This paper examines how many and the most common type of side stops a traveler or travel party makes during long-distance travel of over 100 miles or more. The research uses the 1995 American Travel Survey (ATS) because it is one of the few data sources that collects information on stops and side trips for long-distance trips. The paper utilizes two models to estimate side stop behavior: 1) an ordered probit formulation for modeling the number of side trips during long distance travel, and 2) a mixed multinomial logit formulation for modeling the most common side stop purpose during long-distance travel. A variety of variables, including trip and household characteristics, are considered in the model specification. The factors that play the largest role in determining side stop behavior are the primary purpose of the long-distance trip, whether the trip is a planned vacation or not, and the ethnicity of the travelers.

This thesis examines several nuanced issues, including equitable access, regarding long-distance intercity travel. In the United States, studies of transportation equity focus on affordable access to local destinations and basic services. The limited studies of long-distance intercity travel focus on observed demand, ignoring latent or unmet demand. Both quantitative and qualitative data are used to explore the differences between those who participate in long-distance travel and those with unmet need for it. This thesis found that the ability to participate in long-distance travel plays a role in one’s overall well-being. Undertaking long-distance trips facilitates access to opportunity for cultural and educational experiences, as well as the maintenance and creation of social capital, factors which were indicated by study participants. The first part of the thesis examines equity in access to long-distance travel between individuals by using data from a state-wide survey completed by 2,232 Vermonters for the Vermont Agency of Transportation in 2016. Five ordinal logistic regression models that approximate different levels of realized and unmet travel are used to understand how access to intercity travel differs by socioeconomic, geographic location, and household characteristics. A total of 22 percent of respondents indicated they had unmet demand at least once per year. Furthermore, there was a significant correlation between those who had unmet demand within Vermont and outside of Vermont, proxies for local and intercity travel, respectively. Income level, Internet access, and education level were found to be significant predictors of realized long-distance travel. Household size and composition, household vehicles, age, income, and self-reported urban residence were predictors of both unmet local and long-distance travel need. In addition, full-time employment was significant for local unmet need, while miles to the nearest metropolitan area was a significant predictor for longer travel needs. Models of actual travel were stronger than for unmet demand, indicating that other unmeasured predictor variables may be important, thus requiring qualitative exploration. The second part of the thesis consists of an in-depth examination using semi-structured interviews regarding intercity travel with 24 women living in Chittenden County, Vermont. In addition to the qualitative survey methods, data from a social network geography survey designed specifically for the study and an overall well-being survey were used. Interviews were coded by theme relating to travel type, barriers to travel, and impact on quality of life. A majority of participants felt long-distance travel was very important or essential to their well-being and they wished to increase the amount they did. Additionally, participants felt the need to meet with friends and family in-person, therefore necessitating long-distance travel to those who lived further away. There was also a discrepancy between the desire to meet with friends and family and how often the participants actually were able to do so. Those with higher incomes had less unmet long-distance travel need.

Long-distance and intercity travel generally make up a small portion of the total number of trips taken by an individual, while representing a large portion of aggregate distance traveled on the transportation system. While some research exists on intercity travel behavior between large metropolitan centers, this thesis addresses a need for more research on travel behavior between non-metropolitan areas and large metropolitan centers. This research specifically considers travel from home locations in northern New England, going to Boston, New York City, Philadelphia, and Washington, DC. These trips are important for quality of life, multimodal planning, and rural economies. This research identifies and quantifies factors that influence people's mode choice (automobile, intercity bus, passenger rail, or commercial air travel) for these trips. The research uses survey questionnaire data, latent factor analysis, and discrete choice modeling methods. Factors include sociodemographic, built environment, latent attitudes, and trip characteristics. The survey, designed by the University of Vermont Transportation Research Center and the New England Transportation Institute, was conducted by Resource Systems Group, Inc. in 2014, with an initial sample size of 2560. Factor analysis was used to prepare 6 latent attitudinal factors, based on 70 attitudinal responses from the survey statements. The survey data were augmented with built environment variables using geographic information systems (GIS) analysis. A set of multinomial logit models, and a set of nested logit models, were estimated for business and non-business trip mode choice. Results indicate that for this type of travel, factors influencing mode choice for both business and non-business trips include trip distance; land use; personal use of technology; and latent attitudes about auto dependence, preference for automobile, and comfort with personal space and safety on public transportation. Gender is a less significant factor. Age is only significant for non-business trips. The results reinforce the importance and viability of modeling long-distance travel from less populated regions to large metropolitan areas, and the significant roles of trip distance, built environment, personal attitudes, and sociodemographic factors in how people choose to make these trips for different purposes. Future research should continue to improve these types of long-distance mode choice models by incorporating mode specific travel time and cost, developing more specific attitudinal statements to expand latent factor analysis, and further exploring built environment variables. Improving these models will promote better planning, engineering, operations, and infrastructure investment decisions in many regions and communities across the United States which have not yet been well studied, possibly impacting levels of service.

Reliable forecasting models are needed to achieve the climate related goals in the face of increasing transport demand. Such models can predict the long-term behavioural response to policy interventions, including infrastructure investments, and thus provide valuable pre-dictions for decision makers. Contemporary forecasting models are mainly based on national travel surveys. Unfortunately, the response rates of such surveys have steadily declined, implying that the respondents become less representative of the whole population. A particular weakness is that it is likely that respondents with a high valuation of time are less willing to respond to surveys (because they have less time available for such), and therefore there is a high chance that they are underrepresented among the respondents. The valuation of time plays an important role for the cost benefit analyses of public policies including transport investments, and there is no reliable way of controlling for this uneven sampling of time preferences. Fortunately, there is simultaneously an increase in the number of signals sent between mobile phones and network antennae, and research has now reached the point where it is possible to determine not only the travel destination but also the travel mode based on mobile phone network antennae connections. The aim of this thesis is to investigate if and how mobile phone network data can be used to estimate transportation mode choice demand models that can be used for forecasting and planning. Key challenges with using this data source in the context of mode choice models are identified and met. The identified challenges include uncertainty in the choice variable, the difficulty to distinguish car and bus trips, and the lack of information about the trip purpose. In the first paper we propose three possible model formulations and analyse how the uncertainty in the choice outcome variable would play a role in the different model formulations. We also conclude that it is indeed possible to estimate mode choice demand models based on mobile phone network data, with good results in terms of behavioural interpretability and significance. In the second paper we estimate models using a nested logit structure to account for the difficulty in separating bus and car, and a latent class model specification to meet the challenge of having an unknown trip purpose. / <p><strong>Funding agencies:</strong> The research in this thesis has mainly been funded by the research projects DEMOPAN and DEMOPAN-2 within the research program Transportekonomi at The Swedish Transport Administration.</p>

Reliable forecasting models are needed to achieve the climate related goals in the face of increasing transport demand. Such models can predict the long-term behavioural response to policy interventions, including infrastructure investments, and thus provide valuable pre-dictions for decision makers. Contemporary forecasting models are mainly based on national travel surveys. Unfortunately, the response rates of such surveys have steadily declined, implying that the respondents become less representative of the whole population. A particular weakness is that it is likely that respondents with a high valuation of time are less willing to respond to surveys (because they have less time available for such), and therefore there is a high chance that they are underrepresented among the respondents. The valuation of time plays an important role for the cost benefit analyses of public policies including transport investments, and there is no reliable way of controlling for this uneven sampling of time preferences. Fortunately, there is simultaneously an increase in the number of signals sent between mobile phones and network antennae, and research has now reached the point where it is possible to determine not only the travel destination but also the travel mode based on mobile phone network antennae connections. The aim of this thesis is to investigate if and how mobile phone network data can be used to estimate transportation mode choice demand models that can be used for forecasting and planning. Key challenges with using this data source in the context of mode choice models are identified and met. The identified challenges include uncertainty in the choice variable, the difficulty to distinguish car and bus trips, and the lack of information about the trip purpose. In the first paper we propose three possible model formulations and analyse how the uncertainty in the choice outcome variable would play a role in the different model formulations. We also conclude that it is indeed possible to estimate mode choice demand models based on mobile phone network data, with good results in terms of behavioural interpretability and significance. In the second paper we estimate models using a nested logit structure to account for the difficulty in separating bus and car, and a latent class model specification to meet the challenge of having an unknown trip purpose. / <p><strong>Funding agencies:</strong> The research in this thesis has mainly been funded by the research projects DEMOPAN and DEMOPAN-2 within the research program Transportekonomi at The Swedish Transport Administration.</p>

This study contributes to the literature on national long-distance travel demand modeling by providing an analysis of households' annual destination choices and time allocation patterns for long-distance leisure travel purposes. An annual vacation destination choice and time allocation model is formulated to simultaneously predict the different destinations that a household visits and the time it spends on each of these visited destinations, in a year. The model takes the form of a Multiple Discrete-Continuous Extreme Value (MDCEV) structure (Bhat, 2005; Bhat, 2008). The model assumes that households allocate their annual vacation time to visit one or more destinations in a year to maximize the utility derived from their choices. The model framework accommodates variety-seeking in households' vacation destination choices in that households can potentially visit a variety of destinations rather than spending all of their annual vacation time for visiting a single destination. At the same time, the model accommodates corner solutions to recognize that households may not necessarily visit all available destinations. An annual vacation time budget is also considered to recognize that households may operate under time budget constraints. Further, the paper proposes a variant of the MDCEV model that avoids the prediction of unrealistically small amounts of time allocation to the chosen alternatives. To do so, the continuously non-linear utility functional form in the MDCEV framework is replaced with a combination of a linear and non-linear form. The empirical data for this analysis comes from the 1995 American Travel Survey Data, with the U.S. divided into 210 alternative destinations. The empirical analysis provides important insights into the determinants of households' leisure destination choice and time allocation patterns. An appealing feature of the proposed model is its applicability in a national, long-distance leisure travel demand model system. The annual destination choices and time allocations predicted by this model can be used for subsequent analysis of the number of trips made (in a year) to each destination and the travel choices for each trip. The outputs from such a national travel modeling framework can be used to obtain national-level Origin-Destination demand tables for long-distance leisure travel.

Travel demand models are a useful tool to assess transportation projects. Within travel demand, long-distance trips represent a significant amount of the total vehicle-kilometers travelled, in contrast to commuting trips. Consequently, they pay a relevant role in the economic, social and environmental impacts of transportation. This paper describes the development of a microscopic long-distance travel demand model for the Province of Ontario (Canada) and analyzes the sensitivity to the implementation of a new high speed rail corridor. Trip generation, destination choice and mode choice models were developed for this research. Multinomial logit models were estimated and calibrated using the Travel Survey for Residents in Canada (TSRC). It was complemented with location-based social network data from Foursquare, improving the description of activities and diverse land uses at the destinations. Level of service of the transit network was defined by downloading trip time, frequency and fare using the planning service Rome2rio. New scenarios were generated to simulate the impacts of a new high speed rail corridor by varying rail travel times, frequencies and fares of the rail services. As a result, a significant increase of rail modal shares was measured, directly proportional to speed and frequency and inversely proportional to price.