Estimating the uncertainty of traffic forecasts from their historical accuracy

Jawad Mahmud Hoque, Gregory D. Erhardt, David Schmitt, Mei Chen, Martin Wachs

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Traffic forecasters may find value in expressing the uncertainty of their forecasts as a range of expected outcomes. Traditional methods for estimating such uncertainty windows rely on assumptions about reasonable ranges of travel demand forecasting model inputs and parameters. Rather than relying on assumptions, we demonstrate how to use empirical measures of past forecast accuracy to estimate the uncertainty in future forecasts. We develop an econometric framework based on quantile regression to estimate an expected (median) traffic volume as a function of the forecast, and a range within which we expect 90% of traffic volumes to fall. Using data on observed versus forecast traffic for 3912 observations from 1291 road projects, we apply this framework to estimate a model of overall uncertainty and a full model that considers the effect of project attributes. Our results show that the median post-opening traffic is 6% lower than forecast. The expected range of outcomes varies significantly with the forecast volume, the forecast method, the project type, the functional class, the time span and the unemployment rate at the time forecast is made. For example, consider a 5-year forecast for an existing arterial roadway made in 2019 when the state unemployment rate was 4% using a travel model. If a travel model predicted 30,000 Average Daily Traffic (ADT) on this road, our results suggest that 90% of future traffic volumes would fall between 19,000 and 36,000 ADT. A forecaster can apply the resulting equations to calculate an uncertainty window for their project, or they can estimate new quantile regression equations from locally collected forecast accuracy data. Aided by decision intervals, such uncertainty windows can help planners determine whether a forecast deviation would change a project decision.

Original languageEnglish
Pages (from-to)339-349
Number of pages11
JournalTransportation Research Part A: Policy and Practice
Volume147
DOIs
StatePublished - May 2021

Bibliographical note

Funding Information:
This work was supported by the Cooperative Research Programs of the Transportation Research Board [grant number National Cooperative Highway Research Program (NCHRP) 08-110].

Funding Information:
The Cooperative Research Programs of the Transportation Research Board funded this research. The final project report was published as NCHRP Report 934: Traffic Forecasting Accuracy Assessment Research, which includes the data and models included in this paper, plus a series of ex-post case studies and a guidance document with recommendations for improving practice. We thank the remaining co-authors of that report; our program officer, Larry Goldstein; the project panel, the participants of the project workshop, and especially the individuals and agencies that provided data in support of the study.

Publisher Copyright:
© 2021 The Authors

Keywords

  • Quantile regression
  • Reference class forecasting
  • Traffic forecast accuracy
  • Travel demand forecasting
  • Uncertainty

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Transportation
  • Management Science and Operations Research

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