Recording: https://connectdot.connectsolutions.com/p609mzn3nyf/
Date: December 14, 2016
Description: basic part of travel demand model validation is running the model for a “base year” and comparing the outputs to observed data such as traffic counts, travel times and speeds, transit ridership, and other measures of travel demand. The Federal Highway Administration (FHWA) recently completed a project to provide information for agencies performing this part of the validation process, sometimes known as dynamic validation. The models for two U.S. metropolitan areas, Baltimore and Cincinnati, were chosen as case studies for this work, and the agencies responsible for these models, the Baltimore Metropolitan Council and the Ohio-Kentucky-Indiana Regional Council of Governments, provided the model files and data. For each region, each of the current and previous model versions were run for the base year for that model, and for the base year of the other model. This means that the previous model was run for the base year and a forecast year, and the current model was run for the base year and a backcast year.
The study demonstrated that backcasting and forecasting can be challenging, but several lessons and recommendations for modeling practitioners became evident:
Presenters: Thomas F. Rossi is a Principal of Cambridge Systematics with over 30 years of management experience in travel demand modeling and transportation planning. He has developed and applied travel demand models throughout the U.S., including conventional trip based models, advanced activity based models, and integrated transportation demand-supply models. He has been an expert advisor to federal agencies and metropolitan planning organizations (MPO) in the development of travel models and survey data collection efforts. Mr. Rossi has worked with the U.S. DOT for over 20 years conducting research, authoring reference documents, and developing and teaching training courses on travel demand modeling. He has been the Principal Investigator on several national research projects.
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