link in the connection between the built environment and travel behavior: the built environment presumably influences auto ownership, which in turn impacts travel behavior. However, the way in which individual elements of the built environment affect auto ownership choices is far from understood. Further, residential self-selection may confound the interaction between the built environment and auto ownership. And the absence of panel data impedes our understanding of the causal relationships. Using a survey of 1682 respondents in Northern California, this study applied ordered probit and static-score modeling techniques to investigate the causal link from the built environment to auto ownership in both cross-sectional and quasi-panel contexts. Through variable selection in cross-sectional analysis, we found that individuals’ attitudes regarding residential neighborhood and travel are more strongly associated with their auto ownership decision than is the built environment per se. Specifically, when general preferences for various neighborhood traits were allowed to enter the model, they drove out from the model the (perceived) measure of the same trait for the neighborhood of current residence, a pattern suggesting that the observed correlation between neighborhood characteristics and auto ownership is primarily a result of self-selection. On the other hand, the quasi-panel results indicate that some built environment elements such as outdoor spaciousness and mixed land use are causes of auto ownership (remaining even after attitudes were allowed to enter the model), but their effects are marginal. In contrast, the strong influence of socio-demographics suggests that households’ auto ownership decisions are fundamentally based on their mobility needs and purchasing power. Given the mixed findings, this study does not definitively confirm a causal relationship between the built environment and auto ownership. However, it provide

Suburban sprawl has been widely criticized for its contribution to auto dependence. Numerous studies have found that residents in suburban neighborhoods drive more and walk less than their counterparts in traditional environments. However, most studies confirm only an association between the built environment and travel behavior, and have yet to establish the predominant underlying causal link: whether neighborhood design independently influences travel behavior or whether preferences for travel options affect residential choice. That is, residential self-selection may be at work. A few studies have recently addressed the influence of self-selection. However, our understanding of the causality issue is still immature. To address this issue, this study took into account individuals’ self-selection by employing a quasi-longitudinal design and by controlling for residential preferences and travel attitudes. In particular, using data collected from 547 movers currently living in four traditional neighborhoods and four suburban neighborhoods in Northern California, we developed a structural equations model to investigate the relationships among changes in the built environment, changes in auto ownership, and changes in travel behavior. The results provide some encouragement that land-use policies designed to put residents closer to destinations and provide them with alternative transportation options will actually lead to less driving and more walking.

Suburban sprawl has been widely criticized for its contribution to auto dependence. Numerous studies have found that suburban residents drive more and walk less than residents in traditional neighborhoods. Accordingly, smart growth programs have been advocated as a means to reduce auto travel. However, most studies have established only an association between the built environment and travel behavior, but not a causal relationship. Their connection may be more a matter of residential choice than of travel choice. For example, residents preferring walking may selectively live in walkable neighborhoods and thus walk more. If so, the effects of land use policies may be overstated. Using data collected from 1682 respondents living in four traditional and four suburban neighborhoods in Northern California in 2003, this dissertation explored this causal link by employing a quasi-longitudinal research design and controlling for residential self-selection (namely, residential preferences and travel attitudes). Specifically, we investigated the influence of the built environment on various measurements of personal travel choices including uses of different modes (driving, transit, walking, and biking), trip frequencies for different purposes (overall travel, nonwork travel, shopping travel, and strolling), auto ownership, and vehicle type choice. The results showed that residential preferences and travel attitudes have pervasive influences on all measurements of travel choices. The results also provide some encouragement that land-use policies designed to put residents closer to destinations and provide them with alternative transportation options will actually lead to less driving and more walking. Taking the evidence from all our analyses together, however, neighborhood design appears to have a stronger influence on walking than on driving. In other words, the residential environment promoted by smart growth programs may be an effective strategy to encourage walking but ha

Suburban sprawl has been widely criticized for its contribution to auto dependence. Numerous studies have found that residents in suburban neighborhoods drive more and walk less than their counterparts in traditional environments. However, most studies confirm only an association between the built environment and travel behavior, and have yet to establish the predominant underlying causal link: whether neighborhood design independently influences travel behavior or whether preferences for travel options affect residential choice. That is, residential self-selection may be at work. A few studies have recently addressed the influence of self-selection. However, our understanding of the causality issue is still immature. To address this issue, this study took into account individuals’ self-selection by employing a quasi-longitudinal design and by controlling for residential preferences and travel attitudes. In particular, using data collected from 547 movers currently living in four traditional neighborhoods and four suburban neighborhoods in Northern California, we developed a structural equations model to investigate the relationships among changes in the built environment, changes in auto ownership, and changes in travel behavior. The results provide some encouragement that land-use policies designed to put residents closer to destinations and provide them with alternative transportation options will actually lead to less driving and more walking.

Auto ownership is a critical mediating link in the connection between the built environment and travel behavior: the built environment presumably influences auto ownership, which in turn impacts travel behavior. However, the way in which individual elements of the built environment affect auto ownership choices is far from understood. Further, residential self-selection may confound the interaction between the built environment and auto ownership. And the absence of panel data impedes our understanding of the causal relationships. Using a survey of 1682 respondents in Northern California, this study applied ordered probit and static-score modeling techniques to investigate the causal link from the built environment to auto ownership in both cross-sectional and quasi-panel contexts. Through variable selection in cross-sectional analysis, we found that individuals’ attitudes regarding residential neighborhood and travel are more strongly associated with their auto ownership decision than is the built environment per se. Specifically, when general preferences for various neighborhood traits were allowed to enter the model, they drove out from the model the (perceived) measure of the same trait for the neighborhood of current residence, a pattern suggesting that the observed correlation between neighborhood characteristics and auto ownership is primarily a result of self-selection. On the other hand, the quasi-panel results indicate that some built environment elements such as outdoor spaciousness and mixed land use are causes of auto ownership (remaining even after attitudes were allowed to enter the model), but their effects are marginal. In contrast, the strong influence of socio-demographics suggests that households’ auto ownership decisions are fundamentally based on their mobility needs and purchasing power. Given the mixed findings, this study does not definitively confirm a causal relationship between the built environment and auto ownership. However, it provide

Suburban sprawl has been widely criticized for its contribution to auto dependence. Numerous studies have found that residents in suburban neighborhoods drive more and walk less than their counterparts in traditional environments. However, most studies confirm only an association between the built environment and travel behavior, and have yet to establish the predominant underlying causal link: whether neighborhood design independently influences travel behavior or whether preferences for travel options affect residential choice. That is, residential self-selection may be at work. A few studies have recently addressed the influence of self-selection. However, our understanding of the causality issue is still immature. To address this issue, this study took into account individuals' self-selection by employing a quasi-longitudinal design and by controlling for residential preferences and travel attitudes. In particular, using data collected from 547 movers currently living in four traditional neighborhoods and four suburban neighborhoods in Northern California, we developed a structural equations model to investigate the relationships among changes in the built environment, changes in auto ownership, and changes in travel behavior. The results provide some encouragement that land-use policies designed to put residents closer to destinations and provide them with alternative transportation options will actually lead to less driving and more walking.

Auto ownership is a critical mediating link in the connection between the built environment and travel behavior: the built environment presumably influences auto ownership, which in turn impacts travel behavior. However, the way in which individual elements of the built environment affect auto-ownership choices is far from understood. Further, residential self-selection may confound the interaction between the built environment and auto ownership. And the absence of panel data impedes our understanding of the causal relationships. Using a survey of 1682 respondents in Northern California, we applied ordered probit and static-score modeling techniques to investigate the causal link from the built environment to auto ownership in both cross-sectional and quasi-panel contexts. Through variable selection in cross-sectional analysis, we found that individuals' attitudes regarding residential neighborhood and travel are more strongly associated with their auto-ownership decision than is the built environment per se. Specifically, when general preferences for various neighborhood traits were allowed to enter the model, they drove out from the model the (perceived) measure of the same trait for the neighborhood of current residence, a pattern suggesting that the observed correlation between neighborhood characteristics and auto ownership is primarily a result of self-selection. On the other hand, the quasi-panel results indicate that some built-environment elements such as outdoor spaciousness and mixed land use are causes of auto ownership (remaining even after attitudes were allowed to enter the model), but their effects are marginal. In contrast, the strong influence of sociodemographics suggests that households' auto-ownership decisions are fundamentally based on their mobility needs and purchasing power. Given the mixed findings, we do not definitively confirm a causal relationship between the built environment and auto ownership. However, we provide encouraging eviden

Suburban sprawl has been widely criticized for its contribution to auto dependence. Numerous studies have found that suburban residents drive more and walk less than residents in traditional neighborhoods. Accordingly, smart growth programs have been advocated as a means to reduce auto travel. However, most studies have established only an association between the built environment and travel behavior, but not a causal relationship. Their connection may be more a matter of residential choice than of travel choice. For example, residents preferring walking may selectively live in walkable neighborhoods and thus walk more. If so, the effects of land use policies may be overstated. Using data collected from 1682 respondents living in four traditional and four suburban neighborhoods in Northern California in 2003, this dissertation explored this causal link by employing a quasi-longitudinal research design and controlling for residential self-selection (namely, residential preferences and travel attitudes). Specifically, we investigated the influence of the built environment on various measurements of personal travel choices including uses of different modes (driving, transit, walking, and biking), trip frequencies for different purposes (overall travel, nonwork travel, shopping travel, and strolling), auto ownership, and vehicle type choice. The results showed that residential preferences and travel attitudes have pervasive influences on all measurements of travel choices. The results also provide some encouragement that land-use policies designed to put residents closer to destinations and provide them with alternative transportation options will actually lead to less driving and more walking. Taking the evidence from all our analyses together, however, neighborhood design appears to have a stronger influence on walking than on driving. In other words, the residential environment promoted by smart growth programs may be an effective strategy to encourage walking but ha

Suburban sprawl has been widely criticized for its contribution to auto dependence. Numerous studies have found that residents in suburban neighborhoods drive more and walk less than their counterparts in traditional environments. However, most studies confirm only an association between the built environment and travel behavior, and have yet to establish the predominant underlying causal link: whether neighborhood design independently influences travel behavior or whether preferences for travel options affect residential choice. That is, residential self-selection may be at work. A few studies have recently addressed the influence of self-selection. However, our understanding of the causality issue is still immature. To address this issue, this study took into account individuals’ self-selection by employing a quasi-longitudinal design and by controlling for residential preferences and travel attitudes. In particular, using data collected from 547 movers currently living in four traditional neighborhoods and four suburban neighborhoods in Northern California, we developed a structural equations model to investigate the relationships among changes in the built environment, changes in auto ownership, and changes in travel behavior. The results provide some encouragement that land-use policies designed to put residents closer to destinations and provide them with alternative transportation options will actually lead to less driving and more walking.

Auto ownership is a critical mediating link in the connection between the built environment and travel behavior: the built environment presumably influences auto ownership, which in turn impacts travel behavior. However, the way in which individual elements of the built environment affect auto ownership choices is far from understood. Further, residential self-selection may confound the interaction between the built environment and auto ownership. And the absence of panel data impedes our understanding of the causal relationships. Using a survey of 1682 respondents in Northern California, this study applied ordered probit and static-score modeling techniques to investigate the causal link from the built environment to auto ownership in both cross-sectional and quasi-panel contexts. Through variable selection in cross-sectional analysis, we found that individuals’ attitudes regarding residential neighborhood and travel are more strongly associated with their auto ownership decision than is the built environment per se. Specifically, when general preferences for various neighborhood traits were allowed to enter the model, they drove out from the model the (perceived) measure of the same trait for the neighborhood of current residence, a pattern suggesting that the observed correlation between neighborhood characteristics and auto ownership is primarily a result of self-selection. On the other hand, the quasi-panel results indicate that some built environment elements such as outdoor spaciousness and mixed land use are causes of auto ownership (remaining even after attitudes were allowed to enter the model), but their effects are marginal. In contrast, the strong influence of socio-demographics suggests that households’ auto ownership decisions are fundamentally based on their mobility needs and purchasing power. Given the mixed findings, this study does not definitively confirm a causal relationship between the built environment and auto ownership. However, it provide

Suburban sprawl has been widely criticized for its contribution to auto dependence. Numerous studies have found that residents in suburban neighborhoods drive more and walk less than their counterparts in traditional environments. However, most studies confirm only an association between the built environment and travel behavior, and have yet to establish the predominant underlying causal link: whether neighborhood design independently influences travel behavior or whether preferences for travel options affect residential choice. That is, residential self-selection may be at work. A few studies have recently addressed the influence of self-selection. However, our understanding of the causality issue is still immature. To address this issue, this study took into account individuals' self-selection by employing a quasi-longitudinal design and by controlling for residential preferences and travel attitudes. In particular, using data collected from 547 movers currently living in four traditional neighborhoods and four suburban neighborhoods in Northern California, we developed a structural equations model to investigate the relationships among changes in the built environment, changes in auto ownership, and changes in travel behavior. The results provide some encouragement that land-use policies designed to put residents closer to destinations and provide them with alternative transportation options will actually lead to less driving and more walking.

Auto ownership is a critical mediating link in the connection between the built environment and travel behavior: the built environment presumably influences auto ownership, which in turn impacts travel behavior. However, the way in which individual elements of the built environment affect auto-ownership choices is far from understood. Further, residential self-selection may confound the interaction between the built environment and auto ownership. And the absence of panel data impedes our understanding of the causal relationships. Using a survey of 1682 respondents in Northern California, we applied ordered probit and static-score modeling techniques to investigate the causal link from the built environment to auto ownership in both cross-sectional and quasi-panel contexts. Through variable selection in cross-sectional analysis, we found that individuals' attitudes regarding residential neighborhood and travel are more strongly associated with their auto-ownership decision than is the built environment per se. Specifically, when general preferences for various neighborhood traits were allowed to enter the model, they drove out from the model the (perceived) measure of the same trait for the neighborhood of current residence, a pattern suggesting that the observed correlation between neighborhood characteristics and auto ownership is primarily a result of self-selection. On the other hand, the quasi-panel results indicate that some built-environment elements such as outdoor spaciousness and mixed land use are causes of auto ownership (remaining even after attitudes were allowed to enter the model), but their effects are marginal. In contrast, the strong influence of sociodemographics suggests that households' auto-ownership decisions are fundamentally based on their mobility needs and purchasing power. Given the mixed findings, we do not definitively confirm a causal relationship between the built environment and auto ownership. However, we provide encouraging eviden

Suburban sprawl has been widely criticized for its contribution to auto dependence. Numerous studies have found that residents in suburban neighborhoods drive more and walk less than their counterparts in traditional environments. However, most studies confirm only an association between the built environment and travel behavior, and have yet to establish the predominant underlying causal link: whether neighborhood design independently influences travel behavior or whether preferences for travel options affect residential choice. That is, residential self-selection may be at work. A few studies have recently addressed the influence of self-selection. However, our understanding of the causality issue is still immature. To address this issue, this study took into account individuals’ self-selection by employing a quasi-longitudinal design and by controlling for residential preferences and travel attitudes. In particular, using data collected from 547 movers currently living in four traditional neighborhoods and four suburban neighborhoods in Northern California, we developed a structural equations model to investigate the relationships among changes in the built environment, changes in auto ownership, and changes in travel behavior. The results provide some encouragement that land-use policies designed to put residents closer to destinations and provide them with alternative transportation options will actually lead to less driving and more walking.

Auto ownership is a critical mediating link in the connection between the built environment and travel behavior: the built environment presumably influences auto ownership, which in turn impacts travel behavior. However, the way in which individual elements of the built environment affect auto ownership choices is far from understood. Further, residential self-selection may confound the interaction between the built environment and auto ownership. And the absence of panel data impedes our understanding of the causal relationships. Using a survey of 1682 respondents in Northern California, this study applied ordered probit and static-score modeling techniques to investigate the causal link from the built environment to auto ownership in both cross-sectional and quasi-panel contexts. Through variable selection in cross-sectional analysis, we found that individuals’ attitudes regarding residential neighborhood and travel are more strongly associated with their auto ownership decision than is the built environment per se. Specifically, when general preferences for various neighborhood traits were allowed to enter the model, they drove out from the model the (perceived) measure of the same trait for the neighborhood of current residence, a pattern suggesting that the observed correlation between neighborhood characteristics and auto ownership is primarily a result of self-selection. On the other hand, the quasi-panel results indicate that some built environment elements such as outdoor spaciousness and mixed land use are causes of auto ownership (remaining even after attitudes were allowed to enter the model), but their effects are marginal. In contrast, the strong influence of socio-demographics suggests that households’ auto ownership decisions are fundamentally based on their mobility needs and purchasing power. Given the mixed findings, this study does not definitively confirm a causal relationship between the built environment and auto ownership. However, it provide

Pedestrian travel offers a wide range of benefits to both individuals and society. Planners and public health officials alike have been promoting policies that improve the quality of the built environment for pedestrians: mixed land uses, interconnected street networks, sidewalks and other facilities. Whether such policies will prove effective remains open to debate. Two issues in particular need further attention. First, the impact of the built environment on pedestrian behavior may depend on the purpose of the trip, whether for utilitarian or recreational purposes. Second, the connection between the built environment and pedestrian behavior may be more a matter of residential location choice than of travel choice. This study aims to provide new evidence on both questions. Using 1368 respondents to a 1995 survey conducted in six neighborhoods in Austin, TX, two separate negative binomial models were estimated for the frequencies of strolling trips and pedestrian shopping trips within neighborhoods. We found that although residential self-selection impacts both types of trips, it is the most important factor explaining walking to a destination (i.e. for shopping). After accounting for self-selection, neighborhood characteristics (especially perceptions of these characteristics) impact strolling frequency, while characteristics of local commercial areas are important in facilitating shopping trips.

Suburban sprawl has been widely criticized for its contribution to auto dependence. Numerous studies have found that suburban residents drive more and walk less than residents in traditional neighborhoods. Accordingly, smart growth programs have been advocated as a means to reduce auto travel. However, most studies have established only an association between the built environment and travel behavior, but not a causal relationship. Their connection may be more a matter of residential choice than of travel choice. For example, residents preferring walking may selectively live in walkable neighborhoods and thus walk more. If so, the effects of land use policies may be overstated. Using data collected from 1682 respondents living in four traditional and four suburban neighborhoods in Northern California in 2003, this dissertation explored this causal link by employing a quasi-longitudinal research design and controlling for residential self-selection (namely, residential preferences and travel attitudes). Specifically, we investigated the influence of the built environment on various measurements of personal travel choices including uses of different modes (driving, transit, walking, and biking), trip frequencies for different purposes (overall travel, nonwork travel, shopping travel, and strolling), auto ownership, and vehicle type choice. The results showed that residential preferences and travel attitudes have pervasive influences on all measurements of travel choices. The results also provide some encouragement that land-use policies designed to put residents closer to destinations and provide them with alternative transportation options will actually lead to less driving and more walking. Taking the evidence from all our analyses together, however, neighborhood design appears to have a stronger influence on walking than on driving. In other words, the residential environment promoted by smart growth programs may be an effective strategy to encourage walking but ha

Pedestrian travel offers a wide range of benefits to both individuals and society. Planners and public health officials alike have been promoting policies that improve the quality of the built environment for pedestrians: mixed land uses, interconnected street networks, sidewalks and other facilities. Whether such policies will prove effective remains open to debate. Two issues in particular need further attention. First, the impact of the built environment on pedestrian behavior may depend on the purpose of the trip, whether for utilitarian or recreational purposes. Second, the connection between the built environment and pedestrian behavior may be more a matter of residential location choice than of travel choice. This study aims to provide new evidence on both questions. Using 1368 respondents to a 1995 survey conducted in six neighborhoods in Austin, TX, two separate negative binomial models were estimated for the frequencies of strolling trips and pedestrian shopping trips within neighborhoods. We found that although residential self-selection impacts both types of trips, it is the most important factor explaining walking to a destination (i.e. for shopping). After accounting for self-selection, neighborhood characteristics (especially perceptions of these characteristics) impact strolling frequency, while characteristics of local commercial areas are important in facilitating shopping trips.

Suburban sprawl has been widely criticized for its contribution to auto dependence. Numerous studies have found that suburban residents drive more and walk less than residents in traditional neighborhoods. Accordingly, smart growth programs have been advocated as a means to reduce auto travel. However, most studies have established only an association between the built environment and travel behavior, but not a causal relationship. Their connection may be more a matter of residential choice than of travel choice. For example, residents preferring walking may selectively live in walkable neighborhoods and thus walk more. If so, the effects of land use policies may be overstated. Using data collected from 1682 respondents living in four traditional and four suburban neighborhoods in Northern California in 2003, this dissertation explored this causal link by employing a quasi-longitudinal research design and controlling for residential self-selection (namely, residential preferences and travel attitudes). Specifically, we investigated the influence of the built environment on various measurements of personal travel choices including uses of different modes (driving, transit, walking, and biking), trip frequencies for different purposes (overall travel, nonwork travel, shopping travel, and strolling), auto ownership, and vehicle type choice. The results showed that residential preferences and travel attitudes have pervasive influences on all measurements of travel choices. The results also provide some encouragement that land-use policies designed to put residents closer to destinations and provide them with alternative transportation options will actually lead to less driving and more walking. Taking the evidence from all our analyses together, however, neighborhood design appears to have a stronger influence on walking than on driving. In other words, the residential environment promoted by smart growth programs may be an effective strategy to encourage walking but ha

The rapid growth of e-commerce is imposing profound impacts on modern society. On the supply side, the emergence of e-commerce is greatly changing the operation behavior of some retailers and is increasing product internationalization due to its geographically unlimited nature. On the demand side, the pervasiveness of e-commerce affects how, where, and when consumers shop, and indirectly influences the way in which we live our lives. However, the development of e-commerce is still in an early stage, and why consumers choose (or do not choose) online purchasing is far from being completely understood. To better evaluate and anticipate those profound impacts of e-commerce, therefore, it is important to further refine our understanding of consumers' e-shopping behavior.A number of studies have investigated e-shopping behavior, and reviewing them is valuable for further improving our understanding. This report aims to summarize previous e-shopping research in a systematic way. In this review, we are interested primarily in the potential benefits and costs that the internet offers for the business-to-consumer segment of e-commerce in the transaction (purchase) channel. An overview of the 65 empirical studies analyzed in this report is provided in the Appendix.Most previous studies fall into one or more of several theoretical frameworks, including the theory of reasoned action, the theory of planned behavior, the technology acceptance model, transaction cost theory, innovation diffusion theory, and others. Among them, social psychological theories (the theory of reasoned action, the theory of planned behavior, the technology acceptance model) were widely applied. As shown in the applications of different theories, e-shopping behavior is not a simple decision process, and thus an integration of various theories is necessary to deal with its complexities. We suggest synthesizing these theories through the development of a comprehensive list of benefits and costs, using each

Preparatory to an empirical analysis, this study conceptually discusses the influences of objective and subjective variables on the consideration of 16 travel-related strategies, reflecting a range of options individuals have to adapt to congestion. The variables considered here were measured by a 1998 survey conducted in the San Francisco Bay Area. The conceptual exploration shows that the consideration of travel-related strategies may be affected by the amounts of travel that individuals actually do, their subjective assessments, desires, affinities, and constraints with respect to travel. Individuals’ travel attitudes, personality, lifestyle and prior experience are also likely to affect their current consideration. Socio-economic and demographic characteristics may exhibit distributional effects with respect to the options individuals consider. These potential influences indicate that the individual adaptation process may be influenced by a wide range of qualitative and experiential variables, which are often ignored or omitted by policy makers and planners. A companion paper develops binary logit models of the consideration of each strategy.

The rapid growth of e-commerce is imposing profound impacts on modern society. On the supply side, the emergence of e-commerce is greatly changing the operation behavior of some retailers and is increasing product internationalization due to its geographically unlimited nature. On the demand side, the pervasiveness of e-commerce affects how, where, and when consumers shop, and indirectly influences the way in which we live our lives. However, the development of e-commerce is still in an early stage, and why consumers choose (or do not choose) online purchasing is far from being completely understood. To better evaluate and anticipate those profound impacts of e-commerce, therefore, it is important to further refine our understanding of consumers’ e-shopping behavior.A number of studies have investigated e-shopping behavior, and reviewing them is valuable for further improving our understanding. This report aims to summarize previous e-shopping research in a systematic way. In this review, we are interested primarily in the potential benefits and costs that the internet offers for the business-to-consumer segment of e-commerce in the transaction (purchase) channel. An overview of the 65 empirical studies analyzed in this report is provided in the Appendix.Most previous studies fall into one or more of several theoretical frameworks, including the theory of reasoned action, the theory of planned behavior, the technology acceptance model, transaction cost theory, innovation diffusion theory, and others. Among them, social psychological theories (the theory of reasoned action, the theory of planned behavior, the technology acceptance model) were widely applied. As shown in the applications of different theories, e-shopping behavior is not a simple decision process, and thus an integration of various theories is necessary to deal with its complexities. We suggest synthesizing these theories through the development of a comprehensive list of benefits and costs, using each

This study operationalizes the conceptual analysis presented in a companion paper, to examine the effects of objective and subjective variables on the consideration of 16 travel-related strategies reflecting a range of individuals’ potential reactions to congestion. Using 1283 commuting respondents to a 1998 survey conducted in the San Francisco Bay Area, binary logit models were developed for the consideration of each individual strategy. The proportion of information explained by these models ranges from 0.18 to 0.63. It was found that the consideration of travel-related strategies is affected not only by the amounts of travel that individuals actually do, but also by their subjective assessments, desires and affinities with respect to travel, as well as their travel attitudes, personality and lifestyle. The previous adoption of these strategies greatly affects their current consideration, demonstrating an effect of past experience. Mobility constraints and socioeconomic and demographic characteristics exhibit distributional effects with respect to the options individuals consider. These findings imply that policies designed to alleviate congestion may be less effective than expected, because individuals’ responses to the travel-related strategies analyzed here—many of them directly tied to public policies intended to reduce vehicle travel—are influenced by a large variety of qualitative and experiential variables that are seldom measured and incorporated into demand models. Therefore, understanding the role of such variables will improve our ability to design effective policies and to accurately forecast the response to policy interventions as well as natural trends.

The rapid growth of e-commerce is imposing profound impacts on modern society. On the supply side, the emergence of e-commerce is greatly changing the operation behavior of some retailers and is increasing product internationalization due to its geographically unlimited nature. On the demand side, the pervasiveness of e-commerce affects how, where, and when consumers shop, and indirectly influences the way in which we live our lives. However, the development of e-commerce is still in an early stage, and why consumers choose (or do not choose) online purchasing is far from being completely understood. To better evaluate and anticipate those profound impacts of e-commerce, therefore, it is important to further refine our understanding of consumers’ e-shopping behavior.A number of studies have investigated e-shopping behavior, and reviewing them is valuable for further improving our understanding. This report aims to summarize previous e-shopping research in a systematic way. In this review, we are interested primarily in the potential benefits and costs that the internet offers for the business-to-consumer segment of e-commerce in the transaction (purchase) channel. An overview of the 65 empirical studies analyzed in this report is provided in the Appendix.Most previous studies fall into one or more of several theoretical frameworks, including the theory of reasoned action, the theory of planned behavior, the technology acceptance model, transaction cost theory, innovation diffusion theory, and others. Among them, social psychological theories (the theory of reasoned action, the theory of planned behavior, the technology acceptance model) were widely applied. As shown in the applications of different theories, e-shopping behavior is not a simple decision process, and thus an integration of various theories is necessary to deal with its complexities. We suggest synthesizing these theories through the development of a comprehensive list of benefits and costs, using each

This study operationalizes the conceptual analysis presented in a companion paper, to examine the effects of objective and subjective variables on the consideration of 16 travel-related strategies reflecting a range of individuals’ potential reactions to congestion. Using 1283 commuting respondents to a 1998 survey conducted in the San Francisco Bay Area, binary logit models were developed for the consideration of each individual strategy. The proportion of information explained by these models ranges from 0.18 to 0.63. It was found that the consideration of travel-related strategies is affected not only by the amounts of travel that individuals actually do, but also by their subjective assessments, desires and affinities with respect to travel, as well as their travel attitudes, personality and lifestyle. The previous adoption of these strategies greatly affects their current consideration, demonstrating an effect of past experience. Mobility constraints and socioeconomic and demographic characteristics exhibit distributional effects with respect to the options individuals consider. These findings imply that policies designed to alleviate congestion may be less effective than expected, because individuals’ responses to the travel-related strategies analyzed here—many of them directly tied to public policies intended to reduce vehicle travel—are influenced by a large variety of qualitative and experiential variables that are seldom measured and incorporated into demand models. Therefore, understanding the role of such variables will improve our ability to design effective policies and to accurately forecast the response to policy interventions as well as natural trends.

Preparatory to an empirical analysis, this study conceptually discusses the influences of objective and subjective variables on the consideration of 16 travel-related strategies, reflecting a range of options individuals have to adapt to congestion. The variables considered here were measured by a 1998 survey conducted in the San Francisco Bay Area. The conceptual exploration shows that the consideration of travel-related strategies may be affected by the amounts of travel that individuals actually do, their subjective assessments, desires, affinities, and constraints with respect to travel. Individuals’ travel attitudes, personality, lifestyle and prior experience are also likely to affect their current consideration. Socio-economic and demographic characteristics may exhibit distributional effects with respect to the options individuals consider. These potential influences indicate that the individual adaptation process may be influenced by a wide range of qualitative and experiential variables, which are often ignored or omitted by policy makers and planners. A companion paper develops binary logit models of the consideration of each strategy.

The rapid growth of e-commerce is imposing profound impacts on modern society. On the supply side, the emergence of e-commerce is greatly changing the operation behavior of some retailers and is increasing product internationalization due to its geographically unlimited nature. On the demand side, the pervasiveness of e-commerce affects how, where, and when consumers shop, and indirectly influences the way in which we live our lives. However, the development of e-commerce is still in an early stage, and why consumers choose (or do not choose) online purchasing is far from being completely understood. To better evaluate and anticipate those profound impacts of e-commerce, therefore, it is important to further refine our understanding of consumers’ e-shopping behavior.A number of studies have investigated e-shopping behavior, and reviewing them is valuable for further improving our understanding. This report aims to summarize previous e-shopping research in a systematic way. In this review, we are interested primarily in the potential benefits and costs that the internet offers for the business-to-consumer segment of e-commerce in the transaction (purchase) channel. An overview of the 65 empirical studies analyzed in this report is provided in the Appendix.Most previous studies fall into one or more of several theoretical frameworks, including the theory of reasoned action, the theory of planned behavior, the technology acceptance model, transaction cost theory, innovation diffusion theory, and others. Among them, social psychological theories (the theory of reasoned action, the theory of planned behavior, the technology acceptance model) were widely applied. As shown in the applications of different theories, e-shopping behavior is not a simple decision process, and thus an integration of various theories is necessary to deal with its complexities. We suggest synthesizing these theories through the development of a comprehensive list of benefits and costs, using each

The rapid growth of e-commerce is imposing profound impacts on modern society. On the supply side, the emergence of e-commerce is greatly changing the operation behavior of some retailers and is increasing product internationalization due to its geographically unlimited nature. On the demand side, the pervasiveness of e-commerce affects how, where, and when consumers shop, and indirectly influences the way in which we live our lives. However, the development of e-commerce is still in an early stage, and why consumers choose (or do not choose) online purchasing is far from being completely understood. To better evaluate and anticipate those profound impacts of e-commerce, therefore, it is important to further refine our understanding of consumers' e-shopping behavior.A number of studies have investigated e-shopping behavior, and reviewing them is valuable for further improving our understanding. This report aims to summarize previous e-shopping research in a systematic way. In this review, we are interested primarily in the potential benefits and costs that the internet offers for the business-to-consumer segment of e-commerce in the transaction (purchase) channel. An overview of the 65 empirical studies analyzed in this report is provided in the Appendix.Most previous studies fall into one or more of several theoretical frameworks, including the theory of reasoned action, the theory of planned behavior, the technology acceptance model, transaction cost theory, innovation diffusion theory, and others. Among them, social psychological theories (the theory of reasoned action, the theory of planned behavior, the technology acceptance model) were widely applied. As shown in the applications of different theories, e-shopping behavior is not a simple decision process, and thus an integration of various theories is necessary to deal with its complexities. We suggest synthesizing these theories through the development of a comprehensive list of benefits and costs, using each

Numerous studies have found that suburban residents drive more and walk less than residents in traditional neighborhoods. What is less well understood is the extent to which the observed patterns of travel behavior can be attributed to the residential built environment itself, as opposed to the prior self-selection of residents into a built environment that is consistent with their predispositions toward certain travel modes and land use configurations. To date, most studies addressing this attitudinal self-selection issue fall into seven categories: direct questioning, statistical control, instrumental variables models, sample selection models, joint discrete choice models, structural equations models, and longitudinal designs. This paper reviews and evaluates these alternative approaches with respect to this particular application (a companion paper focuses on the empirical findings of 28 studies using these approaches). We identify some advantages and disadvantages of each approach, and note the difficulties in actually quantifying the absolute and/or relative extent of the true influence of the built environment on travel behavior. Although time and resource limitations are recognized, we recommend usage of longitudinal structural equations modeling with control groups, a design which is strong with respect to all causality requisites. (C) 2007 Elsevier Ltd. All rights reserved.

Purpose. Test for a causal relalionship between neighborhood design and physical activity within the neighborhood by controlling for self-selection.Design. Cross-sectional and quasi-longitudinal analyses of residents of selected neighborhoods.Setting. Eight Northern California neighborhoods.Subjects. Random sample of 1682 adults stratified by movers (moved within 1 year) and nonmovers (moved > 1 year ago) responding to self-administered mail surveys (24. 7 % response rate).Measures. Self-reported number of days in last 7 days of moderate to vigorous physical activity someiuhere in the neighborhood and self-reported change in physical activity in the neighborhood from prior to moving (for movers) or from 1 year ago (for nonmovers).Analysis. Zero-inflated Poisson regression for cross-sectional analysis (n = 1497); ordered probit model for quasi-longitudinal analysis (n = 1352).Results. After we controlled for physical activity attitudes and neighborhood preferences, selected neighborhood design characteristics were associated with physical activity within the neighborhood and changes in selected neighborhood design characteristics were associated with changes in physical activity within the neighborhood.Condusions. Both cross-sectional and quasi-longitudinal analyses provided evidence of a causal impact of neighborhood design. Improving physical activity options, aesthetic qualities, and social environment may increase physical activity. Critical limitations included self report measures of physical activity, lack of measures of duration and intensity of neighborhood physical activity, lack of measures of total physical activity, and limited measures of preferences related to physical activity. (AmJ Health Promot 2008;22l5]:350-358.)

Numerous studies have found that suburban residents drive more and walk less than residents in traditional neighborhoods. What is less well understood is the extent to which the observed patterns of travel behavior can be attributed to the residential built environment itself, as opposed to the prior self-selection of residents into a built environment that is consistent with their predispositions toward certain travel modes and land use configurations. To date, most studies addressing this attitudinal self-selection issue fall into seven categories: direct questioning, statistical control, instrumental variables models, sample selection models, joint discrete choice models, structural equations models, and longitudinal designs. This paper reviews and evaluates these alternative approaches with respect to this particular application (a companion paper focuses on the empirical findings of 28 studies using these approaches). We identify some advantages and disadvantages of each approach, and note the difficulties in actually quantifying the absolute and/or relative extent of the true influence of the built environment on travel behavior. Although time and resource limitations are recognized, we recommend usage of longitudinal structural equations modeling with control groups, a design which is strong with respect to all causality requisites.

Numerous studies have found that suburban residents drive more and walk less than residents in traditional neighborhoods. What is less well understood is the extent to which the observed patterns of travel behavior can be attributed to the residential built environment itself, as opposed to the prior self-selection of residents into a built environment that is consistent with their predispositions toward certain travel modes and land use configurations. To date, most studies addressing this attitudinal self-selection issue fall into seven categories: direct questioning, statistical control, instrumental variables models, sample selection models, joint discrete choice models, structural equations models, and longitudinal designs. This paper reviews and evaluates these alternative approaches with respect to this particular application (a companion paper focuses on the empirical findings of 28 studies using these approaches). We identify some advantages and disadvantages of each approach, and note the difficulties in actually quantifying the absolute and/or relative extent of the true influence of the built environment on travel behavior. Although time and resource limitations are recognized, we recommend usage of longitudinal structural equations modeling with control groups, a design which is strong with respect to all causality requisites. (C) 2007 Elsevier Ltd. All rights reserved.

Numerous studies have found that suburban residents drive more and walk less than residents in traditional neighborhoods. What is less well understood is the extent to which the observed patterns of travel behavior can be attributed to the residential built environment itself, as opposed to the prior self-selection of residents into a built environment that is consistent with their predispositions toward certain travel modes and land use configurations. To date, most studies addressing this attitudinal self-selection issue fall into seven categories: direct questioning, statistical control, instrumental variables models, sample selection models, joint discrete choice models, structural equations models, and longitudinal designs. This paper reviews and evaluates these alternative approaches with respect to this particular application (a companion paper focuses on the empirical findings of 28 studies using these approaches). We identify some advantages and disadvantages of each approach, and note the difficulties in actually quantifying the absolute and/or relative extent of the true influence of the built environment on travel behavior. Although time and resource limitations are recognized, we recommend usage of longitudinal structural equations modeling with control groups, a design which is strong with respect to all causality requisites.