The purpose of the first phase of the project was to estimate the magnitude and distribution of commercial vehicles in urban areas and to identify methods, data and parameters that would be appropriate to forecast these commercial vehicles in urban transportation planning models. The conclusions of the first phase of the project area described in the following sections.
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 all comprise 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 techniques, 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 six to 18 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 or Model Estimation Methods.
Three types of methods to forecast commercial vehicles in urban transportation planning models were considered in this phase of the work. The network-based quick response method is recommended for both macro and meso scale applications because the level of effort to implement this method is reasonable within current four-step planning practices and new data collection is not required. Model Estimation Methods are considered and strongly emphasized as a means to improve the forecasting of commercial vehicles in urban transportation planning models, but new data collection will be required to pursue further evaluation of these methods.
The project team identified a number of areas of future data development, based primarily on gaps in the data required to support the development of advanced commercial vehicle models. These data collection recommendations are designed to support the development of traditional four-step transportation planning models and state-of-the-art tour-based transportation planning models. In this way, the recommendations will support both current practice and future planning models.
The areas of future data development are summarized in three categories: vehicles by type, establishment surveys and forecasting. It is very difficult to definitively classify personal and commercial vehicles based on their use, rather than their registration. Personal vehicles that are used for commercial purposes and commercial vehicles that are used for personal reasons are estimated based on data from the Vehicle Inventory and Use Survey, but it would be useful to collect specific data on these classifications. In addition, not all commercial vehicles are trucks and should therefore be classified by vehicle type (autos, trucks and buses) for use in urban transportation planning models. Current registration data contains this information, but is not processed for this purpose in most states.
The most significant improvement in data collection for commercial vehicles would be collection of establishment surveys to support the following types of vehicles: manufacturing and industrial (for urban freight vehicles); retail and services (for business and personal service vehicles); construction (for construction vehicles); government (for safety, utility, and public service vehicles); education (for school buses); transportation (for shuttle services, taxi, paratransit, and rental vehicles); and other Industries (for package, product, and mail delivery vehicles).
The establishment surveys could be standardized or adapted for unique types of establishments, although the project team recommends standardizing these surveys as much as possible to improve the usefulness for model development. All of the surveys should include a complete day's travel diary information for a sample of vehicles in the establishment.
The current proposed methods for forecasting commercial vehicle travel are necessarily limited by the expected forecast data that would be available to a metropolitan planning organization. These methods could be expanded to provide more accurate assessment of future commercial vehicle travel as these future data sources become available.
Data needs were further identified specifically for each vehicle category and prioritized based on their overall impact on VMT. All of the recommended methods for estimating commercial vehicles in urban transportation planning models (factor analysis, network-based quick response, and Model Estimation Methods) use existing model forms and are not expected to require any future research to support these efforts. Other methods, such as activity-based or tour-based models, would advance the methods proposed in this project, but are beyond the scope of this initial effort, which was primarily aimed at developing network-based quick response techniques that could be adapted or transferred by metropolitan planning agencies. Tour-based models should be considered during the development of any locally specific models using the Model Estimation Methods described here and could be developed using the same data recommended here to support the Model Estimation Methods.
