The changing accuracy of traffic forecasts

Jawad Mahmud Hoque (), Gregory D. Erhardt, David Schmitt, Mei Chen, Ankita Chaudhary, Martin Wachs and Reginald R. Souleyrette
Additional contact information
Jawad Mahmud Hoque: University of Kentucky
Gregory D. Erhardt: University of Kentucky
David Schmitt: Connetics Transportation Group
Mei Chen: University of Kentucky
Ankita Chaudhary: Connetics Transportation Group
Martin Wachs: University of California, Los Angeles
Reginald R. Souleyrette: University of Kentucky

Transportation, 2022, vol. 49, issue 2, No 5, 445-466

Abstract: Abstract Researchers have improved travel demand forecasting methods in recent decades but invested relatively little to understand their accuracy. A major barrier has been the lack of necessary data. We compiled the largest known database of traffic forecast accuracy, composed of forecast traffic, post-opening counts and project attributes for 1291 road projects in the United States and Europe. We compared measured versus forecast traffic and identified the factors associated with accuracy. We found measured traffic is on average 6% lower than forecast volumes, with a mean absolute deviation of 17% from the forecast. Higher volume roads, higher functional classes, shorter time spans, and the use of travel models all improved accuracy. Unemployment rates also affected accuracy—traffic would be 1% greater than forecast on average, rather than 6% lower, if we adjust for higher unemployment during the post-recession years (2008 to 2014). Forecast accuracy was not consistent over time: more recent forecasts were more accurate, and the mean deviation changed direction. Traffic on projects that opened from the 1980s through early 2000s was higher on average than forecast, while traffic on more recent projects was lower on average than forecast. This research provides insight into the degree of confidence that planners and policy makers can expect from traffic forecasts and suggests that we should view forecasts as a range of possible outcomes rather than a single expected outcome.

Keywords: Forecast accuracy; Traffic forecasting; Travel demand modeling; Induced demand (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11116-021-10182-8 Abstract (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:transp:v:49:y:2022:i:2:d:10.1007_s11116-021-10182-8

Ordering information: This journal article can be ordered from
http://www.springer. ... ce/journal/11116/PS2

DOI: 10.1007/s11116-021-10182-8

Access Statistics for this article

Transportation is currently edited by Kay W. Axhausen

More articles in Transportation from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().