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            "title": "A lifestyle-based model of daily activity and travel patterns",
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            "abstractNote": "This study proposes a model of individual daily activity and travel patterns that captures the concurrent influence of the individual’s lifestyle and modality style on each of the many decisions that together constitute her daily activity and travel pattern. Modality styles are defined as lifestyles built around the use of a particular travel mode or set of travel modes (Vij et al., 2013). They are hypothesized to influence all dimensions of individual and household travel and activity behavior. Transportation policies and infrastructural investments seeking to change existing patterns of travel and activity behavior are frequently undermined by deeply entrenched lifestyles and modality styles built around the use of the car. As Ryuichi Kitamura (1988) wrote in his seminal paper on lifestyles and travel behavior, “To deal with the challenge to urban transportation, one must first recognize that congestion is not the problem, but merely a symptom. The true problem is the life-style to which Americans aspire; the American dream is to live in a suburban single-family house on a half-acre lot with a three-car garage. If this is the root of the urban transportation problem, then obviously a fundamental solution to the issue of congestion cannot be reached without addressing the question of life-style.”   Travel demand models constitute an important component of the planning and policy-making process. Forecasts from travel demand models are regularly employed to determine the required capacity that new infrastructure must satisfy, and to facilitate the economic, environmental and social impact assessments that usually accompany the debate on how to allocate funds between competing initiatives. The field of travel demand analysis has progressed far beyond the aggregate models that defined the profession for much of the last century. Research during the eighties and nineties culminated in the activity-based model, a disaggregate model of individual and household travel and activity behavior which formulates the demand for travel as a function of the demand for various activities, predicting which activities are conducted where, when, for how long, with whom and using what mode of travel. Though the shift towards disaggregate models of decision-making has been seen as a significant step forward, activity-based models currently in practice continue to be deficient in their inability to account for the existence of different modality styles, and the subsequent influence exerted by these differing modality styles on multiple dimensions of individual and household travel and activity behavior.   The objective of this study is to develop and test an econometric model framework that recognizes the influence exerted by modality styles on all facets of an individual’s daily activity pattern. Using the model of daily activity patterns embedded within the San Francisco Chained Activity Modeling Process (SF-CHAMP), the state-of-the-art activity-based model developed for the San Francisco County Transportation Authority (SFCTA), as our point of departure (Cambridge Systematics, 2002), we build on recent advances in discrete choice analysis in the sub-domains of taste heterogeneity, choice set generation, preference endogeneity and simultaneous choice models to develop a latent class choice model (LCCM) of daily activity patterns. Our framework extends the LCCM framework proposed by Vij and Walker (2014), first applied to study the influence of modality styles on travel mode choice behavior, to include the additional influence of modality styles on other dimensions of behavior, such as time of day and destination choice, that together make up an individual’s daily activity pattern. For example, in the context of time of day choice, different modality styles may be characterized by differences in the times considered when deciding when to travel and differences in the sensitivity to accessibility measures when making that decision. In the context of destination choice, different modality styles may be characterized by differences in the places considered when deciding where to travel and differences in the sensitivity to land use variables when making that decision. These individual differences, when jointly modeled, should offer a greater understanding of differences in lifestyles and modality styles underlying existing patterns of behavior.   Figure 1 illustrates the model framework in greater detail. Modality styles are represented as latent classes, and the class membership model predicts the probability that an individual has a particular modality style as a function of socioeconomic variables, such as employment and household income, and long-term travel and activity decisions, such as residential location and level of car ownership. Conditioned on the class, or modality style, that the individual belongs to, the class-specific choice model predicts the probability that the individual chooses a particular daily activity pattern as a function of the attributes of the transportation and land use system. The class-specific choice model may further be decomposed into a series of sequentially nested logit models, analogous to SF-CHAMP’s daily activity pattern model. Lower dimensions, such as trip mode choice and trip destination choice, are conditioned on higher dimensions, such as full-day pattern choice and tour time of day choice; outcomes to higher dimensions are formulated as a function of logsums feeding upwards from lower dimensions. Heterogeneity in the decision-making process is captured by allowing taste parameters and choice sets for each choice dimension to vary across the classes.   The framework is tested using single-day travel diary data retrieved from 17,199 individuals belonging to 8,081 households residing in the nine-county San Francisco Bay Area, collected as part of the larger California Household Travel Survey 2012. Early estimation results indicate the existence of seven distinct modality styles, or classes, in the sample population. Different modality styles differ from one another in terms of their socioeconomic composition, their predisposition towards different alternatives and their sensitivity to different attributes of the transportation and land use system. For example, one of the seven classes, labeled Suburban Drivers, comprises 13.7% of the sample population and consists largely of adults belonging to high-income households with kids living in single-family homes with high levels of car ownership. Suburban Drivers do not consider any travel modes other than the car when deciding how to travel, are unlikely to frequent mixed-use walkable neighborhoods, and are immune to congestion and other service-related factors when deciding what time of day to travel. Another class, labeled Multimodals, comprises 6.0% of the sample population and consists largely of single employed adults belonging to small households with no kids living in rented apartments, often with a carshare membership. Multimodals consider all travel modes when deciding how to travel, prefer to visit mixed-use neighborhoods (though they too do not prefer walkable neighborhoods), and are sensitive to congestion and other service-related factors when deciding what time of day to travel. In summary, the LCCM framework is able to paint a far more vivid picture of lifestyle and modality style typologies that exist in the Bay Area than would be possible using a more traditional activity-based model, such as the SF-CHAMP. Ongoing work is refining some of these estimation results and exploring implications for policy analysis.   The model framework developed by this study represents a paradigmatic transition from activity-based models of travel and activity behavior to lifestyle-based models of travel and activity behavior. Our findings should serve as a proof-of-concept for how other activity-based model systems, such as the Comprehensive Econometric Micro-simulator of Daily Activity-Travel Patterns (CEMDAP) developed by Bhat et al. (2004), or the Coordinated Travel - Regional Activity Modeling Platform (CT-RAMP) developed by Parsons Brinckerhoff (Davidson et al., 2010), may analogously be extended to incorporate the influence of lifestyles and modality styles, with the intention of facilitating the kind of analysis that Kitamura thought necessary two-and-a-half decades ago.    References    Cambridge Systematics (2002), “ San Francisco Travel Demand Forecasting Model Development: Executive Summary ,” prepared for San Francisco County Transportation Authority.   Bhat, C. R., Guo, J. Y., Srinivasan, S., and Sivakumar, A. (2004), “ Comprehensive econometric microsimulator for daily activity-travel patterns ,”  Transportation Research Record: Journal of the Transportation Research Board , Vol. 1894, No. 1, pp. 57-66.   Davidson, W., Vovsha, P., Freedman, J., and Donnelly, R. (2010), “ CT-RAMP family of activity-based models ,”  Proceedings of the 33rd Australasian Transport Research Forum (ATRF) .   Kitamura, R. (1988), “ Life-style and travel demand ,”  Transportation Research Board Special Report 220 , pp. 149-189. Washington, DC: Transportation Research Board.   Vij, A., Carrel, A., and Walker, J. L. (2013), “ Incorporating the influence of latent modal preferences on travel mode choice behavior ”  Transportation Research Part A: Policy and Practice,  Vol. 54, pp. 164-178.   Vij, A., and Walker, J. L. (2014), “ Preference Endogeneity in Discrete Choice Models, ”  Transportation Research Part B,  Vol. 64, pp. 90-105.",
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