Incorporating In-Home Activities in ADAPTS: A Sequential Conditional Probability Framework

Abstract In travel demand modeling literature, minor attention has been given to modeling and specifically micro-simulation of In-home activities. One reason could be associated with the fact that in-home activities do not explicitly involve trips. Nonetheless, they could be substituted with out-of-home activities which indirectly affect the trips people make. Perceiving the associations between the activities people perform in-home and out-of-home could enhance the activity generation and scheduling procedures in activity-based models by not only improving the activity-travel decisions but also incorporating people’s in-home activities in to the frameworks. In an effort to analyze such associations, this study uses a Copula-based Joint Multinomial Discrete Continuous approach for modeling activity type and duration decision for in-home activities in conjunction with out-of-home ones. This model helps to better understand the behavioral trade-off individuals consider in terms of performing activities either in-home or outside. A number of activity types such as personal maintenance, household maintenance, mandatory and leisure, among others have been used for this joint model. The paper then proposes an algorithm to incorporate in-home activities within the Agent-based Activity Planning and Travel Scheduling (ADAPTS) (1). The new framework is capable of modeling and simulating in-home activities alongside and in conjunction with out-of-home activities with a special focus on a large set of in-home activity types. To the best our knowledge, the framework is the pioneer in incorporating in-home activity types into the conventional activity-based models and simulates daily routine of individuals. In order to perceive the association between in-home and out-of-home activities and extract the pattern behind how people substitute out-of-home activities for in-home activities, detailed time-use surveys are required (2). Doherty and Miller 2000 designed a survey called Computerized Household Activity Scheduling (CHASE) in order to trace household in-home and out-of-home activities during one week (3). Later on, American Time Use Survey started conducting annual survey on activity time use since 2003 which is by far the most accredited source of activity time use survey that records all activities individuals perform in great detail during one full day. This study uses the data that comes from ATUS 2012 time use survey. The literature behind Discrete Continuous Models goes back to about three decades ago. Discrete-Continuous Choice models were initially proposed by Hanneman 1984 who presented the consumer demand commodity allocation model based on Random Utility Maximization and Optimization technique (4). Bhat 2005, 2008 introduced Multiple Discrete Continuous Extreme Value Model (MDCEV) which was specifically formulated to target time allocation for various activities within random utility framework. The advantage of random utility models over Budget Constrained Consumer models is the consideration of random variations which is the justification for the more behavioral simulation of time allocation (5, 6). Since 2005, MDCEV has been used in a variety of applications from residential energy consumption to conventional time allocation models. In contrast with MDCEV models which are typically used for allowing multiple discrete-continuous choices, this paper uses a joint Copula Multinomial Discrete Continuous Model that can be used for simple multinomial choice applications. The model developed is then incorporated in the ADAPTS. ADAPTS was designed to simulate the process of out-of-home activity planning and scheduling in a setting so as to capture the dynamism behind individuals’ activity planning. Figure 1 represents the schematic framework of the new ADAPTS. To capture the associations between in-home and out-of-home activities, the previous framework is updated to generate in-home activities through the Copula-based Joint Activity Type Choice and Duration Model. The model incorporates variables to associate in-home activities to the out-of-home ones and vice versa. For example, as the simulation runs time step T, if there are no out-of-home activities queued for execution, then the corresponding copula-based model is applied to generate an in-home activity of a type and duration conditional to the previously executed and following preplanned activities, either in-home or out-of-home. At the same time, similar link is connected to out-of-home activity generation and planning models so that the attributes of the executing out-of-home activity would be set by taking in to account the previous or following activities. Therefore, a simulation environment is provided to take in to account the in-home and out-of-home activity associations. This paper elaborates on three important contributions in the literature. First, some important insights will be extracted out of the credible ATUS data source through both the model and the descriptive study on the subject. Second would be the application of Copula-based multinomial activity type and duration model in regards to better perceiving the trade-off between in-home and out-of-home activities. Last, but most important; this is an effort to incorporate in-home activities in to activity-based travel demand models to capture in-home and out-of-home activity associations. Figure 1. ADAPTS Framework Incorporating In-home Activity Generation and Execution References: (1) Auld, J. A. , A. Mohammadian, and P. Nelson. "Activity planning processes in the adapts activity-based modeling framework. " Proceedings of the 12th International Conference on Travel Behavior Research, Jaipur, India . 2009. (2) Bhat, C.R. (1998). A model of post home-arrival activity participation behavior. Transportation Research Part B , 32, 387-400. (3) Doherty, Sean T. , and Eric J. Miller. "A computerized household activity scheduling survey. " Transportation 27.1 (2000): 75-97. (4) Hanemann, W. Michael. "Discrete/continuous models of consumer demand. " Econometrica: Journal of the Econometric Society (1984): 541-561. (5) Bhat, Chandra R. "A multiple discrete–continuous extreme value model: formulation and application to discretionary time-use decisions. " Transportation Research Part B: Methodological 39.8 (2005): 679-707. (6) Bhat, Chandra R. 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