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Transportation

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Portland State University

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2019

Transportation -- Planning -- Statistical methods

Articles 1 - 4 of 4

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Key Enhancements To The Wfrc/Mag Four-Step Travel Demand Model, Reid Ewing, Sadegh Sabouri, Keunhyun Park, Torrey Lyons, Guang Tian Dec 2019

Key Enhancements To The Wfrc/Mag Four-Step Travel Demand Model, Reid Ewing, Sadegh Sabouri, Keunhyun Park, Torrey Lyons, Guang Tian

TREC Final Reports

In a National Transit Institute course on “Coordinating Land Use and Transportation,” co-taught by Robert Cervero, Uri Avin, and the PI on this project, the analytic tools session began with a hypothetical: assume that all households, jobs, and other trip generators are concentrated in a walkable village rather than segregated by use and spread across a traffic analysis zone in the standard suburban fashion. The instructor then asks: How would the outputs of conventional four-step travel demand models differ between these two future land use scenarios. The answer, to most participants’ surprise, was “Not at all.” Conventional four-step travel demand …


Developing Data, Models, And Tools To Enhance Transportation Equity, Amy Lubitow, Raoul Liévanos, Julius Mcgee, Erika Carpenter Sep 2019

Developing Data, Models, And Tools To Enhance Transportation Equity, Amy Lubitow, Raoul Liévanos, Julius Mcgee, Erika Carpenter

TREC Final Reports

This project used a mixed-methods approach to data collection and analysis in the Portland, OR, metropolitan area. It used quantitative techniques, the 2010 Decennial Census, and the 2011 Oregon Household Activity Survey to assess how accurately travel survey data represent minority populations. These groups are of primary interest due to their status as protected classes under Title VI of the Civil Rights Act, and accurate data on the mobility patterns of these groups is essential to equitable transportation planning. Qualitative focus groups with residents in the Portland metropolitan area investigated better methods for building equity into transportation surveys using community …


Transferability & Forecasting Of The Pedestrian Index Of The Environment (Pie) For Modeling Applications, Kelly Clifton, Jamie Orrego-Onate, Patrick Allen Singleton, Robert J. Schneider Mar 2019

Transferability & Forecasting Of The Pedestrian Index Of The Environment (Pie) For Modeling Applications, Kelly Clifton, Jamie Orrego-Onate, Patrick Allen Singleton, Robert J. Schneider

TREC Final Reports

This project focuses on making our measures, models, and methods more transferable to other locations. Specifically, we re-evaluate, compare and test our pedestrian index of the environment (PIE) measure using data resources more commonly available to planning agencies across the country. Next, we test the results of PIE and its input data in models of pedestrian mode choice for stability of estimation results within a region (intraregional) and between regions (interregional). This research is the next logical step in the MoPeD’s enhancement and is critical to enabling its utility beyond the Portland region. The results of this project show that …


New Pedestrian Modeling Tools: Growing Beyond The Portland Region, Kelly Clifton, Jamie Orrego-Onate, Patrick Allen Singleton, Robert J. Schneider Mar 2019

New Pedestrian Modeling Tools: Growing Beyond The Portland Region, Kelly Clifton, Jamie Orrego-Onate, Patrick Allen Singleton, Robert J. Schneider

TREC Project Briefs

This project focuses on making our measures, models, and methods more transferable to other locations. Specifically, we re-evaluate, compare and test our pedestrian index of the environment (PIE) measure using data resources more commonly available to planning agencies across the country. Next, we test the results of PIE and its input data in models of pedestrian mode choice for stability of estimation results within a region (intraregional) and between regions (interregional). This research is the next logical step in the MoPeD’s enhancement and is critical to enabling its utility beyond the Portland region. The results of this project show that …