The inherent uncertainty in travel forecasting models -- arising from errors in input data, parameter estimation, or model formulation -- is receiving increasing attention from the scholarly and practicing community. In this research, we investigate the variance in forecasted traffic volumes resulting from varying the mode and destination choice parameters in an advanced trip-based travel demand model. Using Latin hypercube sampling to construct several hundred combinations of parameters across the plausible parameter space, we introduce substantial changes to mode and destination choice logsums and probabilities. However, the aggregate effects of of these changes on forecasted traffic volumes is small, with a variance of approximately 1 percent on high-volume facilities. Thus, parameter uncertainty does not appear to be a significant factor in forecasting traffic volumes using transportation demand models.

Regional growth caused the emergence of traffic congestion and pollution in the past few decades, which have started to affect small urban areas. These problems are not only related to transportation system design but also to land use planning. There has been growing recognition that the relationship between land use and transportation needs to be understood and analyzed in a consistent and systematic way. Integrated urban models have recently been introduced and implemented in several metropolitan areas to systematically examine the relationship between land use and transportation. The general consensus in the field of integrated urban models is that each model has its own limitations and assumptions because they are each designed for different application purposes. This dissertation proposes a new type of methodology to integrate the regression-based land use model and the combined trip distribution-assignment transportation model that can be applied to both metropolitan areas and small urban areas. The proposed integrated land use and transportation model framework has three components: the regression-based land use model, the combined trip distributionassignment transportation model, and the interaction between these two models. The combined trip distribution-assignment model framework provides the platform to simultaneously integrate the transportation model with the land use model. The land use model is developed using an easy-to-implement method in terms of correlation and regression analysis. The interaction between the land use model and the transportation model is examined by two model frameworks: feedback model framework and simultaneous model framework. The feedback model framework solves the land use model and the transportation model iteratively. The simultaneous model framework brings the land use model and the transportation models into one optimization program after introducing the used path set. Both the feedback model and the simultaneous model can be solved to estimate link flow, origin-destination (OD) trips, and household distribution with the results satisfying network equilibrium conditions. The proposed integrated model framework has an “affordable and easy-toimplement” land use model; it can be performed in small urban areas with limited resources. The model applications show that using the proposed integrated model framework can help decision-makers and planners in preparing for the future of their communities.

This research contributes to a very active body of literature on the application of Automated Data Collection Systems (ADCS) and openly shared data to public transportation planning. It also addresses the interaction between transit demand and land use patterns, a key component of generating time-varying origin-destination (O-D) matrices at a route level. An origin-destination (O-D) matrix describes the travel demand between two different locations and is indispensable information for most transportation applications, from strategic planning to traffic control and management. A transit passenger's O-D pair at the route level simply indicates the origin and destination stop along the considered route. Observing existing land use types (e.g., residential, commercial, institutional) within the catchment area of each stop can help in identifying existing transit demand at any given time or over time. The proposed research addresses incorporation of an alighting probability matrix (APM) - tabulating the probabilities that a passenger alights at stops downstream of the boarding at a specified stop - into a time-varying O-D estimation process, based on the passenger's trip purpose or activity locations represented by the interactions between transit demand and land use patterns. In order to examine these interactions, this research also uses a much larger dataset that has been automatically collected from various electronic technologies: Automated Fare Collection (AFC) systems and Automated Passenger Counter (APC) systems, in conjunction with other readily available data such as Google's General Transit Feed Specification (GTFS) and parcel-level land use data. The large and highly detailed datasets have the capability of rectifying limitations of manual data collection (e.g., on-board survey) as well as enhancing any existing decision-making tools. This research proposes use of Google's GTFS for a bus stop aggregation model (SAM) based on distance between individual stops, textual similarity, and common service areas. By measuring land use types within a specified service area based on SAM, this research helps in advancing our understanding of transit demand in the vicinity of bus stops. In addition, a systematic matching technique for aggregating stops (SAM) allows us to analyze the symmetry of boarding and alightings, which can observe a considerable passenger flow between specific time periods and symmetry by time period pairs (e.g., between AM and PM peaks) on an individual day. This research explores the potential generation of a time-varying O-D matrix from APC data, in conjunction with integrated land use and transportation models. This research aims at incorporating all valuable information - the time-varying alighting probability matrix (TAPM) that represents on-board passengers' trip purpose - into the O-D estimation process. A practical application is based on APC data on a specific transit route in the Minneapolis - St. Paul metropolitan area. This research can also provide other practical implications. It can help transit agencies and policy makers to develop decision-making tools to support transit planning, using improved databases with transit-related ADCS and parcel-level land use data. As a result, this work not only has direct implications for the design and operation of future urban public transport systems (e.g., more precise bus scheduling, improve service to public transport users), but also for urban planning (e.g., for transit oriented urban development) and travel forecasting.

In recent years, various researches are under progress to enhance the quality of the travel survey. These researches were mainly performed with the aid of GPS technology. Initially the researches were mainly focused on the vehicle travel mode due to the availability of GPS technology in vehicle. But, nowadays due to the accessible of GPS devices for personal uses, researchers have diverted their focus on personal mobility in all travel modes. This master’s thesis aimed at developing a mechanism to extract one type of travel survey information particularly travel mode from collected GPS dataset. The available GPS dataset is collected for travel modes of walk, bike, car, and public transport travel modes such as bus, train and subway. The developed procedure consists of two stages where the first is the dividing the track trips into trips and further the trips into segments by means of a segmentation process. The segmentation process is based on an assumption that a traveler switches from one transportation mode to the other. Thus, the trips are divided into walking and non walking segments. The second phase comprises a procedure to develop a classification model to infer the separated segments with travel modes of walk, bike, bus, car, train and subway. In order to develop the classification model, a supervised classification method has been used where decision tree algorithm is adopted. The highest obtained prediction accuracy of the classification system is walk travel mode with 75.86%. In addition, the travel modes of bike and bus have shown the lowest prediction accuracy. Moreover, the developed system has showed remarkable results that could be used as baseline for further similar researches.

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>

The major transit systems operating in San Francisco are San Francisco Municipal (MUNI), Bay Area Rapid Transit (BART), and Caltrain. The system of interest for this paper is MUNI, in particular the bus and light rail systems. During the past decade transit ridership in the area has experienced diverging growth, with bus ridership declining while rail ridership is growing significantly (Erhardt et al. 2017). Our data show that between 2009 and 2016, MUNI rail ridership increases from 146,000 to 171,400, while MUNI bus ridership decreases from 520,000 to 450,000. Direct ridership models (DRMs) are used to determine what factors are influencing MUNI light rail and bus ridership. The DRMs predict ridership fairly well, within 10% of the observed change. However, the assumption of no multi-collinearity is voided. Variables, such as employment and housing density, are found to be collinear. Fixed-effects panel models are used to combat the multi-collinearity issue. Fixed-effects panel models assign an intercept to every stop, so that any spatial correlation is removed. A transportation network company, Uber and Lyft, variable is introduced (TNC) to the panel models, to quantify the effect they have on MUNI bus and light rail ridership. The addition of a TNC variable and elimination of multi-collinearity helps the panel models predict ridership better than the daily and time-of-day DRMs, both within 5% of the observed change. TNCs are found to complement MUNI light rail and compete with MUNI buses. TNCs contributed to a 7% growth in light rail ridership and a 10% decline in bus ridership. These findings suggest that the relationship TNCs have with transit is complex and that the modes cannot be lumped together.

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.

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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.