Count Every Trip: Finding the Uncertainty in Energy Estimates Made from Inferred Travel Modes

Research output: NRELPresentation


To properly inform transport policy and infrastructure changes, transportation related metrics need both measured values and uncertainties of those values. Travel monitoring smartphone apps can record people's travel behavior, but trip data quality is limited by sensor errors, user labeling rates and the accuracy of inference algorithms used for travel diary creation. We discuss the use of phone app recorded travel diary data to estimate energy consumption, and propose the use of propagation of variance to find error bars for such estimates. We define energy consumption for one trip as trip length times the energy intensity per distance unit of the travel mode used. We characterize trip length errors with relative error and inferred trip mode errors with confusion matrix columns. The resulting variances of each measurement are then propagated to the final calculated energy consumption. We tested our uncertainty methods on a dataset that used phone app data combined with prompted recall, consisting of 92,234 labeled trips for over 500,000 miles. Accounting for uncertainty using expected energy intensities and variance propagation gives a dataset-wide aggregate energy consumption percent error of about 9%, within one standard deviation from the truth. Future work could involve applying similar methods to other travel diary based metrics.
Original languageAmerican English
Number of pages7
StatePublished - 2023

Publication series

NamePresented at the TRB Innovations in Travel Analysis and Planning Conference, 4-6 June 2023, Indianapolis, Indiana

NREL Publication Number

  • NREL/PR-5400-86201


  • conditional probability
  • confusion matrix
  • mode detection
  • travel behavior
  • travel diary
  • uncertainty quantification


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