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15DESIGN FEATURES OF ACTIVITY- BASED MICROSIMULATION MODELS shopping, household and/or personal business, socialâ recreational, eat out, and other serve passenger activi- ties) and three for children (school, joint discretionary activity with parent, and independent discretionary activities). CEMDAP does not distinguish between types of in- home activities. INTRAHOUSEHOLD INTERACTIONS AND EXPLICIT ALLOCATION OF ACTIVITIES In CEMDAP, the activity generation and allocation decisions are simulated in the following three sequen- tial steps: (a) the generation of work and school activ- ity participation, (b) the generation of childrenâs travel needs and explicit allocation of escort responsibilities to one of the parents, and (c) the generation of inde- pendent activities for personal and household needs. Linkage of joint activities, travel, or both is imple- mented between parents and children (in single- parent and nuclear- family households) in two ways: drop off at or pick up from school and joint discretionary activ- ities. Due to data limitations, the nature of these inter- actions is currently restrictive. For instance, CEMDAP does not consider the case of one of the parents drop- ping off or picking up multiple school children at multiple locations. There is also an âother serve- passengerâ activity type recognized in CEMDAP, but the activity- travel pattern linkage across household members is not now explicitly implemented for this activity type because of lack of data. The grocery shopping activity is modeled to be gen- erated at the household level and is allocated to one of the adults. Joint participation of adults in activities is currently not considered because of lack of good data to estimate these models in the DallasâFort Worth, Texas, area. LEVEL AT WHICH INTERMEDIATE STOP PURPOSE AND FREQUENCY ARE MODELED Activity travel patterns are modeled separately for workers (adults who go to school or work on travel day) and nonworkers (adults who neither go to work nor attend school during the day). The daily pattern of workers is characterized by four subpatterns: before- work pattern, which represents the activity- travel undertaken before leaving home to work; com- mute pattern, which represents the activity- travel pursued during the home- to- work and work- to- home commutes; work- based pattern, which includes all activity and travel undertaken from work; and after- work pattern, which comprises the activity and travel behavior of individuals after arriving home at the end of the work- to- home commute. Within each of the before- work, work- based, and after- work patterns, there might be several tours. Each tour, the home- to- work commute, and the work- to- home commute may include several activity stops. In the case of nonwork- ers, the activity- travel pattern is considered as a set of tours, each of them comprising a sequence of out- of- home activity stops. The number of tours is predicted at the subpattern level for workers (pattern level for nonworkers), while the tour mode and the stop frequency are predicted at the tour level. The activity purpose, activity duration, home stayâwork stay duration before the activity, travel time to the activity stop, and destination are predicted for each of the individual activity stops. In essence, the stop purpose is modeled at the stop level, and the stop frequency is modeled at the tour level. The purpose and frequency of stops are modeled conditional on a higher- level choice of each person to undertake activities of various types (activity generation models). (continued on next page) DAY- PATTERN TYPE LINKED EXPLICITLY ACROSS HOUSEHOLD MEMBERS This and the following three sections are concerned with the modeling of explicit linkages between the predicted activities and travel of different members of the same household. All the models treat such linkages implicitly through the use of a wide variety of person type and household composition variables, and indeed one of the main advantages of the microsimulation approach is the ability to reduce aggregation bias by including such case- specific variables. The use of explicit linkages takes that ability one step further and reduces aggregation bias even more. One of the key linkages is fairly simple: if each personâs full- day activity pattern is classified into three main typesâ stay at home, go to work or school, or travel for some other purposeâ then strong similarities can be seen between the patterns of members of the same household, ones even stronger than the similarities that would be predicted indirectly. The Columbus model sys- tem includes a sequential model of these linkages, simu- lating children first and then adults conditional on what the children do. The Atlanta model system includes a similar model that is estimated simultaneously across all household members, avoiding the need to assume the order in which they are simulated and thus the direction