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Recent advances in global positioning systems (GPS) technology have resulted in a transition in household travel survey methods to test the use of GPS units to record travel details, followed by the application of an algorithm to both identify trips and impute trip purpose, typically supplemented with some level of respondent confirmation via prompted-recall surveys. As the research community evaluates this new approach to potentially replace the traditional surveyreported collection method, it is important to consider how well the GPS-recorded and algorithm-imputed details capture trip details and whether the traditional survey-reported collection method may be preferred with regards to some types of travel.This paper considers two measures of travel intensity (survey-reported and GPSrecorded) for two trip purposes (work and non-work) as dependent variables in a joint ordered response model. The empirical analysis uses a sample from the full-study of the 2009 Indianapolis regional household travel survey. Individuals in this sample provided diary details about their travel survey day as well as carried wearable GPS units for the same 24-hour period.The empirical results provide important insights regarding differences in measures of travel intensities related to the two different data collection modes (diary and GPS). The results suggest that more research is needed in the development of workplace identification algorithms, that GPS should continue to be used alongside rather than in lieu of the traditional diary approach, and that assignment of individuals to the GPS or diary survey approach should consider demographics and other characteristics.

In loving memory of my grandmother N. N. Rugmani AcknowledgmentsFirst and foremost, I must express appreciation to Dr. Chandra Bhat, with whom it has been a pleasure and a privilege to work. I thank him for his insights and advice as also for the stimulus and motivation to dig deep into research. I am grateful to him for showing me how to make sense of mathematical models and for instilling in me the notion that writing should be simple, direct, logical, and tight. His invaluable ideas and the high standards that he sets for himself and his students form the backbone of

This paper proposes a reformulation of count models as a special case of generalized orderedresponse models in which a single latent continuous variable is partitioned into mutually exclusive intervals. Using this equivalent latent variable-based generalized ordered response framework for count data models, we are then able to gainfully and efficiently introduce temporal and spatial dependencies through the latent continuous variables. Our formulation also allows handling excess zeros in correlated count data, a phenomenon that is commonly found in

This paper proposes a new spatial multivariate model to predict the count of new businesses at a county level in the state of Texas. Several important factors including agglomeration economies/diseconomies, industrial specialization indices, human capital, fiscal conditions, transportation infrastructure, and land development characteristics are considered. The results highlight the need to use a multivariate modeling system for the analysis of business counts by sector type, while also accommodating spatial dependence effects in business counts.*The authors are grateful to Lisa Macias for her help in formatting this document. Three referees provided valuable comments on an earlier version of the paper.

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This paper offers an econometric model system that simultaneously considers six dimensions of activity-travel choices in a unifying framework. The six dimensions include residential location choice, work location choice, automobile ownership, commuting distance, commute mode, and number of stops on commute tours. The paper presents the modeling methodology in detail as well as estimation results for a joint model system estimated on a data set extracted from the 2009 National Household Travel Survey.