If you weren’t one of the 10,000 people who attended the Transportation Research Board’s Annual Meeting in January, there are fifty students and twenty faculty for PSU, UO, OSU and OIT who can tell you what they learned there. OTREC's bright yellow lanyards made our presence especially visible! PSU student Brian Davis blogged about his experience, OTREC’s Jon Makler was interviewed in a local newspaper, and the Oregon “delegation” at the conference was covered by both local and national blogs. Team OTREC filed some daily debriefs, highlighting presentations on topics such as federal stimulus investments in Los Angeles and Vermont’s efforts to address their transportation workforce crisis with returning military veterans (as well as the...
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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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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: 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.
For the third year, we're hosting our two-part data science course in Portland, OR. You can register for one part or the other– or attend both at a discount: Data Science Course - Part 2: Intermediate Scientific Computing for Planners, Engineers, and Scientists
Did you ever feel you are “drinking from a hose” with the amount of data you are attempting to analyze? Have you been frustrated with the tedious steps in your data processing and analysis process and thinking, “There’s gotta be a better way to do things”? Are you curious what the buzz of data science is about? If any of your answers are yes, then this course is for you.
Classes will all be hands-on sessions with lecture, discussions and labs. Participants can choose to sign up for one or both courses. For more information, download the syllabus (PDF). This course was developed as part of a NITC education project: Introduction to Scientific Computing for Planners, Engineers, and Scientists.
Agenda: Part One -...
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Summary: Where and when does overcrowding happen on TriMet's bus network? Which routes have the best on-time performance? Portland State University and TriMet have collaborated to make this kind of data available to anybody through Portal, PSU's transportation data archive for the Portland/Vancouver region. This presentation will cover the use of General Transit Feed Specification (GTFS) data for mapping TriMet’s performance data and the development of Portal’s innovative transit application. In the MAP-21 era of performance management, see how tools like Portal can support enhanced agency decision-making as well as community engagement.
Bio: Jon Makler researches and teaches about transportation planning and engineering at Portland State University. His research portfolio centers on intelligent transportation systems, including how they can be harnessed to benefit the environment and how the data they generate can support operational strategies and planning decisions. Since moving to Oregon 9 years ago, he has worked at Metro, the City of Portland and OTREC, the federally-funded research center housed at PSU. His previous employers were the North Jersey Transportation Planning Authority, the Harvard Kennedy School, IBI Group and Sarah...
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