In October 2002, the Federal Highway Administration began a research project to evaluate the magnitude and distribution of commercial vehicles in urban transportation planning models. The research was designed to look at all travel that is not adequately represented by the current state-of-the-practice for urban transportation planning models, which are developed from household travel surveys. Household travel surveys are designed only to capture household-related personal travel. Trips made for commercial purposes or using commercial vehicles are not captured. Some household travel surveys may inadvertently capture commercial trips such as realtors or tradesman making door-to-door visits but this does not represent a comprehensive assessment of this type of commercial vehicle travel.
To date, the literature and modeling for commercial vehicles has focused on urban freight distribution. The state-of-the-practice in the modeling of commercial vehicle travel in the urban transportation context has been geared toward developing a limited number of commercial vehicle trip generation factors, typically only disaggregated by truck type; for example, light, medium and heavy trucks. The traditional approach of relating these rates to land use activity has been found to be limited for application in travel demand modeling due to lack of data on differences in trip purpose, vehicle occupancy, and origin-destination (O-D) patterns. This study is the first to develop methods for forecasting all commercial vehicles, rather than just those involved in the distribution of urban freight.
This project is the first phase of a two-phase project to develop methods for forecasting commercial vehicles in urban transportation planning models. The goal of the first phase is to research, evaluate and identify methods for forecasting commercial vehicles in urban transportation planning models. The goal of the second phase is to develop these methods and estimate parameters that can be used in urban transportation planning models across the country.
The first phase has three primary work tasks:
The focus of this report is on the third work task to identify methods, parameters and data sources that can be used to estimate and forecast commercial vehicles in urban transportation planning models. The purpose of this phase of the project was not to estimate the parameters, but rather to identify the parameters that would be most appropriate. As part of the previous work efforts, we defined a commercial vehicle as one that is used primarily for commercial purposes. Some, but not all, commercial vehicles will be registered as commercial vehicles, since some vehicles registered as non-commercial may be used primarily for commercial purposes (we expect that these would be used for business and personal services). Commercial vehicles include autos, trucks and buses and are operated by both public and private sector agencies.
Trips made by commercial vehicles are organized into three groups, based on what is being carried and what economic, demographic and land use factors influence the magnitude and distribution of these trips. The three groups of commercial vehicles are vehicles that move people, move goods and provide services.
These three groups are further subdivided into 12 specific categories of commercial vehicles, based on the same criteria. These 12 categories of commercial vehicles are:
These 12 categories of commercial vehicles are direct subsets of the three commercial vehicle groups, as follows:
There were three categories of methods developed for use in forecasting commercial vehicles for urban transportation planning models:
The primary objective of the Aggregate Demand Method is to support emissions analysis and to improve validation of aggregate statistics in planning models. The primary objective of the Network-based Quick Response Method is to determine the effects of commercial vehicles on congestion and to improve validation of network volumes in planning models.
MPOs analyze various types of performance measures, including mobility, safety, reliability, and environmental impacts. These analyses are performed at the regional or county level, at the corridor or traffic analysis zone (TAZ) level, and at the intersection or link level, depending on the purpose of the analyses they carry out. To meet MPOs' varying needs, the project team divided the analyses levels into the following three groups:
Both the Aggregate Demand and the Network-based Quick Response Methods have been developed to address the macro analyses, with the difference being that Network-based Quick Response Methods would be more useful for commercial vehicle categories that have larger impacts on congestion or air quality. The Model Estimation Methods are primarily for the meso level of analysis because it is more useful to use local data when considering subarea or corridor-level analyses. This report does not present a method to address the micro level of analyses, leaving this subject instead for future research.
There are also other methods identified that were identified for future research but not pursued as part of this project because these are not currently in practice. Many of these future methods hold promise for advancing the state-of-the-practice in estimating commercial vehicles in urban transportation planning packages and should be considered in future evaluations. These methods for future research include: tour-based models, supply chain models and integrated models.
The overall share of total VMT for the urban areas in the project team's evaluation represented by commercial vehicles ranges from three to 25 percent. 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.
Data sources were identified and compiled in the previous work documented in the Magnitude and Distribution of Commercial Vehicle Travel report (Cambridge Systematics, November 2003). These data were expanded and further analyzed to provide data sources required for forecasting commercial vehicle travel in urban transportation planning models. Data sources were described separately for their use in forecasting individual commercial vehicles by category and are summarized below:
The methods were developed separately for each of the 12 individual commercial vehicle categories. In many cases, the methods and data sources were similar, but there are differences in application based on available data and expectations for certain causal relationships with demographic variables. The Aggregate Demand and Network-based Quick Response Methods are described for each of the 12 commercial vehicle categories. The Model Estimation Method is not described separately because it follows the same procedures that the Network-based Quick Response Method follows, only is based on locally specific data sources rather than national default data sources.
The travel behavior characteristics that are described for the Aggregate Demand Method include the fleet size, the vehicle trips and the vehicle miles traveled for each commercial vehicle category. For each travel behavior characteristic, we provide a description of appropriate methods and in most cases, an estimate of the parameter for these travel behavior characteristics. These estimates are derived from available data, which is frequently limited in sample size, and not recommended to represent a national default value for the parameter estimates.
The travel behavior characteristics that are described for the Network-based Quick Response Method include the trips produced and attracted to a traffic analysis zone, the trips distributed between traffic analysis zones, the vehicle type, the time of day, the vehicle occupancy (for commercial vehicles moving people) and the characteristics of trip assignment. Again, we provide a description of the methods and an estimate of the parameter, but with limited sample sizes available to estimate these parameters.
Following the evaluation of forecasting commercial vehicle travel by vehicle category (12), the same travel behavior characteristics of the Aggregate Demand analysis and Network-based Quick Response Methods were evaluated by groups of commercial vehicles. These groups represent aggregations of the commercial vehicle categories based on the primary purpose of the commercial vehicle: to move people, to move goods or to provide services. The methods and parameters developed for these groups of commercial vehicles are intended for use by metropolitan planning organizations that do not need or want to segregate commercial vehicles into 12 categories. It also is intended to work in coordination with the commercial vehicle categories for agencies that require additional detail for some groups but not others. For example, agencies may decide that it is useful to have greater accuracy for commercial vehicles moving goods and providing services, but that this additional accuracy is not required for commercial vehicles moving people.
Model calibration and validation data should be a unique source distinct from the data used in estimating model parameters. As a result, one needs to identify unique sources of data that can support model calibration and validation. For the purpose of this report, calibration and validation data are those data that can be used to compare with model predictions and determine the reasonableness of the model parameters. Model calibration and validation data also are used as a means to adjust the model parameter values so that travel predicted by the model matches observed travel in the region. This is especially important when applying nationally derived model parameters to a specific region.
There are three types of data that were identified in the Magnitude and Distribution of Commercial Vehicle Travel report (Cambridge Systematics, November 2003) that are appropriate for use in calibrating and validating commercial vehicles in urban transportation planning models:
The project team identified a number of areas of future research and 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 research and 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 (VIUS, 2000), but it would be useful to collect specific data on these classifications. In addition, since many commercial vehicles are automobiles and buses rather than trucks, commercial vehicles should 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 establishment surveys to support the estimation of 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 should be standardized or adapted for unique types of establishments, as much as possible to improve the usefulness for model estimation. 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. The current proposed methods for forecasting commercial vehicle travel also are unable to estimate micro-level transportation impacts. Specific techniques to estimate micro-level transportation impacts would be more appropriate, but should be developed after the regional-level impacts are better understood.
Data needs were further identified specifically for each vehicle category. All of the recommended methods for estimating commercial vehicles in urban transportation planning models (Aggregate Demand analysis, Network-based Quick Response Method, 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 Methods 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.
