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Innovations in Travel Demand Modeling, Volume 2: Papers (2008)

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Suggested Citation:"T57054 txt_018.pdf." National Academies of Sciences, Engineering, and Medicine. 2008. Innovations in Travel Demand Modeling, Volume 2: Papers. Washington, DC: The National Academies Press. doi: 10.17226/13678.
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have not been recommended for the Bay Area models. In addition, the limited number of activity categories offered in most surveys makes it rather difficult to deter- mine which activities are most likely to be allocated. For example, grocery shopping is mainly an allocated activ- ity, while shopping for a good book is an individual activity, but both are usually coded the same. LEVEL AT WHICH INTERMEDIATE- STOP PURPOSE AND FREQUENCY ARE MODELED When the models in an activity- based system are ordered from top to bottom, it is not always clear which deci- sions should be modeled conditionally on which other decisions. A prime example is the generation of interme- diate stops made during tours. Are activities planned and combined into trip chains when a person is planning a day (in which case the mode, timing, and location of the tours may depend on which stops they contain)? Or, con- versely, do people make tours and then decide during the tour how often and where to make stops, depending on their mode and location? Clearly, both of these situa- tions describe real behavior, and which description is more accurate depends on the particular person and the types of activities they are carrying out. The Portland and San Francisco models follow closely the original Bowman and Ben- Akiva day- pattern approach, in which the number and purpose of any intermediate stops are predicted at the person- day level before any particular tours are simulated. In contrast, the Columbus, New York, and Atlanta models predict only the number and purpose of tours at the person- day level, and then the number and purpose of intermediate stops on any par- ticular tour are predicted at the tour level once the tour destination, time of day, and main mode are known. In the Sacramento models, an intermediate approach is used. Some information about stop- making is predicted at the person- day level, predicting whether or not any intermediate stops are made for each activity purpose during the day (seven yes–no variables). These are pre- dicted jointly with the choice of whether or not to make any tours for each of the activity purposes (seven more yes–no variables), thus capturing some substitution effects between the number of tours and the number of trips per tour. Then, when each tour is simulated, the exact number and purpose of stops on each tour are pre- dicted conditional on both the mode and destination of that tour and the types of stops that still need to be sim- ulated to fulfill the person- day level prediction. There is no obvious behavioral reason for this structure other than that it balances the model sensitivities between the two types of behavior described earlier. A similar approach is planned for Denver and recommended for the Bay Area. NUMBER OF NETWORK ZONES USED This and the next two sections discuss spatial aspects of the model systems. In all cases, the zone system used for model development and application is the same as that used for trip- based models. The automobile and transit networks and assignments are also the same as those in the trip- based models. This fact has facilitated the transi- tion to activity- based models, but at the same time, the microsimulation framework can also be used with more detailed spatial systems and would support more accu- rate traffic simulation methods as well. SMALLER SPATIAL UNITS USED BELOW ZONES Because the microsimulation framework is not tied as strongly to zone definitions, it is possible to use the zones only to provide the level- of- service variables for roads and transit paths, while variables related to land use, parking, and walk access (which do not need to be stored as matrices) can be specified at a finer level. The Portland model uses such an approach for roughly 20,000 “blocks,” while the Sacramento models use over 700,000 parcels. The Denver metropolitan planning organization is also planning to predict demand at the parcel or build- ing level by means of a model framework for two- stage destination choice. An intermediate approach, which has been recommended for the Bay Area models, is to divide zones with heterogeneous transit and walk accessibilities into more homogeneous subzones, but with assignments and skims still done at the larger zone level. SIMULTANEOUS MODE AND DESTINATION CHOICE MODEL ESTIMATION It has become a sort of tradition in modeling to condi- tion mode choice upon a known destination, sometimes by using a sequential nested structure in which the mode choice log sum is used in the destination choice model. That is probably appropriate for purposes such as work and school. For purposes such as shopping, however, the choice of store may depend more upon the mode used than vice versa. Simultaneous estimation of mode and destination choices allows the modeler to test different nesting hypotheses. Such an approach was used in the Portland model and may be used in Denver as well. NETWORK AND MODELED TIME PERIODS Most four- step models only use two times of day— peak and off peak— and use fixed time- of- day factors. All the activity- based models contain tour time- of- day models 18 INNOVATIONS IN TRAVEL DEMAND MODELING, VOLUME 2

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TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 2: Papers includes the papers that were presented at a May 21-23, 2006, conference that examined advances in travel demand modeling, explored the opportunities and the challenges associated with the implementation of advanced travel models, and reviewed the skills and training necessary to apply new modeling techniques. TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 1: Session Summaries is available online.

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