The purpose of this report is to identify methods, parameters, and data sources that can be used to estimate and forecast commercial vehicles in each of the commercial vehicle categories defined in the prior tasks of this project for urban transportation models. The three primary goals of this report are:
The purpose of this phase of the project was not to estimate the parameters for use in urban transportation planning models, but rather to identify the variables and parameters that would be most appropriate. In many cases, the project team was able to estimate the magnitude of these parameters using the data sources developed as part of the project, but these estimates are based on limited data and not intended to be used directly in urban transportation planning models. The estimates are provided as indicators of the approximate size of the parameters and are expected to be replaced with actual parameters estimated from robust datasets in the second phase of the project.
The process of developing methods, variables, parameters, and data sources for each commercial vehicle category began with an identification of data sources that would address the following needs:
These data were then analyzed to describe the travel behavior characteristics that are appropriate for three different methods of estimating commercial vehicles:
After the methods, parameters, and data sources were analyzed for each commercial vehicle category, the parameters and data sources appropriate for analyzing groups of commercial vehicles based on their primary purpose were identified. There are three primary purposes, including specific commercial vehicle categories, as follows:
Finally, the individual commercial vehicle categories and the groups of categories were studied to identify these categories with the greatest overall impact on urban transportation models.
Many of the commercial vehicle categories defined for this project have a negligible impact on VMT; school buses, fixed shuttle services, private transportation, and paratransit vehicles each total less than one percent of VMT. It may, therefore, be reasonable to estimate these commercial vehicles using the Aggregate Demand Method or to estimate these commercial vehicles as a group (all vehicles moving people) using the Network-based Quick Response Method. If a particular study focuses on areas such as central business districts or airports that are more greatly impacted by these types of vehicles, then more robust techniques may be considered.
The commercial vehicles with the largest impact on VMT are urban freight distribution vehicles, business and personal service vehicles, rental cars, and public service vehicles. To more accurately capture the impacts of these commercial vehicles on congestion and air quality in the transportation planning models, network-based quick response or Model Estimation Methods should be used.
The commercial vehicles with some (but still not significant) impact on VMT are the package, product, and mail delivery vehicles, the construction transport vehicles, and the safety and utility vehicles. Their impact may be estimated using Aggregate Demand Methods or Network-based Quick Response Methods, depending on the characteristics of the urban area under consideration. Network-based techniques are desirable, but not necessary, for these categories, given their low overall impact on congestion.
The overall impact of commercial vehicles ranges from three to 25 percent of the total VMT for the urban areas in the project team's evaluation. This percent indicates that commercial vehicles should be considered directly in urban transportation planning models, at a minimum with the Aggregate Demand Methods, but preferably with Network-based Quick Response Method or Model Estimation Methods.
This report contains six sections. Section 2.0 presents the overview of the three forecasting methods and the recommended methods by vehicle category. Each of the methods was applied to 12 categories of commercial vehicles and the travel behavior characteristics were summarized.
Twelve categories of commercial vehicles were organized into three groups based on what is being carried - people, freight, or services - and what economic, demographic, and land use factors influence them. Section 3.0 describes these three groups of commercial vehicles and presents their travel behavior characteristics.
Section 4.0 presents the data available for calibration and validation of commercial vehicle models. These data are divided into three groups: registration records, VMT, and vehicle classification counts. Each is described in a separate subsection and its applicability for calibrating and validating commercial vehicles is discussed.
Section 5.0 presents recommendations for future research. Several of the 12 commercial vehicle categories prove difficult to estimate using the Aggregate Demand Method, Network-based Quick Response Method, and Model Estimation Method described in other sections. This section discusses the problems the project team faced, lists additional data needed in forecasting the commercial vehicle categories, and suggests how these problems can be solved. Finally, Section 6.0 presents the references for the report.
