Federal Highway Administration’s Transportation Planning for Uncertain Times.
Exploratory Modeling for Policy Analysis - Steven Bankes
The use of scenarios in transport modelling and appraisal - Charlene Rohr & Richard Batley
Do you have slides, white papers, or other content that has been useful to describe planning under uncertainty? Share them here!
This game has been presented in several conferences and at individual agencies to great success. Example slides and summaries are below.
The slide deck prepared by the original game creators
The slide deck presented at MoMo 2025 that walks through the game step by step, along with the game board
Coming soon…
TMIP-EMAT is an open-source Python framework developed under FHWA’s Travel Model Improvement Program designed to bridge traditional travel forecasting models with DMDU methodologies. Rather than running a heavy regional model for just one or two scenarios, TMIP-EMAT automates experimental design across wide parameter spaces and builds machine-learning metamodels (surrogates). These surrogates rapidly approximate core model outputs, allowing planners to explore thousands of policy levers and uncertain future conditions in seconds.
TMIP-EMAT is not being actively supported by FHWA, but is in use. Responses to a 2024 survey of 14 users is available here.
VisionEval is an open-source, multi-agency strategic planning framework built in R for rapid scenario evaluation and high-level policy analysis. Structurally designed as a “disaggregate demand / aggregate supply” system, VisionEval models synthetic households and travel behavior in detail without requiring computationally intensive network assignment. This lightweight architecture allows agencies to evaluate hundreds of land-use, pricing, technology, and investment combinations in minutes.
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