Travel demand modeling is inherently associated with uncertainty. The land use and network inputs that go into future scenario forecasts are uncertain, as are the form and parameter values of the statistical models that comprise a travel demand modeling system. In spite of this uncertainty, the practice in travel demand forecasting has been to generate a point estimate of highway loads, transit ridership, and other critical decision-making statistics. During the past few years, the modeling community has been highlighting the need to move away from treating model outputs as point forecasts and moving towards explaining them as ranges of possible outcomes with associated confidence intervals. With the COVID-19 pandemic disrupting travel and activity patterns of people across the globe, the importance of highlighting the uncertainty around travel model forecasts is higher than ever.
Modeling practitioners today are in need of the resources and guidance to practically model uncertainty. A number of academic studies have been published over the last decade in transportation research journals (see reference list) calling attention to the problem. More recently, FHWA has supported the development of a tool called the “Exploratory Modeling and Analysis Tool” or EMAT through an online community for the Travel Model Improvement Portal (TMIP). TMIP-EMAT is a methodological approach to exploratory modeling and analysis. It provides a window to rigorous analytical methods for handling uncertainty and making well informed decisions using travel forecasting models of all types. It is designed to integrate with and enhance an existing transportation model or tool to perform exploratory analysis of a range of possible scenarios. In recent years, TMIP has facilitated numerous webinars (opens new window) that provide information on forecasting uncertainty and studies that have used TMIP-EMAT and other tools. Extensive documentation has also been developed for TMIP-EMAT (Uncertainty in Travel Forecasting: Exploratory Modeling and Analysis TMIP-EMAT: A Desk Reference (opens new window), https://tmip-emat.github.io/ (opens new window)).
Zhao, Yong & Kockelman, Kara. (2001). The Propagation Of Uncertainty Through Travel Demand Models: An Exploratory Analysis. The Annals of Regional Science. 36. 10.1007/s001680200072.
Niles J. S., and Nelson D.. Identifying Uncertainties in Forecasts of Travel Demand. Presented at the 80th Annual Meeting of the Transportation Research Board, Washington D.C., Jan. 2001.
Soora Rasouli & Harry Timmermans (2012) Uncertainty in travel demand forecasting models: literature review and research agenda, Transportation Letters, 4:1, 55-73, DOI: 10.3328/TL.2012.04.01.55-73.
Petrik, Olga & de Abreu e Silva, Joao & Moura, Filipe. (2013). Impact of Distribution Choice for Representing Input Variation. Transportation Research Record Journal of the Transportation Research Board. 40-48. 10.3141/2344-05.
Duell, Melissa & Gardner, Lauren & Dixit, Vinayak & Waller, Steven. (2014). Evaluation of a Strategic Road Pricing Scheme Accounting for Day-to-Day and Long-Term Demand Uncertainty. Transportation Research Record: Journal of the Transportation Research Board. 2467. 12-20. 10.3141/2467-02.
Soora Rasouli, Harry Timmermans. Applications of theories and models of choice and decision-making under conditions of uncertainty in travel behavior research, Travel Behaviour and Society, Volume 1, Issue 3,2014,Pages 79-90. ISSN 2214-367X, https://doi.org/10.1016/j.tbs.2013.12.001.
Qiong Bao, Bruno Kochan, Tom Bellemans, Davy Janssens & Geert Wets (2015) Investigating micro-simulation error in activity-based travel demand forecasting: a case study of the FEATHERS framework, Transportation Planning and Technology, 38:4, 425-441, DOI: 10.1080/03081060.2015.1026102.
Milkovits, Martin & Copperman, Rachel & Newman, Jeffrey & Lemp, Jason & Rossi, Thomas & Sun, Sarah. (2019). Exploratory Modeling and Analysis for Transportation: An Approach and Support Tool - TMIP-EMAT. Transportation Research Record: Journal of the Transportation Research Board. 2673. 036119811984446. 10.1177/0361198119844463.