Vehicle mix refinements in project-level traffic forecasting

# Objective

Vehicle mix is defined as the percentage of vehicles within each of many vehicle classes within a traffic stream. Project level decisions often require good knowledge of the number of trucks and the size of trucks, particularly for delay calculations, pavement designs and environmental assessments. Regional travel forecasting models tend to have only a few vehicle types, at best, within a multiclass traffic assignment. Coverage traffic counts, for the most part, do not count trucks separately from passenger cars. Thus, there is a need for factoring traffic forecasts, based directly on counts or otherwise, into many vehicle classes.

# Background

The concept of vehicle mix refinement was first introduced in NCHRP Report 255 and then updated for NCHRP Report 765. It is well known that the percentage of trucks varies considerably with the location (urban/rural) and functional class. Thus, classification counts at the specific location of the project are strongly preferred over adopting default values from national or even local sources.

# Guidelines

Classification counts should be obtained consistently with the 13 FHWA vehicle classes. Classification counts should be performed by visual observation of the traffic stream, either directly or by video. If automatic classifiers are used, then they should have accuracy at least equivalent to visual observation. Classification counts should be performed for a minimum of two days (48 hours) and in accordance with FHWA’s “Traffic Monitoring Guide”. Default vehicle mix tables are not recommended for some states like Hawaii because of the sizeable variations in vehicle mix that occurs on highways across the state.

The following four-step procedure is adapted from NCHRP Report 765.

Step 1. Select a base year vehicle mix from available data such as existing classification counts on the highway or on adjacent, parallel highways of a similar functional class (when the project is a new highway), or special counts for this project.

Step 2. Compare base year and future land uses. Consider only land uses (such as retail, ports, military bases and manufacturing) that are expected to generate sizable numbers of truck trips.

Step 3. Estimate the future year vehicle mix. The analyst may exercise judgment when adjusting a base year vehicle mix to account for changes in land uses.

Step 4. Factor forecasts of total traffic according to the vehicle mix determined in Step 3. See (future article Refining Vehicle Class Forecasts for Evaluation) for an expanded discussion of this step with a numerical example.

Truck time-of-day factors differ considerably from automobile time-of-day factors on most facilities. If possible classification counts should be done by time-of-day, at hourly intervals, to gain an understanding of how truck traffic varies diurnally. Small sample sizes might require that classification count data be combined across multiple sites and multiple days at each site. Any set of time-of-day factors developed locally should be compared for reasonableness with national data found in NCHRP Report 765.

Forecasts of total trucks are comprised of FHWA vehicle classes 4 to 13. See (opens new window) for details. Vehicle mix factors should be reported to the nearest hundredth of one percent.

# Advice

Truck forecasts from a regional travel model may or may not be consistent with FHWA’s vehicle classes. If there is inconsistency then the analyst must resolve any issues by making reasonable assumptions about the composition of the truck class within the travel forecasting model. Forecast year vehicle mixes should be compared with national defaults as a reasonableness check. Any large variations from national defaults should be explained.

# Items to Report

  • Base year vehicle mix
  • Forecast of number of vehicles by each truck class

# References

NCHRP Report 765, Quick Response Freight Manual I, Quick Response Freight Manual II.

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