The methods, variables, parameters, and data sources used for estimating commercial vehicle travel must be related to the appropriate level of planning application and the resources that are available to a metropolitan planning organization (MPO). The project team identified numerous sets of commercial vehicle forecasting approaches that could be used to evaluate different categories of vehicle types and meet MPOs' varying needs and levels of travel model complexity. These methods fall into three categories ranging from simple applications requiring limited data inputs to more advanced techniques to produce more spatial and temporal detail for a more specific range of commercial vehicle types:
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 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.
Figure 2.1 presents the modeling process for the Aggregate Demand Methods, Network-based Quick Response Methods, and Model Estimation Methods. This figure shows, in the form of a flow chart, the steps necessary to estimate impacts for commercial vehicles in urban transportation models. The Aggregate Demand Method provides a means to estimate regional impacts using nationally derived parameters and regional demographic estimates. The Network-based Quick Response Method and Model Estimation Methods are both based on applying four-step planning modeling techniques to estimate trips, origins and destinations, time periods, and volumes for commercial vehicles traveling in an urban area. These modeling processes would be applied to each category of commercial vehicle or to a group of categories. The mode choice component of the Model Estimation Method is included to represent the possibility of estimating commercial passenger vehicles for residents and visitors through the mode choice modeling process.
At the simplest level, MPOs may be served best if the fleet size rate by land use, employment, or any other readily available data can be provided, along with the miles traveled per vehicle per day for each category of commercial vehicle. These rates and miles traveled can be used to estimate VMT. The primary advantage of this approach is that it extends the typical commercial vehicle forecasting procedures used by MPOs to a broader range of commercial vehicle and trip types. This technique is primarily applicable at a regional (macro) level of detail.
Figure 2.1
Modeling Process for Three Methods to Estimate Commercial Vehicle Travel in Urban Transportation Models
This is a simple procedure for using national average vehicle rates and miles traveled to project commercial vehicle fleet sizes in the MPO's jurisdiction. It assumes the availability of demographic projections (population, employment, tourists visiting, etc.) for the year under consideration. Using existing data, the number of vehicles for a particular category of commercial vehicle is calculated as follows:
FleetSizec = VehicleRatec x SocioeconomicData
where:
The number of commercial vehicles can be estimated using the above equation. The miles traveled per vehicle per day for commercial vehicle categories are available from a variety of sources, identified in the Task 3 report. Hence, it is possible to estimate the VMT for commercial vehicle categories, as follows:
DailyVMTc = VMTperVehiclec x FleetSizec
where:
These procedures are documented separately for each commercial vehicle category in Section 2.2.
Some of the sources of data on mileage for specific commercial vehicle categories report annual vehicle mileage rather than daily vehicle mileage. This method uses these data to estimate daily vehicle miles using the same equation to determine fleet size, but then estimates daily mileage as follows:
DailyVMTc = (AverageAnnualMileagec Operating Daysc) x FleetSizec
where:
| Commercial Vehicle Category | Number of Days per Year | Assumption |
|---|---|---|
| School Bus | 180 | Weekdays from September to June |
| Fixed Shuttle Services | 365 | Every day |
| Private Transportation | 365 | Every day |
| Paratransit | 365 | Every day |
| Rental Cars | 365 | Every day |
| Package, Product and Mail Delivery | 306 | Weekdays and Saturdays, excluding holidays |
| Urban Freight Distribution, Warehouse Deliveries | 306 | Weekdays and Saturdays, excluding holidays |
| Construction Transport | 260 | Weekdays, excluding holidays |
| Safety Vehicles | 365 | Every day |
| Utility Vehicles | 260 | Weekdays, excluding holidays |
| Public Service Vehicles | 260 | Weekdays, excluding holidays |
| Business and Personal Services | 306 | Weekdays and Saturdays, excluding holidays |
The Network-based Quick Response Method applies a simplified four-step planning model where the parameters are derived from national data as default parameters. The method uses national average vehicle rates to develop trips produced and attracted by commercial vehicles, distributing these trips using the gravity model method, and assigning these trips to a planning model network to produce VMT. This procedure is applicable for either regional- (macro) or corridor- (meso) level detail, since the data is developed at a TAZ level and applied to a transportation planning network.
For the purposes of this simplified four-step planning model, the project team assumes that commercial vehicle travel does not include trips from outside the region. This assumption is based on the understanding that long-haul movement of commercial vehicles (i.e., tractor-trailers) carrying freight would be estimated using commodity flow forecasting methods separately from this process to estimate commercial vehicles within an urban area. So the simplified four-step planning model process does not include any external travel. If this process is applied at a corridor level, then trips from the region that pass into, out of, or through the study area must be included as external travel.
The simplified four-step planning model process can be applied by individual vehicle category or by group of commercial vehicles. The parameters, methods, and data sources for these models are described in Section 2.2 and Section 3.0 by category and by group, respectively. The Quick Response Freight Manual (Cambridge Systematics, 1996) provides a similar approach for the development of commercial vehicle trips carrying freight, which is the second group of commercial vehicles.
The Model Estimation Method estimates the parameters of a trip-based or tour-based planning model from local survey data. Model Estimation Methods rely on more detailed data sources than Network-based Quick Response Methods. The surveys required are establishment surveys for specific industries, as described below:
This results in commercial vehicles contributing up to a maximum of 25 percent of total VMT.
The four-step planning model components may be similar in structure to the Network-based Quick Response Methods, or they may be developed to be more sophisticated if the data supports this. These models are more resource-intensive than the Network-based Quick Response Methods, but provide greater flexibility in terms of capabilities and accuracy for a specific region. Model Estimation Methods developed for macro-level analysis can be used for meso-level (corridor) analysis, but only if the regional models already are developed.
A number of modeling methodologies currently being researched will advance the state-of-the-art for forecasting commercial vehicles. These are described briefly below.
Tour-based models estimate the number of "tours" that an individual commercial vehicle will make from when the vehicle leaves the garage to when it returns to the same garage. A number of individual trips typically comprise each tour. Model estimation requires a tour-based commercial vehicle survey; these are the same type of establishment surveys recommended for use in developing the four-step models. The surveys should include public and private establishments (retail, service, manufacturing, and government). If establishments include movement of people (rental cars, taxis, etc.), then this could cover all commercial vehicles.
Tour-based models are estimated by type of establishment (such as manufacturing and construction, etc.). These establishment models predict the number and types of vehicles (light, medium, heavy), the purpose of each trip on a tour (service, goods, other, return to establishment) and the location of the stops for every trip on a tour. These methods can account for a mixture of vehicles providing service and moving goods as well as empty vehicles returning to the establishment directly. One example of this type of tour-based model was estimated for retail and service delivery vehicles in Calgary, Alberta (Hunt, et al., 2003).
Supply chain models estimate the supply chain from distributor to warehouses to retailer to buyer. These supply chains can then be converted into the number of commercial vehicles required to support the supply of goods from the distributor to the buyer, including any intermediate storage locations. Supply chain models only represent the movement of goods and possibly services but would not be appropriate to model the movement of people in commercial vehicles. Supply chain models are typically estimated by type of supply chain (just-in-time, inventory, etc.) and product. One example of this type of supply chain model is the GoodTrip model developed for the City of Groningen, Netherlands (Boerkamps, et al., 2000).
Integrated models estimate the personal and commercial vehicles from an integrated model of land use and demographics. In this definition, integrated models would include personal travel, commercial travel, and forecasting of households and businesses within a single modeling framework. These models will predict the movement of people, goods, and services by design. These integrated models can predict the demand and supply of each vehicle type - for example, the demand for school buses as a function of the number of children in each household and the supply for school buses as a function of the size and population of the school district. Integrated models also can predict the location and need for new schools as a function of the growth in households and changes in lifestyles (i.e., decisions to have children). One example of this type of integrated model is the Oregon 2nd Generation Land Use Transport Model (Oregon Department of Transportation, 2002).
While similar methods may be used for each of the 12 individual vehicle categories, their application varies based on available data and expectations for certain causal relationships with demographic variables. These models are intended to estimate commercial vehicles directly, rather than the persons or goods traveling in these vehicles. In the description of the Network-based Quick Response Method, trips and tours refer to the number of trips or tours that the commercial vehicle is required to make to transport people, goods, or services. In cases where tours are more appropriate (such as school buses and mail delivery), these would use tours instead of trips to estimate the travel behavior, then either used directly to estimate vehicle miles traveled or converted to trips for assignment.
This description of methods by vehicle category is limited to the Aggregate Demand and Network-based Quick Response Methods. The procedures for the Network-based Quick Response Methods are similar to those in the Model Estimation Methods, except that the parameters for the Model Estimation Method are developed using local survey data and statistical model estimation practices. In addition, the Model Estimation techniques could accommodate advanced methods not covered by this evaluation.
Identification of the data sources used to determine estimates of travel behavior characteristics for each method are provided in Appendix A for reference. These data sources also are discussed in greater detail in a companion report (Cambridge Systematics, November 2003).
A description of the method is provided for each travel behavior characteristic, along with an estimate of the travel behavior wherever possible. These estimates are made based on available data and sometimes represent very limited datasets. In all cases, the number of urban areas (or cities) is identified with the estimate in the tables so that the limitations on the calculation of the estimates is understood. The estimates are not intended to represent national default parameters, except in a few cases where the data includes enough sample cities to be considered statistically accurate.
In the discussion of time-of-day characteristics, the following time periods were defined to summarize data across all vehicle categories:
School buses were defined as non-personal transportation vehicles that carry elementary, middle, and high school students. School buses operate on fixed routes, predominately on local streets and the distribution is affected by the location of school age children. Some school districts do not provide school bus service, some districts provide it only for their elementary schools, and some districts provide it only for students crossing major roadways. Still other districts provide service based on a minimum distance of travel or to achieve a racial balance. While this makes estimating and forecasting school bus trips more complicated, there are several good data sources for school bus fleets. These also were discussed in the Literature Review report (Cambridge Systematics, January 2003) and the Magnitude and Distribution of Commercial Vehicle Travel report (Cambridge Systematics, November 2003).
The Aggregate Demand Method estimates school bus fleet size and VMT using regional estimates of the population under 18, the total number of elementary, middle, and high school students, and/or other national default parameters. The project team matched each school district's boundary with Census 2000 data blocks and extracted population under 18, elementary, middle, and high school students, and educational employees.
Table 2.2 summarizes the resulting travel behavior characteristics. It includes estimates of fleet size and VMT calculated from a statistical analysis of the data from SchoolBusFleet.com and the population and student data for the same area from the Census 2000 database (U.S. Census Bureau, 2004). The percent of VMT was estimated and presented in the Magnitude and Distribution of Commercial Vehicle Travel report (Cambridge Systematics, November 2003).
| Travel Behavior Category | Description | Estimates |
|---|---|---|
| Fleet Size | Fleet size can be estimated as a function of school-age students, and population under age 18. | 0.004 school bus per school-going student ranging from 0.003 to 0.015 (data from 100 school districts). |
| Trip/Tour Length | School buses make several round trips each day to serve different schools within a district. Average mileage is calculated from annual miles traveled assuming 180 days of operation per year. | 83 miles per day per bus ranging from 22 miles per day to 192 miles per day (data from 88 school districts). |
| Vehicle Miles Traveled | School buses represent a larger share of VMT in local and collector streets and streets around schools than on major arterials | 0.15 percent of total VMT on all roads, based on data from 100 school districts. |
Data from 100 school districts on the SchoolBusFleet.com web site can be used to estimate regressions for school bus fleet size and regional VMT on a national basis. The project team collected demographic data for these cities from the Census database. Table 2.2 presents a summary of the travel behavior characteristics for the Network-based Quick Response Method. School bus trips start from school, follow fixed routes, then return to school. In some cases, school bus trips start from school, go to a specific destination, and then return to school (field trips). As a result, it is not possible to estimate trip productions and attractions separately.
Regarding trip distribution, only 0.15 percent of total VMT on all U.S. roads are attributed to school buses, but the figure varies significantly from highways to local streets. In terms of vehicle types, most of the buses are large, although there are many small buses and buses with lifts, as shown in Table 2.3.
No data are available for time-of-day analysis. However, morning peak period is more or less the same as the commuter peak period, and most of the buses are garaged by 4:00 p.m.
Students begin and end their trip at home, but school buses begin and end their routes at school or at a garage. The accurate representation of school bus routing and district definition would require substantial data collection and network coding, but these can be approximated using school district boundaries, school locations and Census demographic data.
The project team could find no studies related to time-of-day distribution. Data might be collected for time of distribution of school buses. In terms of assignment, further data collection is needed to develop proper assignment procedures.
| Travel Behavior Category | Description |
|---|---|
| Trips/Tours | Regression models can be used with variables for residence (population under 18, school-age children, households) and/or educational employees. Most school bus trips start from school and finish at school. Some school trips start from school and go to other places (field trips). |
| Distribution | School buses are distributed through local streets, with origins and destinations at school. Some buses are garaged at the contractor's facility or at the driver's residence and deadhead to the start of the route. |
| Vehicle Type | There are three types of school buses, large, small, and small with lifts. In the 100 top school districts (http://schoolbusfleet.com/t_home.cfm?CFID=5754288&CFTOKEN=24981657), there are 79 percent large buses, 16 percent small buses and five percent small buses with lifts. |
| Time of Day | School buses run from 7:00 a.m. to 4:00 p.m. with the a.m. peak from 7:00 to 9:00 a.m. and p.m. peak from 2:00 to 4:00 p.m. |
| Vehicle Occupancy | Vehicle occupancy varies from one to 50 depending on district. Average 14 students (Pearson, 1997). |
| Assignment | School buses generally run on fixed routes using local streets. |
Shuttles are available at most airports in the United States, serving either fixed routes between hotels and airports or operating as on-demand services from various geographic locations to airports. Shuttle services also serve major intermodal terminals other than airports (such as rail stations). Shuttles serve both residents and non-residents of any urban area and fleet sizes can be a function of population size, level of tourism, and the options (or lack thereof) for alternative modes. In addition, the presence of rail transit can have a significant impact on the use of shuttle services.
There are some cities where there are direct tradeoffs with rental cars, shuttle services and taxis to serve the travel needs of the non-resident population. For example, Seattle would have higher rates for shuttle services and lower rates for taxis and rental cars than the average, but other cities like Tampa have lower rates for shuttle services and higher rates for taxis and rental cars than the average. This is apparent in the airport survey data collected (Appendix D of Cambridge Systematics, November 2003). For example, shuttle services in Seattle are 60 percent of total trips compared to Tampa where shuttle services are 30 percent of total trips while rental cars are 27 percent in Seattle and 64 percent in Tampa.
The Aggregate Demand Method estimates fleet sizes and VMT using model area estimates of total population and factors developed from the Airport Ground Access Planning Guide. The project team tried to estimate fleet sizes and VMTs using number of tourists and hotel rooms, but these two variables are not statistically significant with the available data. The Guide provides enough data to estimate vehicle trips but not fleet sizes. A summary of the travel behavior characteristics is provided in Table 2.4.
| Travel Behavior Category | Description | Estimates |
|---|---|---|
| Fleet Size | Fleet size cannot be derived with the available data. | N/A |
| Trip/Tour Length | Shuttle services primarily provide transport for visitors and residents to and from airports. Round trip mileage can be short with many trips per day, or longer with fewer trips per day. | Average daily miles of travel per trip is 14.7 miles (data from 28 cities). Note that average mileage per day is not available since we do not have fleet sizes. |
| Vehicle Trips | Vehicle trips can be estimated as a function of population, number of tourists, and number of hotel rooms. | 0.0002 trips per person (data from 28 cities). |
| Vehicle Miles Traveled | Shuttle services typically represent a small percentage of overall VMT but contribute to a potentially large share of VMT at airports | 0.02 percent of total VMT in an urban area. The daily average VMT is 11,518. (data from 28 cities). |
Airport shuttle data derived from the Airport Ground Access Planning Guide can be used to estimate travel behavior characteristics for the Network-based Quick Response Method. Table 2.5 presents a summary of these characteristics.
| Travel Behavior Category | Description |
|---|---|
| Trips/Tours | Cross-classification or regression models can be used with variables for demand (population and tourists). |
| Distribution | Shuttle trips typically originate at airports and are destined to tourist destinations, hotels and residences. These shuttle services would include fixed-route and demand-response vans (i.e., Super Shuttle) that serve both residents and visitors, as well as courtesy vans. |
| Vehicle Type | Vehicle types include minivans, SUVs, and minibuses. |
| Time of Day | Shuttle services are available during the entire period that airports remain open. There are 24.1 percent of shuttle vehicle trips for a.m. peak, 14.8 percent for p.m. peak, and 61.2 percent for midday (data from the Denver CV survey). |
| Vehicle Occupancy | Vehicle occupancy can be estimated from survey data to estimate person trips per vehicle. There are three to 10 persons per vehicle (data from Boston Logan Airport). |
| Assignment | Most shuttle service vehicles are permitted on the entire roadway system and are concentrated around airports. |
From the data available, production and attraction of shuttle trips were found to be best represented by the population within an area. Contrary to expectations the number of tourists and hotel rooms was not a useful variable in estimating vehicle trips. Both production and attractions are based on a sample of 28 cities.
Trip distribution may be performed using a gravity model. An average trip length of 14.7 miles was determined using the statistics provided in the Guide.
Private transport is represented by taxis, which are present in most urban areas in the U.S. Taxis serve both residents and non-residents of any urban area and fleet sizes can be a function of population size, level of tourism, and the options (or lack of options) for alternative modes. Specifically, the presence of rail or significant shuttle services to airports can affect the use of taxis in the region. In addition, some cities ban "hailing" of taxis, which greatly reduces their use. Taxi service is on-demand, so can cover the entire urban area, but is typically concentrated in areas of high employment, around intermodal terminals, and at tourist attractions.
There are some cities where there are direct tradeoffs with rental cars, shuttle services and taxis to serve the travel needs of the non-resident population. For example, Minneapolis has more service for shuttles and less service for taxis and rental cars than the average, but St. Louis has more service for taxis and rental cars than the average. This is apparent in the airport survey data collected (Appendix D of Cambridge Systematics, November 2003). For example, shuttle services in Minneapolis are 40 percent of total trips compared to St. Louis where shuttle services are 26 percent of total trips while taxis are 36 percent in St. Louis and 22 percent in Minneapolis.
A few models specifically estimate taxis around the globe and a few models in the U.S. address taxis as part of the four-step modeling process. In most urban transportation planning models, the resident-based taxi trips are accounted for in the overall trip-making, but taxis are not specifically separated out in the mode choice models, except in a few cases.
The Aggregate Demand Method estimates taxi trips and fleet size using regional estimates of employment and hotel rooms and nationally derived default parameters. Taxi trips are estimated for each city using the national averages for trips per taxi for different fleet size categories. These parameters can be derived by expanding the Taxi Fact Book to include the number of workers and hotel rooms for 270 cities. Current estimates of these parameters are based on a smaller sample of cities.
A summary of the travel behavior characteristics is provided in Table 2.6. This summary includes estimates of fleet size, trips, and VMT calculated from a statistical analysis of the data in the Taxi Fact Book, combined with demographic data. The percent of VMT was estimated and presented in the Magnitude and Distribution of Commercial Vehicle Travel report (Cambridge Systematics, Inc., November 2003).
| Travel Behavior Category | Description | Estimates |
|---|---|---|
| Fleet Size | Fleet size can be estimated as a function of employment and hotel rooms. Density also may be a factor. | 0.003 taxis per worker + 0.012 taxis per hotel room (data from 28 cities). |
| Trip/Tour Length | Taxi trips have a wide range of mileages and may not represent the complete daily mileage of the taxi due to empty taxi trips and cruising that occurs. Average mileage is calculated from annual mileage information assuming 365 days of operation per year. | 149.5 average miles traveled per day, nine average miles per trip (data from 270 cities). |
| Vehicle Trips | National average = 16.5 trips per taxi from Taxi Fact Book. Varies by fleet size, 1-24 = 20.2 trips per taxi, 25-99 = 17.1, and 100+ = 16.2. | 16.5 trips per taxi per day (range is 16.2 to 20.2). |
| Vehicle Miles Traveled | Taxis represent a potentially large share of VMT in central business districts and at airports but a small overall contribution to total VMT. | 0.20 percent of total VMT in an urban area (data from 13 cities). |
The 270 cities reported in the Taxi Fact Book can be used to estimate regressions for fleet size and regional VMT on a national basis. The project team collected demographic data for a portion of these cities for use in evaluating taxi trips. Table 2.7 presents a summary of the travel behavior characteristics for the Network-based Quick Response Method.
Taxi trips as a function of population and hotel rooms represent trip origins. Taxi trips as a function of employment and tourist attractions represent trip destinations. These trip rates can be estimated from the 270 cities in the Fact Book, combined with Census Bureau data on population and employment and hotel rooms obtained from tourist bureaus in each city. Estimates of trip origins are derived from a linear regression of taxi trips as a function of population and hotel rooms, based on data from 28 cities (limited by the available data on hotel rooms). Estimates of trip destinations are derived from a linear regression of taxi trips as a function of employment, based on data from 134 cities (limited by the available data on employment).
| Travel Behavior Category | Description |
|---|---|
| Trips/Tours | Cross-classification or regression models can be used with variables for resident (population, households) and non-resident (hotel rooms). There are 0.02 daily taxi trips per person + 0.08 taxi trips per hotel room (based on data from 28 cities). Cross-classification or regression models also can be used with variables for employment by type, households, and tourists at major destinations and intermodal terminals (such as airports). There are 0.06 daily taxi trips per worker (based on data from 134 cities). |
| Distribution | Taxis are distributed widely throughout the region, but are most common in areas of higher employment, in the vicinity of tourist destinations, and at airports/rail/bus stations. |
| Vehicle Type | Most taxis are passenger autos, with some passenger vans. |
| Time of Day | Taxis operate at all times of day, especially during daytime hours (both peak and off-peak). Peak time for taxis is 7:00 a.m. to 1:00 p.m., with 50 percent of trips in this time period (data from 260 taxi trips). |
| Vehicle Occupancy | In this application, taxi trips are vehicle trips and do not require conversion for use in trip assignment. There are 1.3 passengers per trip (based on 270 cities). |
| Assignment | Taxis drive on all routes, particularly major arterials and freeways. |
Taxi trips can be distributed using a gravity model with friction curves estimated from travel survey data on taxis. Taxi trips from the travel survey data are plotted to represent the trip length frequency of all taxi trips. The friction curve is then estimated to produce a similar trip length frequency for estimated taxi trips. The taxi trips in the National Highway Travel Survey and Florida surveys are a source for this estimation process, but the sample size may require expansion with other surveys for reliable statistical estimation. Estimates of average mileage and miles traveled are from the Taxi Fact Book (Taxicab, Limousine and Paratransit Association, 2002).
One source of data on time of day is the National Highway Travel Survey, with 260 taxi trips in the U.S. This source includes only resident taxi trips and should be supplemented with data from non-resident taxi trips to ensure a more representative sample. Vehicle occupancy is derived from the Taxi Fact Book and represents 270 cities.
Another means of estimating taxi trips for residents and non-residents would be to incorporate taxis as a modal option in existing mode choice models. This process assumes that the trip generation model includes taxi trips (most existing urban area models do so) and that there is a separate trip generation model for visitor (non-resident) trips. These trips are distributed by trip purpose along with other resident and non-resident trips. The mode choice models for resident and non-resident trips include taxi as a separate modal option. As is the case with mode choice models for transit, a large enough sample of taxi trips in the household survey is needed to adequately separate out these trips in the mode choice model. This has been done in the Houston-Galveston, Cleveland, Las Vegas, and New York mode choice models. In the Las Vegas model, there is a separate resident and non-resident (visitor) modeling process, so taxis are represented as a mode in the mode choice model for each of these models. The primary drawback of this approach is that it only predicts taxi trips when a passenger is in the taxi, which will not represent the empty taxi trips.
Paratransit and social service vehicles serve primarily disabled and senior populations. Since the mid-1980s, the number of paratransit and social service systems across the United States has increased significantly (Bearse, P., et al., 2003). In 198 cities with fewer than 400,000 inhabitants, paratransit trips increased from 6.0 million in 1984 to 16.9 million in 1995 (J. Fitzgerald et al., 2000). Unfortunately, very little research has been done concerning paratransit and no models have been developed to estimate the demand for these trips.
The Aggregate Demand Method estimates paratransit fleet size, total trips and vehicle miles using the regional estimates of total population over the age of 60 and total employment. These parameters were derived using data from 220 cities. A summary of the travel behavior characteristics is provided in Table 2.8.
| Travel Behavior Category | Description | Estimates |
|---|---|---|
| Fleet Size | Fleet size can be estimated as a function of population greater than 60 years of age. | 0.008 paratransit vehicle per population over 60 (data from 220 cities). |
| Trip/Tour Length | Paratransit vehicles are demand responsive so the average miles are dependent upon demand. The average mileage is calculated from the annual miles assuming 365 days of operation per year. | 24 average miles (range one to 58 miles) traveled per day, five average miles per vehicle trip (data from 316 cities). |
| Vehicle Trips | National average of trips per paratransit vehicle is provided in the National Transit Database. | 5.1 daily trips per paratransit vehicle with a range from one to 27 (data from 220 cities). |
| Vehicle Miles Traveled | Paratransit and social service vehicles' share of VMT is negligible. | 0.01 percent of total VMT in an urban area (data from 220 cities). |
The team used NTD data for 220 cities and estimated the regression coefficients for trips. Because the NTD data do not include population and employment data, paratransit data was matched with Census Bureau data to obtain total population, population over the age of 60, and employment data. Table 2.9 presents a summary of the travel behavior characteristics for the Network-based Quick Response Method.
| Travel Behavior Category | Description |
|---|---|
| Trips/Tours | Cross-classification or regression models can be used with variables for residence (population over the age of 60, total population, households). There are 0.006 daily paratransit trips per person over the age of 60 (data from 220 cities) Cross-classification or regression models also can be used with variables for employment by type. There are 0.0002 daily paratransit trips per employee (data from 220 cities). |
| Distribution | Paratransit vehicles are distributed throughout the region, but are concentrated in areas with a higher percentage of elderly and fewer transit services. Paratransit trips can be distributed with a gravity model. |
| Vehicle Type | Most vehicles are vans or van conversions, but some larger buses are also in paratransit service. |
| Time of Day | Paratransit vehicles operate seven days a week without any specific peak periods. |
| Vehicle Occupancy | Vehicle occupancy for paratransit vehicles depends on the type of services. Demand responsive services may have one passenger, while fixed-route services may have many. |
Rental cars form one of the most important categories of commercial vehicles in urban areas. About two percent of the total VMT on U.S. roadways is from rental cars (Cambridge Systematics, November 2003). However, very limited data are available on this category. While some aggregated statistics are available from different sources, the project team found no specific studies related to rental car demand analysis, fleet size, or VMT. Almost without exception, rental car companies do not release data on their fleet size and mileage. Only Hertz Rent-A-Car provided this information for 13 cities in response to the project team's request, but on the condition the data not be published directly. Based on Hertz's data and other available data, the total U.S. rental car fleet size, average mileage per vehicle, and other statistics for selected cities were estimated.
There are some cities where there are direct tradeoffs with rental cars, shuttle services and taxis to serve the travel needs of the non-resident population. For example, New York City would have lower rates for rental cars and higher rates for taxis than the average because of the limitations for driving and parking that are inherent on New York City streets, but other cities with lower densities and inexpensive parking (such as Florida cities) will likely have higher rates for rental cars and lower rates for taxis than the average. This is apparent in the airport survey data collected (Appendix D of Cambridge Systematics, November 2003). For example, rental cars in Orlando are 46 percent of total trips compared to New York (JFK) where rental cars are three percent of total trips while taxis and on-demand services are six percent in Orlando and 45 percent in New York (JFK).
Similar to the taxis, rental cars operated by residents are captured by household-based models in the trip generation model, but not typically separated out as rental car trips in the mode choice model. Since rental cars for residents probably have similar distributions as vehicles owned by the household, it is reasonable to incorporate these into urban transportation planning models this way, except that it will be important not to double-count resident-based rental car trips when including rental cars as commercial vehicles. These can be excluded from the household-based planning model estimates (which is very straight-forward) or excluded from the rental car data (which is done by surveying only non-residents).
The Aggregate Demand Method can be used to estimate the rental car fleet size, the number of trips, and VMT using the regional estimates of population, employment, and the number of hotel rooms. A summary of the travel behavior characteristics is provided in Table 2.10.
| Travel Behavior Category | Description | Estimates |
|---|---|---|
| Fleet Size | Fleet size can be estimated as a function of population, employment, and hotel rooms in an urban area. Number of tourists also may be a factor. | 0.7 rental cars per hotel room (based on data from 13 cities). |
| Trip/Tour Length | Rental car companies reported an average of 80 miles per day per vehicle, compared to 43 miles per day from VIUS, based on annual mileage reported. | Average mileage per rental car per day ranges from 43 to 80 miles (from the Vehicle Inventory and Use Survey and sample rental car companies). |
| Vehicle Trips | This requires rental car or visitor survey data to estimate number of trips. | 2.2 daily trips per rental car (Orlando Trip Log Data). |
| Vehicle Miles Traveled | Rental cars represent a large share of VMT in urban areas and at airports. | Two percent of total VMT ranging from 0.8 percent to 4.3 percent (data from 13 cities). |
Rental car trips can be divided into three groups: business-related, social-recreational, and other, with other trips being a very small proportion of the total (less than 10 percent in the Florida surveys). Most business trips start at the airport and go to business districts or employment centers. Business-related rental car trips can be estimated from airport surveys. Recreational rental car trips usually start from airports or car rental sites and go to tourist or recreational areas. All rental car trips also can be estimated from rental car surveys.
Data for 13 cities were used in conjunction with population, employment, and hotel room data from the U.S. Census Bureau to estimate rental car fleets. Table 2.11 shows the recommended methods for estimating rental car data using the Network-based Quick Response Method and the estimated parameters.
| Travel Behavior Category | Description |
|---|---|
| Trips/Tours | Cross-classification or regression models can be used with variables for residents (population, households) and non-residents (hotel rooms). There are 0.005 daily trips per person + 0.8 daily trips per "population/square miles" (data from 13 cities). |
| Distribution | Rental cars are distributed throughout the region, but are concentrated in areas of tourist destinations, airports, and central business districts. |
| Vehicle Type | Most vehicles are passenger autos, but many medium and heavy vehicles are also in this category. Cars are 76 percent, pickups are four percent, SUVs are nine percent, vans are eight percent, medium and heavy vehicles are three percent (from the California DMV data). |
| Time of Day | Rental cars operate seven days a week. There is six percent in the a.m. peak, 39 percent at midday, 11 percent in the p.m. peak, and 44 percent at night (from Florida District 5 survey trip log data). |
| Vehicle Occupancy | Vehicle occupancy depends on the purpose of trips. Vehicle occupancy for recreational trips is higher than for business trips. There are 3.4 person per vehicle (Florida District 5 trip log data). |
| Assignment | Rental cars drive on all routes. |
Package, product, and mail delivery vehicles can be classified as either public or private. The United States Postal Service (USPS) represents the public section of this category, while there are numerous private carriers.
Package, product, and mail delivery represents approximately one percent of total VMT for an urban area (Cambridge Systematics, November 2003). Mail is typically delivered during daytime hours in smaller vehicles (SUV-sized to large vans and parcel trucks), while bulk distribution of mail is performed by heavier vehicles (tractor-trailers and combination trucks) during nighttime hours.
The Aggregate Demand Method estimates package, product, and mail delivery trips using model area estimates of total population, households, and employment factors developed from USPS data and the four commercial vehicle surveys.
A summary of the travel behavior characteristics is provided in Table 2.12.
| Travel Behavior Category | Description | Estimates [Based on USPS data for seven cities (public) and commercial vehicle survey data from four cities (private)] |
|---|---|---|
| Fleet Size | Fleet size can be estimated as a function of population, households, and employment. The presence of a major private or public distribution center will greatly impact the fleet size for the urban area. | Fleet size = 0.005 per number of area employees (62 percent private and 38 percent public). |
| Trip/Tour Length | There is a wide range of miles traveled per day from different sources reflecting the broader geographic coverage from private sources. | Average ranging from 19 miles per day (public) to 163 miles per day (private). |
| Vehicle Trips | Average number of trips estimated from commercial vehicle survey data from Detroit, Atlanta, Denver, and the Piedmont Area in North Carolina. | Average daily number of trips per vehicle is 4.0, with a range of 2.1 to 7.5 daily trips per vehicle (data from four cities). |
| Vehicle Miles Traveled | Package, product and mail delivery typically represents a small percentage of overall VMT. Private VMT is approximately twice as large as public VMT. | Daily VMT = 0.2 per number of employees (70 percent private and 30 percent public. |
1 Estimates are based on USPS data for seven cities (public) and commercial vehicle survey data from four cities (private).
Delivery trip data derived from multiple commercial vehicle surveys can be used to estimate travel behavior characteristics for the Network-based Quick Response Method. Table 2.13 presents a summary of these characteristics.
| Travel Behavior Category | Description |
|---|---|
| Trips/Tours | Cross-classification or regression models can be used with variables for area residents (population). There are 0.01 daily trips per employee (private). Cross-classification or regression models also can be used with variables for area workers. There are 0.02 trips per household (private). |
| Distribution | Delivery trips are distributed throughout the entire study area, but may be more concentrated in areas of high employment, where there are higher incidences of private delivery vehicles. Delivery trips are expected to be tours rather than individual trips, measured by the number of delivery locations. |
| Vehicle Type | Vehicle types may range from passenger autos to combination tractor-trailers. Public vehicle types are 95 percent light-duty, three percent medium-duty, and two percent heavy-duty (data from USPS). |
| Time of Day | Package, product, and mail delivery is performed mostly during the day by smaller delivery vehicles. These lighter vehicles are more numerous than the heavier vehicles that carry the mail to/from distribution centers, often at night. Seventy-two percent of all trips occur between 9:00 a.m. and 3:00 p.m. (data from three commercial vehicle surveys). |
| Assignment | Most delivery vehicles are permitted on the entire roadway system, with the exception of some heavy vehicles that may be excluded from certain roads. |
From the data available, estimation of delivery trips was found to be best represented by the number of households within an area. Although population and household are highly correlated, contrary to expectations, population was not a useful variable in estimating vehicle trips. This may be due in part to high populations in apartment buildings or other housing clusters that have multiple delivery boxes located in one place, requiring only one trip to satisfy a larger number of people. Estimates of delivery trips also can be based on the number of employees for the cities with commercial vehicle survey data. Because these estimates are based on a sample of only four cities, more data is required to develop better estimates.
Delivery trips begin and end at distribution centers and travel either on fixed routes or on-demand route systems. An average mileage of 16 miles was determined from the commercial vehicle survey data (for private delivery vehicles).
In urban areas, vehicles deliver goods to warehouses and distribution centers, and transport these goods from the warehouses and distribution centers to their final (or next) destination. Passenger, light-duty, and heavy-duty vehicles are all used to perform these operations, depending on the goods being transported.
The Aggregate Demand Method estimates urban freight trips and fleet sizes using regional population estimates combined with derived default parameters. The Quick Response Freight Manual contains two types of Aggregate Demand Methods, the first based on historical trends and the second based on forecasts of economic activity. The historical trends Aggregate Demand Method uses two years of historical data to create an annual growth factor. The economic activity method assumes that the demand for transport of a specific commodity is directly proportional to an economic indicator variable that measures the demand for the commodity. With this assumption growth factors for economic indicator variables can be developed. The economic activity method requires base- and forecast-year data for the economic indicators and base traffic by commodity.
Outside of the Quick Response Freight Manual, commercial vehicle survey data can be used for estimates using the Aggregate Demand Method. Table 2.14 outlines some basic travel behavior characteristics of Urban Freight based on the available commercial vehicle surveys.
| Travel Behavior Category | Description | Estimates 1 [Based on commercial vehicle survey data from four cities] |
|---|---|---|
| Fleet Size | Fleet size can be estimated based on population. Total employment does not appear to be as significant as population. | 0.02 vehicles per person based on commercial vehicle surveys. |
| Trips/Tours | Average mileages are typically longer than other commercial vehicles (except taxis), based on annual VIUS data assuming 306 days of operation per year. | Average 12.7 miles per trip; 65 miles per day per vehicle. |
| Vehicle Trips | Average daily number of trips estimated from commercial vehicle survey data from Detroit, Atlanta, Denver, and Piedmont Area in North Carolina. | Average daily number of trips per vehicle is 5.1 from four surveys with a range of 3.2-6.6 trips per vehicle. |
| Vehicle Miles Traveled | Urban freight represents a larger percentage of overall VMT than most other types of commercial vehicles. | 4.4 percent of total VMT based on commercial vehicle surveys. |
1 Estimates are based on commercial vehicle survey data from four cities.
The Quick Response Freight Manual (Cambridge Systematics, 1996) outlines procedures for calculating trip generation and trip distribution. Trip generation rates provided by the Manual are shown in Table 2.15. These are intended to capture all commercial vehicles carrying goods and providing services, so they are higher than rates that are calculated only for urban freight vehicles. Nonetheless, they may be scaled to provide comparisons for the urban freight category of commercial vehicles. Updated trip generation rates (NCHRP Synthesis 298, 2002) also can be used to assess truck trip generation of urban freight vehicles.
The Manual uses a traditional gravity model to distribute the total origins and destinations estimated from the trip generation process. Friction factors are estimated using exponential factors with -0.08 for four-tire, -0.1 for single-unit trucks, and -0.03 for combination trucks.
| Generator | Trip Rates |
|---|---|
| Agriculture, Mining, and Construction | 1.573 trips per worker |
| Manufacturing, Transportation, Communications, Utilities, and Wholesale Trade | 1.284 trips per worker |
| Retail Trade | 1.206 trips per worker |
| Office and Services | 0.514 trips per worker |
| Households | 0.388 trips per household |
Source: Quick Response Freight Manual.
Urban freight trip data derived from multiple commercial vehicle surveys can be used to estimate travel behavior characteristics for the Network-based Quick Response Method. Table 2.16 presents a summary of these characteristics.
| Travel Behavior Category | Description |
|---|---|
| Trips/Tours | Cross-classification or regression models can be used with variables for area workers with 0.17 daily trips per worker. Cross-classification or regression models also can be used with variables for area residents (population and households) with 0.10 daily trips per person. |
| Trip Distribution | Urban freight vehicles are distributed throughout the system with higher concentrations in areas of high total employment and industrial employment. |
| Vehicle Type | The majority of urban freight is carried by light-duty vehicles, but will vary depending on the commodity being transported. (See Table 2.17 for details.) |
| Time of Day | Urban freight trips mostly occur during business hours. There is seven percent in the a.m. peak, 60 percent in the midday, 23 percent in the p.m. peak and 10 percent at night (data from three commercial vehicle surveys). |
| Trip Assignment | Some urban freight vehicles may be restricted from certain parts of the road network. |
From the survey data available, urban freight trips was found to be best represented by the population or employment within an area. Because these trips were based on a sample of only four cities, more data is required to develop better estimates.
Trip distribution may be performed using a gravity model. An average trip length of 12.7 miles per trip was determined from the commercial vehicle surveys.
As illustrated in Table 2.17, light duty vehicles make up the majority of trips made by urban freight vehicles. Heavy duty vehicles make up the second largest number of trips.
| Vehicle Type | Atlanta | Denver | Detroit | Triad | Weighted Average |
|---|---|---|---|---|---|
| Heavy | 18.56% | 31.02% | 17.21% | 41.42% | 25.16% |
| Medium | 2.58% | 8.89% | 24.18% | 27.00% | 8.01% |
| Light | 78.86% | 60.09% | 58.60% | 31.58% | 66.82% |
Construction transport vehicles are used primarily for hauling materials and equipment to a construction site. These vehicles are usually heavy trucks; the numbers of these trips can vary depending on the duration, scale, and type of construction project with which they are associated.
The construction transport commercial vehicle category is the most site-specific of all of the categories identified. The origin end of these trips is relatively fixed. Producers of construction materials and equipment tend to have set locations and varying productions of trips. The demand for these trips varies both in terms of location and need, depending upon the construction project.
The Aggregate Demand Method estimates construction transport trips and fleet sizes using regional estimates of population, combined with derived default parameters. The magnitude of these trips will vary from city to city, depending on differences in local growth rates. The distribution of the construction transport activity within the region also will vary. Ideally, two sets of factors should be developed for these trips, one for activity during the construction season and one for activity outside of the construction season. It may even be useful to consider regional differences in construction activity relative to weather conditions (such as cold weather climates). Table 2.18 describes the travel behavior of this category of vehicle.
| Travel Behavior Category | Description | Estimates |
|---|---|---|
| Fleet Size | Average number of vehicles estimated from commercial vehicle survey data from Detroit, Atlanta, Denver, and Piedmont Area in North Carolina. | Fleet size = 0.009 per number of area employees (data from four cities). |
| Trip/Tour Length | Average mileage for construction trips is based on commercial vehicle survey data collected for an average day. Range is 31 to 58 miles per vehicle per day, with smaller cities quoting less mileage on average. | Average 43 daily miles per vehicle per day and 12.6 miles per trip (data from 13 cities). |
| Vehicle Trips | Average number of trips estimated from commercial vehicle survey data from Detroit, Atlanta, Denver, and Piedmont Area in North Carolina. | Daily average number of trips per vehicle is 4.1, with a range of 1.8-4.8 daily trips per vehicle (data from four cities). |
| Vehicle Miles Traveled | VMT is under two percent of overall VMT. This number will increase around construction sites. | Average percent VMT is 0.6 percent, with a high of 1.4 percent and a low of 0.02 percent (data from 13 cities). |
The Network-based Quick Response Method uses national or other available default values for estimating the effects of construction transport vehicles within urban areas. Most of the default values shown in Table 2.19 are derived from commercial vehicle surveys for Atlanta, Denver, Detroit, and the Triad cities (Greensboro, High Point, and Winston-Salem, North Carolina).
| Travel Behavior Category | Description |
|---|---|
| Trips/Tours | Cross-classification or regression models can be used with variables for area residents (population and households). There are 0.04 daily trips per worker (data from four cities). Cross-classification or regression models also can be used with variables for area workers. There are 0.02 daily trips per person (data from four cities). |
| Distribution | Distribution of construction trips will vary depending on the types of construction activities and are concentrated around areas of new growth. |
| Vehicle Type | Light-duty trucks make up the majority of vehicle types. (See Table 2.20 for details.) |
| Time of Day | The highest percentage of construction trips occurs between 9:00 a.m. and 3:00 p.m. There are eight percent in the a.m. peak, 21 percent in the p.m. peak, 61 percent in the midday and 10 percent at night (data from four cities). |
| Assignment | Heavy construction vehicles will tend to stay on higher functionally classified roadways until they approach their trip ends. |
The rate of change of population or employment may prove to be a stronger indicator of construction activity than the actual number of population or employment, since construction only occurs when population changes.
As illustrated in Table 2.20, light-duty vehicles make the majority of trips from the construction transport commercial vehicle group. Heavy-duty vehicles make up the second largest number of trips. Light-duty vehicles make the most trips, possibly because they not only transport construction materials and equipment, but also account for business and personal service trips made from the construction site.
| Vehicle Type | Atlanta | Denver | Detroit | Triad | Weighted Average |
|---|---|---|---|---|---|
| Heavy | 33.21% | 34.38% | 24.49% | 88.89% | 35.56% |
| Medium | 0.80% | 28.46% | 36.55% | 7.41% | 14.49% |
| Light | 65.99% | 37.16% | 38.96% | 3.70% | 49.95% |
Safety vehicles include both publicly and privately operated vehicles, such as police cars, fire trucks, ambulances, tow trucks, tow truck wreckers, snow plows, and sanders.
The Aggregate Demand Method estimates fleet size for safety vehicles based on a number of demographic factors:
A summary of the travel behavior characteristics is provided in Table 2.21. This summary includes estimates of fleet size, trips, and VMT, calculated from a statistical analysis of the data available combined with demographic data. The estimate of trips per vehicle was derived from the Detroit commercial vehicle survey. The percent of VMT was estimated and presented in the Magnitude and Distribution of Commercial Vehicle Travel report.
| Travel Behavior Category | Description | Estimates |
|---|---|---|
| Fleet Size | Fleet size can be estimated as a function of government employment, total employment, and population. | 0.0006 per total population + 80 per the percent of government employment (data from five cities). |
| Trip/Tour Length | There is a wide range of mileages for this category due to differences in vehicle use. Mileages are estimated from VIUS data, assuming 365 days of operation per year, from Detroit survey data on tow trucks and snow plows and from samples of police departments. | The average mileage in this category varies from 22-31 miles per day for police cars to 47-100 miles per day for tow trucks or nine miles traveled per tow truck trip on average. |
| Vehicle Trips | Trips per vehicle can be derived from commercial vehicle surveys, including public and private safety vehicles. | 5.4 daily trips per vehicle (based on Detroit data for private vehicles). |
| Vehicle Miles Traveled | Safety vehicles represent a small share of total VMT and a small share of service-related commercial vehicle VMT (7 percent). | 0.4 percent of total VMT (based on data from five cities). |
Only five cities in the project team's collection of data on commercial vehicles include safety vehicles, and only one city includes vehicle trips and mileages. Further data is needed to more accurately evaluate travel behavior for safety vehicles. Table 2.22 presents a summary of the travel behavior characteristics for the Network-based Quick Response Method, based on data from the Detroit commercial vehicle survey.
Safety trips can be estimated as a function of government employment and some combination of other types of employment. The Detroit commercial survey has tow trucks and snow plows in a wide variety of industries, probably because many of these are privately owned and operated. This survey does not include government vehicles, so it cannot be used to estimate trips by public safety vehicles. Estimates of trips could likely be based on population and some estimate of crash statistics, which could be constructed from the network as a level of service variable. Additional data are required to develop these quick response parameters.
| Travel Behavior Category | Description of Methods |
|---|---|
| Trips/Tours | Cross-classification or regression models can be used with variables for acreage and employment. Government employment is the most likely variable, but this was not available for testing in Detroit. Accident statistics also may be a significant variable, but may not be available. |
| Distribution | Safety vehicles are distributed widely throughout the region and could be distributed with a gravity model. Vehicles that are cruising will have different distribution characteristics than vehicles that are dispatched on demand. |
| Vehicle Type | Most safety vehicles are passenger autos (49 percent). The remainder are primarily fire trucks (27 percent) and tow trucks (21 percent) with a small percentage of ambulances (three percent). These are derived from vehicle registration for four cities. There are 49 percent light-duty vehicles, 51 percent medium-/heavy-duty. |
| Time of Day | Safety vehicles operate at all times of day, but with greater frequency during peak periods. There are 63 percent of trips in peak hours, 26 percent of trips in midday, and 11 percent at night (based on Detroit survey). |
| Assignment | Safety vehicles drive on all routes and the distribution of traffic by facility type is expected to be similar to the distribution for the full population. |
Safety vehicle trips can be distributed using a gravity model with friction curves estimated from commercial vehicle surveys of safety vehicles. Safety vehicle trips from the commercial vehicle survey data are plotted to represent the trip length frequency of all safety trips and then the friction curve is estimated to produce a similar trip length frequency for estimated safety trips. The safety vehicles in the Detroit survey are one source for this estimation process, but additional surveys are needed for reliable statistical estimation. Estimates of average mileage are derived from a combination of the Vehicle Inventory and Use Survey (VIUS) data and the Detroit survey to produce a national average. (VIUS provides data on the physical and operational characteristics of the nation's truck population.)
There are two types of trip patterns for safety vehicles: cruising (police patrols and highway tow trucks) and emergency response (fire and ambulance trucks). Cruising vehicles are either uniformly distributed within an area or along specific routes (such as highways). Emergency response vehicles make trips on demand to specific locations, which are randomly distributed within the service area. The tow trucks are more likely to be active during peak hours (both for cruising and emergency response) because of the higher likelihood of accidents from higher volumes, leading to an overall increase in safety vehicles traveling during peak periods.
The Detroit commercial vehicle survey provides time-of-day data for 132 safety vehicle trips. The survey includes only private sector safety vehicles and should be supplemented with data from government sources to ensure a more representative sample.
Utility vehicles include publicly and privately operated vehicles such as garbage trucks, public utility trucks, hazardous waste trucks, water/irrigation trucks, and vehicles used for maintenance, electrical, and plumbing services. The private sector vehicles used for maintenance, electrical and plumbing services have been included in this category rather than business and personal services because of the similarity in trip-making characteristics to the public utility vehicles. They are classified based on the industry or land use (utilities) being served or on the cargo (electrical) being carried or on the purpose (fuel/service vehicle).
The Aggregate Demand Method estimates fleet size for safety vehicles based on a number of demographic factors:
A summary of the travel behavior characteristics is provided in Table 2.23. This summary includes estimates of fleet size, trips, and VMT, calculated from a statistical analysis of the data available combined with demographic data. The estimate of daily trips per vehicle was derived from the commercial vehicle surveys, which did not include public utility vehicles. The percent of VMT was estimated and presented in the Task 3 report, Magnitude and Distribution of Commercial Vehicle Travel.
| Travel Behavior Category | Description | Estimates |
|---|---|---|
| Fleet Size | Fleet size can be estimated as a function of population. | 0.001 per population (data from six cities). |
| Trip/Tour Length | National average miles traveled are derived from VIUS data, based on 260 days of operation per year. Local average mileage is derived from commercial vehicle survey data for three cities and is much lower than the VIUS estimate, probably because it only includes private utility vehicles. | 60 daily miles traveled on average per vehicle from VIUS, 22 daily miles traveled per vehicle from CV surveys (average 6.4 miles per trip). |
| Vehicle Trips | Daily trips per vehicle can be derived from commercial vehicle surveys, including public and private utility vehicles. | 3.5 daily trips per vehicle (data from three cities). |
| Vehicle Miles Traveled | Utility vehicles represent a small share of total VMT and a small share of service-related commercial vehicles (6 percent). | 0.3 percent of total VMT (data from six cities). |
Only six cities in the project team's collection of data on commercial vehicles include utility vehicles, and only three cities include data on vehicle trips and mileages. These three, in turn, only represent private utility vehicles. Further data are needed to more accurately evaluate travel behavior for utility vehicles. Table 2.24 presents a summary of the travel behavior characteristics for the Network-based Quick Response Method, based on data from the Detroit commercial vehicle survey.
| Travel Behavior Category | Description |
|---|---|
| Trips/Tours | Cross-classification or regression models can be used with variables for population, acreage and employment. Government employment is the most likely variable, but this was not available for testing in Detroit. |
| Distribution | Utility vehicles are distributed widely throughout the region and could be distributed with a gravity model. There are likely higher concentrations of utility vehicles in areas of high employment. |
| Vehicle Type | Most utility vehicles are trucks (43 percent), with the remainder passenger autos (30 percent) and garbage trucks (27 percent) (data from four cities). |
| Time of Day | Utility vehicles operate at all times of day, but more frequently during the normal working hours. There are 55 percent in the p.m. peak, 41 percent in the midday, four percent at night (data from three cities). |
| Assignment | Utility vehicles operate on all facilities. |
Public service vehicles include publicly operated vehicles such as city, county, state, and Federal government vehicles, as well as vehicles used to serve schools and colleges.
The Aggregate Demand Method estimates fleet size for public service vehicles based on two demographic factors: government employment and population.
A summary of the travel behavior characteristics is provided in Table 2.25. This summary includes estimates of fleet size, trips, and VMT, calculated from a statistical analysis of the data available combined with demographic data. A source of data for trips per vehicle is unavailable because none of the commercial vehicle surveys include government vehicles. The percent of VMT was estimated and presented in the Magnitude and Distribution of Commercial Vehicle Travel report.
| Travel Behavior Category | Description | Estimates |
|---|---|---|
| Fleet Size | Fleet size can be estimated as a function of population. | 0.005 per population (data from four cities). |
| Trip/Tour Length | Public service vehicles have low rates of mileage on average because they are not always in use every day, but on demand by government and education employees. National average miles traveled are derived from VIUS data, based on 260 operating days per year. | 29 average miles per day (data from four cities in California). |
| Vehicle Trips | Daily trips per vehicle can be derived from a government vehicle survey. | N/A |
| Vehicle Miles Traveled | Public service vehicles represent a larger share of total VMT in capital cities (3.6 percent) than in other cities (0.6 to 1.2 percent). | 1.6 percent of total VMT (data from four cities). |
Only four cities in the project team's collection of data on commercial vehicles include public service vehicles, and none include data on vehicle trips and mileages. One of the four cities is a capital city, which has very different travel characteristics for public service vehicles than a non-capital city. Further data is necessary to more accurately evaluate travel behavior for public service vehicles. Table 2.26 presents a summary of the travel behavior characteristics for the Network-based Quick Response Method, based on data from the Detroit commercial vehicle survey.
| Travel Behavior Category | Description of Methods |
|---|---|
| Trips/Tours | Cross-classification or regression models can be used with total and government employment. There are 0.06 per government employment or 0.01 per total employment (data from three cities). |
| Distribution | Public service vehicles are distributed widely throughout the region but are more concentrated in areas of government and education employment. |
| Vehicle Type | All public service vehicles are light-duty vehicles, based on the definition of vehicles in this category from the registration records. |
| Time of Day | Public service vehicles operate primarily during weekday business hours and probably have a relatively uniform distribution during these hours. The project team found no data on time of day for public service vehicles. |
| Assignment | Public service vehicles operate on all facilities. |
Business and personal service vehicles include those used by realtors, door-to-door salespersons, and others who do not have a fixed business address. Business and personal service vehicles are seldom included in travel demand models due to the inherent difficulty in separating out business-related trips from other trips in traditional household surveys. The number of business and personal service vehicles will be a function of the level of employment in out-of-office services, the population of the urban area, and land use.
The Aggregate Demand Method estimates business and personal service trips and fleet sizes using regional estimates of population combined with derived default parameters, as shown in Table 2.27.
| Travel Behavior Category | Description | Estimates |
|---|---|---|
| Fleet Size | Fleet size can be estimated from population. Total employment does not appear to be as significant as population. | 0.02 vehicles per person (data from eight cities). |
| Trip/Tour Length | These average mileage and range are derived from three commercial vehicle surveys and the national average from the VIUS. | Average mileage is 46 miles per vehicle per day (or 15 miles per trip) and ranges from 27 to 78 miles per day (data from three cities and national average from the VIUS). |
| Vehicle Trips | Average number of daily trips estimated using commercial vehicle survey data from Detroit, Atlanta, and Denver. | Daily average number of trips per vehicle is 3.0 with a range of 1.8-5.1 daily trips per vehicle (data from three cities). |
| Vehicle Miles Traveled | Business and personal services is the largest percentage of overall commercial VMT. | 3.6 percent of total VMT (data from seven cities). |
Business and personal trip data derived from multiple commercial vehicle surveys can be used to estimate travel behavior characteristics for the Network-based Quick Response Method. Table 2.28 presents a summary of these characteristics.
From the survey data available, production of business and personal trips was best represented by the employment within an area. Estimates of trip attractions are based on the population within an area. Because both productions and attractions are based on a sample of only three cities, more data is required to develop better estimates.
Trip distribution may be performed using a gravity model. An average trip length of 15.3 miles was determined using the VIUS data and 12.7 miles was determined using the commercial vehicle surveys.
| Travel Behavior Category | Description of Methods |
|---|---|
| Trips/Tours | Cross-classification or regression models can be used with population and employment variables. There are 0.03 daily trips per worker or 0.01 daily trips per person (data from three cities). |
| Distribution | Business and personal service vehicles are distributed widely throughout the region, with a focus on non-CBD areas. Business and personal service trips can be estimated using the gravity model or destination choice. |
| Vehicle Type | Business and personal service trips are generally made in passenger autos, pickup trucks, minivans or SUVs; they may be available from the DMV. There are 32 percent cars, 26 percent pickups, 16 percent vans, nine percent SUVs and 17 percent medium and heavy trucks. |
| Time of Day | Business and personal trips occur throughout the workday, with half taking place between 9:00 a.m. and 3:00 p.m. There are 11 percent in the a.m. peak, 53 percent at midday, 22 percent in the p.m. peak, and 14 percent at night (data from three cities). |
| Assignment | Business and personal service trips are likely to be uniformly distributed among freeways, arterials and local streets. |
