Access to Destinations: Estimation of Arterial Travel Times

Principal Investigator:

Gary Davis, Professor, Civil, Environmental and Geo-Engineering

Project Summary:

The primary objective of this project was to identify and evaluate parametric models for making default estimates of travel times on arterial links. A review of the literature revealed several candidate models, including the Bureau of Public Roads (BPR) function, Spiess' conical volume-delay function, the Singapore model, the Skabardonis-Dowling model, and the Highway Capacity Manual's model. A license plate method was applied to a sample of 50 arterial links located in the Twin Cities seven-county metropolitan area to obtain measurements of average travel time. Also obtained were the lengths of each link, measurements of traffic volume, and signal timing information. Default values for model parameters were obtained from the Twin Cities planning model's database. Using network default parameters, the researchers found the BPR and conical volume-delay models produced mean average percent errors (MAPE) of about 25 percent, while the Singapore and Skarbardonis-Dowling models, using maximal site-specific information, produced MAPE values of around 6.5 percent. As site-specific information was replaced by default information, the performance of the latter two models deteriorated; however, even under conditions of minimal information the models produced MAPE values of around 20 percent. A cross-validation study of the Skabardonis-Dowling model showed essentially similar performance when predicting travel times on links not used to estimate default parameter values.

In what is likely to be an enduring period of constrained public resources, lawmakers and government executives will seek the best information possible for making policy choices and deciding where to make public investments. In a landmark series of studies known as Access to Destinations, the Center for Transportation Studies (CTS) at the University of Minnesota has opened up new frontiers of information for better policy and investment decisions.


Project Details: