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No archived materials are available for this presentation.
The video begins at 4:15.
Abstract: The California High-Speed Rail Ridership and Revenue Forecasting Model is a state-of-the-practice transportation model designed to portray what future conditions might look like in California with and without a high-speed train. The model was developed by Cambridge Systematics, Inc., and took roughly two years to complete. The resulting ridership and revenue forecasts provided, and continue to provide, sound information for planning decisions for high-speed rail in California. This presentation briefly describes the underlying model that was developed to generate the ridership and revenue forecasts along with summaries of ridership forecasts from published reports.
Abstract: We propose to decompose residential self-selection by understanding its formation process. We take a life course perspective and postulate that locations experienced early in life have a lasting effect on our locational preferences in life. In other words, what was experienced spatially is a key factor contributing to our residential self-selection and our preferences in residential locations are formed long before our own self-selection begins. We further hypothesize that prior locational influence interacts with period effect such that the same location experienced in different periods may have distinct effects. Using an empirically collected dataset in the New York Metropolitan Region, we estimated a series of models to test these hypotheses. The results demonstrate that prior locational influence precedes residential self-selection. Furthermore, we show a variety-seeking behavioral pattern resulted from locations experienced during adolescence.
Kristie Gladhill, Transportation Modeler, on Modeling Safety and Urban Form.
The video begins at 1:57.
The video starts at 0:58.
Abstract: Walking and bicycling are being promoted as transportation options that can increase the livability and sustainability of communities, but the automobile remains the dominant mode of transportation in all United States metropolitan regions. In order to change travel behavior, researchers and practitioners need a greater understanding of the mode choice decision process, especially for walking and bicycling.
This presentation will summarize dissertation research on factors associated with walking and bicycling for routine travel purposes, such as shopping. More than 1,000 retail pharmacy store customers were surveyed in 20 San Francisco Bay Area shopping districts in fall 2009, and 26 follow-up interviews were conducted in spring and summer 2010. Mixed logit models showed that walking was associated with shorter travel distances, higher population densities, more street tree canopy coverage, and greater enjoyment of walking. Bicycling was associated with shorter travel distances, more bicycle facilities, more bicycle parking, and greater enjoyment of bicycling. Respondents were more likely to drive when they perceived a high risk of crime, but automobile use was discouraged by higher employment densities...Read more
The video begins at 1:47.
Abstract: In transportation planning and engineering, market segments or groups of individuals with varying attitudes and travel behavior are often identified in order to define a set of policies and strategies targeted at each segment. Examples include residential location choice studies, electric vehicle adoption and the marketing of public transit options. Defining market segments is common in the marketing literature, typically based on observed socioeconomic characteristics, such as gender and income. However, in addition to these characteristics, travelers may also be segmented based on variations in their observed travel and activity patterns. The activity-based approach to travel demand analysis acknowledges the need to analyze the travel patterns of individuals, conceptualized as a trip chain or tour, as opposed to individual trip segments. This has implications for identifying markets segments based on travel patterns which needs to distinguish between the sequencing and timing of travel choices and activities, in addition to the actual travel choices and activities. One approach that holds promise is pattern recognition theory which has wide applications in image analysis, speech recognition and physiological signal processing. In this study, pattern recognition methods are applied to observed daily travel and activity patterns from Oregon to identify travel market...Read more
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Abstract: Metro's Transportation Research and Modeling Services Program's (TRMS) is responsible for the development, maintenance, and application of travel demand models for application in long-range planning efforts in the Portland metropolitan region. Representation of traffic -- both vehicular and transit -- plays an integral role in the travel demand modeling process. Complex software is required to assign vehicles and transit users to transportation networks to determine viable options available to travelers, costs associated with those options, and sets of routes by which travelers might navigate their trips. TRMS's current static assignment model has traditionally sufficed for use with Metro's four-step travel demand model. However, static assignments have well documented limitations that preclude the ability of the analyst to answer complex policy questions, especially those related to green house gas emissions, congestion, and transportation network reliability. In addition, static assignments cannot fulfill a need for small duration travel time increments required by the next generation activity-based models. The shortcomings of the static assignment necessitates TRMS's development of regional dynamic traffic assignment (DTA) models. The resolution of these models allows for continuous modeling of traffic over an analysis period, which allows the analyst to capture...Read more
The video begins at 3:30.
Abstract: We all use abstractions of reality to help understand the world around us, synthesize knowledge, and to predict the consequences of our actions. These range from ad hoc mental models to highly complex mathematical creations. In this discussion we'll examine the motivations for building formal models, with particular focus on the types of models that will be explored in this course. Several different modeling approaches will be compared, along with the strengths and limitations of each. Some important questions that builders and consumers of models should ask will be covered, as well as ideas for building more useful and informative models. A discussion on how to judge the validity of a model will round out the discussion.
Speaker Bio: Rick Donnelly has over 25 years of experience in the modeling and simulation of transportation systems, from the urban to national level. His current interests include agent-based modeling of freight and logistics, integrated land use-transportation models, and dynamic network modeling. Rick leads the travel modeling and simulation practice at Parsons Brinckerhoff, an international civil and transportation engineering consultancy. He is also a senior fellow at the University of Melbourne, where he earned his doctorate in engineering, and a visiting scholar at Los Alamos National Laboratory.