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Why model pedestrians?
A new predictive tool for estimating pedestrian demand has potential applications for improving walkability. By forecasting the number, location and characteristics of walking trips, this tool allows for policy-sensitive mode shifts away from automobile travel.
There is growing support to improve the quality of the walking environment and make investments to promote pedestrian travel. Despite this interest and need, current forecasting tools, particularly regional travel demand models, often fall short. To address this gap, Oregon Metro and NITC researcher Kelly Clifton worked together to develop...
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Summary: A growing concern related to large-truck crashes has increased in the State of Texas in recent years due to the potential economic impacts and level of injury severity that can be sustained. Yet, studies on large truck involved crashes highlighting the contributing factors leading to injury severity have not been conducted in detail in the State of Texas especially for its interstate system. In this study, we analyze the contributing factors related to injury severity by utilizing Texas crash data based on a discrete outcome based model which accounts for possible unobserved heterogeneity related to human, vehicle and road-environment. We estimate a random parameter logit model (i.e., mixed logit) to predict the likelihood of five standard injury severity scales commonly used in Crash Records Information System (CRIS) in Texas – fatal, incapacitating, non-incapacitating, possible, and no injury (property damage only). Estimation findings indicate that the level of injury severity outcomes is highly influenced by a number of complex interactions between factors and the effects of the some factors can vary across observations. The contributing factors include drivers’ demographics, traffic flow condition, roadway geometrics, land use and temporal...
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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.
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Summary: Since about 2008, the planning world has been experiencing a paradigm shift that began in places like California and Oregon that have adopted legislation requiring the linking of land use and transportation plans to outcomes, specifically to the reduction of greenhouse gases (GHGs). In response to this need, Calthorpe Associates has developed a new planning tool, called UrbanFootprint, on a fully Open Source platform (i.e. Ubuntu Linux, PostGIS, PostGreSQL, etc.). As a powerful and dynamic web and mobile-enabled geo-spatial scenario creation and modeling tool with full co-benefits analysis capacity, UrbanFootprint has great utility for urban planning and research at multiple scales, from general plans, to project assessments, to regional and state-wide scenario development and analysis. Scenario outcomes measurement modules include: a powerful ‘sketch’ transportation model that produces travel and emissions impacts; a public health analysis engine that measures land use impacts on respiratory disease, obesity, and related impacts and costs; climate-sensitive building energy and water modeling; fiscal impacts analysis; and greenhouse gas and other emissions modeling.
Bio: Garlynn Woodsong is a Project Manager in the regional and large-...
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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.