Office of Planning, Environment, & Realty (HEP)
Current travel forecasting models are quite limited in their ability to estimate delay on links or at intersections. It is unlikely that good delay estimates can be calculated without substantial rewriting of software.
The 1985 Highway Capacity Manual was not developed for the purpose of travel forecasting, so many important relationships were omitted. Furthermore, HCM's delay relationships violate strict mathematical requirements that are necessary for the most widely adopted equilibrium traffic assignment algorithm, Frank-Wolfe decomposition.
For uncontrolled, multilane road segments, link delay can be adequately calculated with the BPR speed/volume function or with alternative functions proposed by Spiess and Overgaard.
Some models, including UTPS, calculate link capacity from a preset capacity for each lane, which can vary only by location in the region and by facility type. The complexity of the HCM procedures suggest that it is not possible to accurately calculate capacity within this type of modeling framework.
Complicated delay relationships are required for signalized intersections, unsignalized intersections, weaving sections, and two-lane roads. For these situations, delay on a single link is a function of volumes on two or more links.
It is possible to build a travel forecasting model that contains intersection delay relationships very similar to those in the HCM. One algorithm, sometimes referred to as equilibrium/incremental assignment, is available for finding an equilibrium solution. Strict application of the HCM procedures would result in networks with multiple equilibrium solutions. It is likely that the burdens of network calibration will be considerably reduced with such a model.
Levels of adaptation are important to the results of travel forecasts. Adaptation is a principal justification calibrating a network. The HCM provides sufficient information about the relationships between volume, capacity and delay to build assignment algorithms that are highly adaptive.