Residential Choice Locations - A Search in the Mirror of the Past

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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.

Integrated Transport/Land Use Modeling

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Managed Lanes in Orlando, FL

Special Seminar: Room 315 of the Maseeh College of Engineering & Computer Science on the Portland State University campus.

Florida’s Turnpike Enterprise has completed planning studies to forecast the revenue earning potential of tolled special use lanes along Interstates in Florida. The tolled special use lanes or “Managed Lanes” will be contained within the interior of the Interstates' highway corridors. The Managed Lanes concept has been incorporated into larger widening projects in Central and South Florida, which is under development by the Florida Department of Transportation. The presentation will focus on the approach and methodology for estimating traffic and revenue for Express Toll Lanes in an existing limited access corridor. The core content is the required data, traffic modeling efforts, and how the results are used by the Finance Department to estimate potential revenues.

Bio: Jack Klodzinski received his Bachelors’, Master’s and Ph.D. in Civil Engineering from the University of Central Florida where his focus was on toll road operations. He is now the Travel Forecast Manager at Florida’s Turnpike for the URS Corporation where his main focus is on traffic forecasting for toll facilities. He works with a team of modelers to produce toll traffic forecasts used in roadway design, operations, or future revenue estimates. Jack also stays active with UCF as a Graduate Faculty Scholar for the Department of Civil, Environmental, and Construction...

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Developing a Bicyclist Route Choice Model Using GPS Data

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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.

Application of Microsimulation Tools to Large Freeway-Arterial Corridors

The video begins at 2:59.

Modeling and Visualizing Sustainability in the Bay Area

The video begins at 4:30.

The San Francisco Bay Area, like other metropolitan regions in California, is in the process of developing regional plans to reduce greenhouse gas emissions in response to state legislation that sets targets for such reduction, and prescribes that Metropolitan Planning Organizations develop Sustainable Communities Strategies that leverage changes in land use patterns in combination with transportation investments, that will meet those targets. This talk describes the land use modeling that is being used, in combination with the activity-based transportation model system at the Metropolitan Transportation Commission, to analyze alternative combinations of land use policies and transportation policies. It also will demonstrate visualization technology that has been developed to facilitate community engagement in the process

Speaker Bio: Paul Waddell is Professor and Chair of the City and Regional Planning Department at the University of California, Berkeley. He teaches and conducts research on land use and transportation modeling and planning. He designed and leads the development of the UrbanSim land use modeling platform, now being used in metropolitan planning organizations across the U.S., and in research projects throughout the world.

Modeling High Speed Rail Ridership and Revenue in California: An Overview

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.

Modeling Freight & Passenger Rail Capacity in the Interstate 5 Trade Corridor

The video begins at 1:09.

New Travel Demand Models

Webinar: New Travel Demand Modeling for our Evolving Mobility Landscape

PRESENTATION ARCHIVE

OVERVIEW

Conventional four-step travel demand models are used by nearly all metropolitan planning organizations (MPOs), state departments of transportation, and local planning agencies, as the basis for long-range transportation planning in the United States. A flaw of the four-step model is its relative insensitivity to the so-called D variables. The D variables are characteristics of the built environment that are known to affect travel behavior. The Ds are development density, land use diversity, street network design, destination accessibility, and distance to transit. In this seminar, we will explain how we developed a vehicle ownership model (car shedding model), an intrazonal travel model (internal capture model), and mode choice model that consider all of the D variables based on household travel surveys and built environmental data for 32, 31, and 29 regions, respectively, validates the models, and demonstrates that the models have far better predictive accuracy than Wasatch Front Regional Council (WFRC)/Mountailand Association of Governments’ (MAG) current models.

In this webinar, researchers Reid Ewing and Sadegh Sabouri will...

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Evaluating the Feasibility of Passive Travel Survey collection in a Complex Urban Environment: Lessons Learned from the New York City Case Study

The video begins at 1:34.

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Abstract: The combination of increasing challenges in administering household travel surveys as well as advances in global positioning systems (GPS) and geographic information systems (GIS) technologies motivated this project. It tests the feasibility of using a passive travel data collection methodology in a complex urban environment, by developing GIS algorithms to automatically detect travel modes and trip purposes. The study was conducted in New York City where the multi-dimensional challenges include urban canyon effects, an extremely dense and diverse set of land use patterns, and a complex transit network. Our study uses a multi-modal transportation network, a set of rules to achieve both complexity and flexibility for travel mode detection, and develops procedures and models for trip end clustering and trip purpose prediction. The study results are promising, reporting success rates ranging from 60% to 95%, suggesting that in the future, conventional self-reported travel surveys may be supplemented, or even replaced, by passive data collection methods.

Speaker Bio: Cynthia Chen is an associate professor in the department of civil and environmental engineering at the University of Washington. She obtained her Ph.D in civil and...

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