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.

Integrated Transport/Land Use Modeling

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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.

Application of Microsimulation Tools to Large Freeway-Arterial Corridors

The video begins at 2:59.

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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Where do we go from here? Changing the state of the practice of pedestrian demand modeling

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

The video begins at 1:09.

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