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Garikapati V.M.,Arizona State University | You D.,Arizona State University | Pendyala R.M.,Arizona State University | Vovsha P.S.,Parsons Brinckerhoff | And 2 more authors.
Transportation Research Record | Year: 2014

Activity-based travel demand models use the notion of tours or trip chains as the fundamental building blocks of daily traveler activity-travel patterns. Travelers may undertake a variety of tours during the course of a day, and each tour may include one or more stops where individuals participate in and devote time to the pursuit of activities. This paper presents a framework capable of simulating the complete composition of a tour and oilers an approach to model the mix of activities and the time allocated to various activities in a tour. Embedded in the framework is a multiple discrete-continuous extreme value modeling component that was used to model the simultaneous decisions of participating in one or more activities in the course of a tour and of allocating time to each of the activities in the tour. The model was estimated with travel survey data collected in 2008 in the Greater Phoenix Metropolitan Area in Arizona. Validation and policy simulation exercises were conducted to examine the efficacy of the model. The model was found to perform well in replicating tour patterns in the estimation sample and responded in a behaviorally intuitive manner in the context of a policy sensitivity test.

Vyas G.,Parsons Brinckerhoff | Vovsha P.,Parsons Brinckerhoff | Paul B.,Parsons Brinckerhoff | Givon D.,Jerusalem Transportation Masterplan Team | Livshits V.,Maricopa Association of Governments
Transportation Research Record | Year: 2015

Most modern activity-based travel demand models (ABMs) in practice and research do not fully capture the central idea that travel is derived from activities. The basic unit adopted in ABMs for travel analysis is the tour, which is borrowed largely from tour-based travel demand models. To a certain extent, this approach contradicts the basic idea of ABMs in which the unit for travel analysis is the activity. In reality, individuals plan to participate in various activities in a day, and the tours and corresponding trips emerge from activity participation, potential activity location, and activity sequence choices coupled with time and space constraints imposed by activities with relatively lower spatial and temporal flexibility. The model discussed in this paper is an effort to better mimic this decision-making process. This model is a part of the latest version of the coordinated travel and regional activity modeling platform (CT-RAMP) adopted for the Jerusalem, Israel, and Phoenix, Arizona, ABMs.

Vovsha P.,Parsons Brinckerhoff | Freedman J.,Parsons Brinckerhoff | Livshits V.,Maricopa Association of Governments | Sun W.,San Diego Association of Governments
Transportation Research Record | Year: 2011

This paper describes design features of several different regional activity-based models (ABMs) that share the coordinated travel-regional activity modeling platform (CT-RAMP) design and software platform. The CTRAMP models are characterized by features such as a full simulation of travel decisions for discrete households and persons, explicit tracking of time in half-hourly increments, the use of time constraints on the generation of travel, and explicitly modeled intrahousehold interactions across a range of activity and travel dimensions. These important features allow for greater behavioral realism in representing the response to numerous transportation policies. Each implementation of the CT-RAMP system, as for many ABM systems used in practice over the years, shares certain common features with others. However, each implementation is tailored to address specific regional conditions and includes additional advanced features to provide increased policy sensitivity and greater behavioral realism. These features are explained in the paper and analyzed in the context of model applications for different transportation projects and policies. Some of these features stem from ongoing intensive research and development in the field, including cross-pollination of ideas between the CT-RAMP family and other ABMs developed elsewhere, but many other features are unique and were driven by practical needs. The main conclusion of this paper is that it is too early to establish a completely generic and standard approach to an ABM design in practice. The evolution of features in ABMs still continues and is driven by theoretical achievements in behavior research and practical considerations.

Paul B.M.,Parsons Brinckerhoff | Vovsha P.S.,Parsons Brinckerhoff | Hicks J.E.,Parsons Brinckerhoff | Livshits V.,Maricopa Association of Governments | Pendyala R.M.,Arizona State University
Transportation Research Record | Year: 2014

Most of the recent advances in activity-based models (ABMs) have been on the demand side, that is, description of the individual needs for certain types of activities and travel as a function of person, household, and accessibility variables. The supply side of activities that describes characteristics of the locations where a certain activity can be undertaken remains largely unexplored. Two examples of specific activity generators for which the supply side of activity is essential for modeling are major universities and special events. Travel behavior and activity patterns of university students are different from that of the general population, and therefore modeling them with the necessary level of detail enhances the ABM forecasting ability. The same is true about special events such as sporting events. Special events participants can form a substantial part of the overall travel demand in the subarea on the event day, and therefore it is important to model them properly. Although previous studies have acknowledged the importance of modeling university students and special events participants, the challenge remains to integrate them in an ABM framework. This paper describes new practical methods for addressing the supply side of activities (using university students and special events as examples) in the framework of an operational ABM developed for Phoenix and Tucson, Arizona. The paper provides details on the data and methods used to develop university and special events submodels. The methodology and technical approach used to incorporate these models in the ABM framework are presented.

Gupta S.,Parsons Brinckerhoff | Vovsha P.S.,Parsons Brinckerhoff | Livshits V.,Maricopa Association of Governments | Maneva P.,Maricopa Association of Governments | Jeon K.,Maricopa Association of Governments
Transportation Research Record | Year: 2014

Escorting children to school is a common travel arrangement in a household with schoolchildren. This escorting task affects travel patterns of the adult household members as accommodations are made for dropping children off at school or picking them up or doing both. Approaches to modeling joint travel arrangements between adults and children with respect to escorting have been previously suggested. However, examples of implementing such models in the framework of an operational activity-based model (ABM) are limited. This paper focuses on the explicit modeling of the escorting of children to school by adults and takes into account the possible bundling of escorting tasks in households with multiple children. The developed model is part of the regional ARM system currently being developed for the Maricopa Association of Governments in Arizona. Such a model allows for constraining the travel schedules of workers who tend to escort children on their way to and from work. Escorting has important policy implications because workers who escort children to and from school are very restricted in changing their departure times to and from work and in switching to transit; these restrictions are not evident otherwise. A choice model was formulated and estimated for each household by outbound (to school) and inbound (from school) escorting needs that were dependent on the number of schoolchildren, options of bundling children for escorting on one tour, and number of available chauffeurs in the household.

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