Lead Presenter Biography

Andrew Rohne has 20 years of experience in travel model development, data collection, analysis, and analytics. Andrew has recently completed the implantation of the ActivitySim activity-based model in Washington DC and Minneapolis-St. Paul.

Andrew has significant experience with non-traditional applications of passive data and non-traditional methods, including the use of passive data for analyzing traffic movements during the Great American Eclipse, imputing light commercial vehicles from passive data using machine learning methods, and using the genetic algorithm to calibrate destination choice models.

Prior to working for RSG, Andrew managed the transportation modeling department at OKI Regional Council, he led several successful survey data collection and traffic data collection projects from inception to implementation in the model and led several model update and application efforts.

Co-Authors

Presentation Description

Activity-based travel demand forecasting models provide a significantly higher fidelity in transportation decisions than trip-based models, but much of this fidelity is untested. ActivitySim is an open-source activity-based travel demand modeling software developed by a consortium of agencies and currently being implemented in several regions in the United States. In three recent ActivitySim implementations, RSG and agency staff have undertaken sensitivity tests as a way to evaluate the model’s response to changes in population, land use, highway and transit networks, transportation pricing, and telecommuting behavior changes. These three implementations are the regional travel models developed by the Metropolitan Washington Council of Governments (MWCOG) in Washington, D.C.; the Southeast Michigan Council of Governments (SEMCOG) in Detroit, Michigan; and the Metropolitan Council (Met Council), in Minneapolis-St. Paul Minnesota. The results from these tests are being used to guide further development of each of these travel models with the intent to improve the response of these models to non-traditional scenarios.
For MWCOG, five tests were conducted:
• Increased Auto Operating Costs for the Entire Region
• Increased Telecommuting into DC
• Closing Arlington Memorial Bridge to Auto and Truck Traffic
• Doubling the Frequency of High-Capacity Transit Services
• Increased Peak-Period Toll Rates
For SEMCOG, three tests were conducted:
• Adding 5,000 employees in Corktown (just west of downtown Detroit)
• Adding Ann Arbor to Detroit Commuter Rail
• Changes in Household Income (+/- 25% and +/- 50%)
For Met Council, four tests were conducted:
1. Increased Auto Operating Costs
2. Introduction of New Transit Services
3. Increased Telecommute
4. Discounted Transportation Network Company (TNC)/Ride-Hailing Costs for the lowest income group
This presentation will discuss the details and methodology of each test, how the test was implemented, the expected results of the test, the actual results of each test, and next steps taken in the development of the models based on the results of the tests. 

Presentation File