Uncertainty in Travel Forecasting: Exploratory Modeling and Analysis TMIP-EMAT: A Desk Reference

Details

• Creators:
  Lemp, Jason ; Rossi, Thomas ; Newman, Jeffrey ; Copperman, Rachel B.
• Corporate Creators:
  Cambridge Systematics, Inc.
• Contributors:
  Sun, Sarah
• Corporate Contributors:
  United States. Federal Highway Administration. Office of Planning, Environment, and Realty
• Subject/TRT Terms:
  • Traffic forecasting
  • Travel demand
  • Highway corridors
  • Traffic models
  • Transportation planning
  • Transportation policy
• Publication/ Report Number:
  FHWA-HEP-21-032
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Contracting Officer:
  Sun, Sarah
• Corporate Publisher:
  United States. Federal Highway Administration. Office of Planning, Environment, and Realty
• Abstract:
  Traditionally, travel forecasting models have been used to provide single point predictions. That is, a single future scenario is developed, and the model is applied to that scenario. This approach, however, ignores the deep uncertainty that exists in future land use, demographic, and transportation systems inputs, not to mention the uncertainty that exists in the model itself. More importantly, transportation policy decisions made on the basis of such model outputs may be misguided and ineffective. This report demonstrates and motivates the use of travel forecasting models in an exploratory manner that accounts for the inherent uncertainties of the future. Specifically, this report describes the approach that can be supported by a new planning and modeling tool: the Travel Model Improvement Program Exploratory Modeling and Analysis Tool (TMIP-EMAT) that has been developed to facilitate the use of exploratory techniques with travel forecasting models.
• Format:
  PDF
• Funding:
  DTFH61-16-D00016L
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/58168/dot_58168_DS1.pdf
• File Type:
  [PDF - 5.39 MB]
• Collection(s):
  Federal Highway Administration
• Main Document Checksum:
  urn:sha-512:f86b2023eda72e657dcb4dd657c40b14edfb2dd6ba7f6dea9103463947722afe83a13ee99e521d1b6bda271a57ca26752e0324a61f6daa8944db1e6bb8270d7b