Nov 25, 2014

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The video begins at 2:53.

Abstract: The concept of accessibility has long been theorized as a principal determinant of household residential choice behavior. Research on this influence is extensive but the empirical results have been mixed, with some research suggesting that accessibility is becoming a relatively insignificant influence on housing choices. Further, the measurement of accessibility must contend with complications arising from the increasing prevalence of trip-chains, non-work activities, and multi-worker households, as well as reconcile person-specific travel needs with household residential decisions. This paper contributes to the literature by addressing the gap framed by these issues and presents a novel residential choice model with three main elements of innovation. First, it operationalized a time-space prism (TSP) accessibility measure, which the authors believe to be the first application of its kind in a residential choice model. Second, it represented the choice sets in a building-level framework, the lowest level of spatial disaggregation available for modeling residential choices. Third, it explicitly examined the influence of non-work accessibility at both the local- and person-...

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Nov 25, 2014

Abstract: We study the impact on productivity of a specific operating practice currently adopted by some demand responsive transit (DRT) providers. We investigate the effect of using a zoning vs. a no-zoning strategy on performance measures such as total trip miles, deadhead miles and fleet size. It is difficult to establish closed form expressions to assess the impact on the performance measures of a specific zoning practice for a real transportation network. Thus, we conduct this study through a simulation model of the operations of DRT providers on a network based on data for DRT service in Los Angeles County.

The video begins at 2:26.

Nov 25, 2014

No archived materials are available for this presentation.

Nov 25, 2014

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.

Nov 25, 2014

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Abstract: Existing regional travel forecasting systems are not typically set up to forecast usage of bicycle infrastructure and are insensitive to bicyclists' route preferences in general. We collected revealed preference, GPS data on 162 bicyclists over the course of several days and coded the resulting trips to a highly detailed bicycle network model. We then use these data to estimate bicyclist route choice models. As part of this research, we developed a sophisticated choice set generation algorithm based on multiple permutations of labeled path attributes, which seems to out-perform comparable implementations of other route choice set generation algorithms. The model was formulated as a Path-Size Logit model to account for overlapping route alternatives. The estimation results show compelling intuitive elasticities for route choice attributes, including the effects of distance and delay; avoiding high-volumes of vehicular traffic, stops and turns, and elevation gain; and preferences for certain bike infrastructure types, particularly at bridge crossings and off-street paths. Estimation results also support segmentation by commute versus non-commute trip types, but are less clear when it comes to gender. The final model will be implemented as part of the regional travel forecasting system for Portland, Oregon, U.S.A.
Nov 25, 2014

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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.

Nov 24, 2014

Kristie Gladhill, Transportation Modeler, on Modeling Safety and Urban Form.

The video begins at 1:57.

Nov 24, 2014

The video starts at 0:58.

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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...

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Nov 24, 2014

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...

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