Progress in practical applications of large, passively collected data sets is often hindered by the lack of appropriate analytical tools or the proprietary nature of applicable software. One of the most widely used data sources in the United States is truck GPS data that are commercially available from a few sources nationwide. Although many large GPS data sets are used in the development of tour-based truck models, the development of a fairly general approach to data analysis and processing that can be readily applied to various GPS data sets without need of proprietary software is still of interest. First, this paper presents a set of tools and techniques used to transform low-frequency truck GPS data available from commercial sources into complete trajectories on the network, that is, sequences of links constituting continuous paths traversed by each truck, with corresponding time stamps on each of the nodes. For this exercise, only open-source software was used, and the algorithm implementation was released as an open-source tool under a business-friendly license. Second, use of the truck GPS data was expanded beyond the standard extraction of trip matrices and estimation of tour models. Additional applications include select link analysis, time-of-day analysis, and trajectory data visualization.

The travel associated with special events in any metropolitan region is often under-represented in regional travel demand models owing to the lack of travel data to and from these events. This paper presents a comprehensive data collection effort aimed at capturing travel data related to special events and development of a standalone special events model for the Phoenix metropolitan area. The importance of this study was highlighted by the success of the introduction of light rail transit in the region. Special event patrons constitute a significant portion of the light rail ridership and overall regional travel demand. Therefore, the need to better understand and forecast transit markets served as the impetus to conduct an in-depth study and modeling of planned special events in the region. This paper focuses on all aspects of data collection and model development including survey design and sampling plan, collection of special events travel data, expansion of survey data, model design, estimation and development of the special events model, calibration and validation, integration with the four step trip based model, and the impact on light rail transit ridership due to special event travel.

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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 ABM 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.

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 offers 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.