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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.

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.

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.

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 environmental engineering from...

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New Travel Demand Models



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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Topic: Understanding Where We Live and How We Travel: The Development of an Online Visual Survey Tool and Pilot Studies Evaluating Preferences in Residential Neighborhood Choice and Commute

Summary: Understanding changing residential preferences—especially as they are represented within land use and travel demand models—is fundamental to understanding the drivers of future housing, land use and transportation policies. As communities struggle to address a rising number of social challenges with increasing economic uncertainty, transportation and land use planning have become increasingly centered on assumptions concerning the market for residential environments and travel choices. In response, an added importance has been placed on the development of toolkits capable of providing a robust and flexible understanding of how differing assumptions contribute to a set of planning scenarios and impact future residential location decisions.

In this presentation, we discuss one such improvement that can be added to the transportation planning toolkit: an innovative visual online survey tool. This tool was developed to provide a means for...

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