Accounting for Commercial Vehicles in Urban Transportation Models

Summary Report

5.0 Magnitude and Distribution

The magnitude and distribution of commercial vehicles in each of 12 commercial vehicle categories were estimated from available data sources. The magnitude was estimated using the total fleet size and fleet size per capita. The distribution was estimated using the vehicle miles traveled, the percentage of total vehicle miles traveled and the average vehicle miles traveled per day.

5.1 Analysis by Commercial Vehicle Category

Table 5.1 presents the summary statistics of the percentage of total VMT for each commercial vehicle category. These data are derived from a variety of data sources. It is important to recognize that the data sources may not be fully compatible, although we tried to achieve compatibility wherever possible.

Table 5.1
Percent of Total VMT
		All Data
		Minimum	Maximum	Average*
Vehicle Type
1	School Bus	0.03%	0.55%	0.15%
2	Fixed Shuttle Services	0.00%	0.04%	0.02%
3	Private Transportation	0.09%	0.46%	0.21%
4	Paratransit	0.00%	0.01%	0.01%
5	Rental Cars	0.83%	4.29%	1.98%
6	Package, Product and Mail Delivery	0.00%	0.71%	0.18%
7	Urban Freight Distribution, Warehouse Deliveries	1.01%	4.87%	2.73%
8	Construction Transport	0.02%	1.35%	0.62%
9	Safety Vehicles	0.14%	1.25%	0.44%
10	Utility Vehicles	0.03%	1.03%	0.35%
11	Public Service Vehicles	0.57%	3.48%	1.56%
12	Business and Personal Services	0.66%	6.97%	3.55%
	Movement of People (Categories 1 through 5)	1.0%	5.4%	2.4%
	Movement of Goods (Categories 6 through 8)	1.0%	6.9%	3.5%
	Services (Categories 9 through 12)	1.4%	12.7%	5.9%
	TOTAL	3.39%	25.01%	11.79%

NOTE: The averages for each group of categories (people, goods and services) are based on weighted averages rather than direct averages and may not be the same as the sum of the categorical averages.

Distribution of commercial vehicles ranged from three to 25 percent of total vehicle miles traveled, across all categories, with an average of 12 percent. This was highest for vehicles providing services (13 percent) based on the percent of total vehicle miles traveled. The maximum percent of total vehicle miles traveled was used to evaluate the individual categories. Urban freight distribution and business and personal services (five and seven percent, respectively) had the highest percent of total vehicle miles traveled, next highest was rental cars (four percent) and public service vehicles (three percent). All other categories had less than two percent of total vehicle miles traveled (maximum).

The magnitude and distribution also was evaluated across time periods and facility types, but these data were not sufficient to stratify the data by urban area or commercial vehicle category. Based on data from the commercial vehicle surveys, the majority of commercial vehicles operate in the off-peak hours (58 percent). The a.m. peak period of three hours (31 percent) has quite a bit more travel than the p.m. peak period of three hours (11 percent). The distribution of commercial vehicles by facility type is based on data in the Freight Analysis Framework. This shows that freight and non-freight trucks have higher allocation of vehicle miles traveled on interstates and lower allocation of vehicle miles traveled on arterials than autos.

5.2 Aggregated Categories

In our original analysis, the commercial vehicle categories were aggregated into three types of vehicles, based on trip purpose. These three types were moving people, moving goods, or providing services. Table 5.2 presents a summary of fleet sizes per 1,000 population for selected urban areas by these aggregated categories. This table includes only urban areas with either a commercial vehicle survey or DMV data. At this aggregated level, the following conclusions can be drawn:

Table 5.2
Summary of Fleet Size per 1,000 Population by Aggregated Category and Data Source for Selected Urban Areas
					Percent
	Moving People	Moving Goods	Providing Services	Total	Moving People (%)	Moving Goods (%)	Providing Services (%)
Los Angeles	7.82	8.59	35.20	51.60	15%	17%	68%
San Francisco	23.70	11.32	50.50	85.50	27%	13%	60%
Detroit	0.59	14.20	5.40	20.20	2%	81%	18%
Atlanta	1.34	27.52	8.69	37.55	4%	73%	23%
San Diego	5.15	5.84	26.30	37.30	14%	16%	70%
Denver	0.81	23.80	8.70	33.20	2%	69%	29%
Sacramento	8.07	13.98	66.60	87.70	9%	16%	75%
Winston-Salem	1.82	16.60	0.90	19.40	3%	93%	4%
Greensboro	2.77	16.60	0.90	19.40	5%	91%	4%
High Point	0.38	16.60	0.90	17.00	1%	95%	4%
DMV Cities	11.03	9.93	44.13	65.09	17%	15%	68%
CV Survey Cities	1.28	37.36	5.06	43.70	3%	85%	12%

The inclusion of rental cars in the DMV data and not in the commercial vehicle survey data has a significant impact on the percent of vehicles moving people, with a difference of 14 percent between these two sources.
The specific definitions of the Business and Personal services and Urban Freight Distribution categories (12 and 7, respectively) for the two data sources have a significant impact on the summary totals for vehicles moving goods and providing services. In the case of the DMV data, Business and Personal Services is the dominant category, and in the case of the commercial vehicle surveys, Urban Freight Distribution is the dominant category. In both cases, the vehicles in these categories were not easily separated to create consistency in the definitions.
The Package, Product, and Mail Delivery category (#6) is dominated by fleets in the U.S. Postal Service, but these data are not clearly identified in the DMV data. From the results, it appears that U.S. Postal Service vehicles are not included in the DMV data regarding parcel delivery, but are included in the public service vehicle category. In the commercial vehicle surveys, U.S. Postal Service vehicles were excluded, and the U.S. Postal Service separately provided the necessary data for addition to the commercial vehicle survey data.
The DMV data yields 73 percent higher average per capita fleet sizes than the commercial vehicle survey because of the more comprehensive nature of these data.

From a data analysis perspective, it may be useful to combine certain categories that were unable to be stratified. This will be considered during the next task to identify methods for evaluation.