Comprehensive Truck Size and Weight Limits Study - Modal Shift Comparative Analysis Technical Report

Appendix G: VMT and Weight Distribution Estimates Methodology

The USDOT's Comprehensive Truck Size and Weight Study, 2014 (2014 CTSW Study) compiled all relevant data, including (1) vehicle classification and weigh-in-motion (WIM) data collected by the states and reported via the Vehicle Travel Information System (VTRIS) and Traffic Monitoring Analysis System (TMAS) data reporting systems, (2) tables of VMT published on the FHWA website, (3) a custom control-total spreadsheet that includes VMT totals by broad vehicle and highway types for ten groups of states, and (4) WIM data collected under the long-term pavement performance (LTPP) program. Most data covered years from 2010 through 2013, and all data were adjusted to control totals for 2011.

FHWA's process for estimating VMT data started with the 2012 control-total spreadsheet. We adjusted these control totals based on the 2011 VM1 table version that was included on FHWA's website on late January 2014. We factored the 2012 spreadsheet totals up or down so that we precisely matched the 2011 VM1 tables. Using vehicle classification data and the January 2014 website version of FHWA's VM-2 table, we split the control totals for the groups of States, broad classes of vehicle types, and groups of highway types into the 13 vehicle types estimated in the classification data, 12 functional highway classes, and 51 States, adjusting the auto estimates such that the 2011 VM2 tables were precisely matched. Using WIM data, we further split the 13 vehicle types into 28 detailed vehicle classes (VC) and 100 operating weight groups (OGW) needed for the CTSW Study, and developed detailed arrays of axle weights and types for each combination of VC and OGW.

The detailed breakdowns were aggregated to the levels of detail required for each phase of analysis of the 2014 CTSW Study. Bridge analysis, for example, required arrays of axle weights and types for two broad groups of States and with all vehicle classes and OGWs grouped together. Pavement analysis required grouping by the ten regions used earlier (groups of states chosen based on similar truck size and weight characteristics), and required aggregating the 24 truck classes into no more than 10. By starting with the full level of detail needed for all phases of the study, all the phases were able to use the same set of travel data, aggregating as needed to suit their purposes.

VMT Control Totals

The table below shows FHWA's estimated 2012 control totals (in millions of VMT) for broad classes of vehicles on six types of highways in each of ten groups of States (or regions).

Table G1. VMT Control Totals Provided by FHWA
Region/Hwy Type	Auto /MC	Light Trucks	Bus	Single Unit	Combination	Total VMT
1	230,142.388	58,827.182	1,893.698	10,951.156	18,043.832	319,858.256
Rural Arterial	18,048.666	5,891.008	160.022	1,138.271	2,638.827	27,876.795
Rural Interstate	13,333.829	3,447.334	191.004	831.124	4,769.184	22,572.475
Rural Other	32,291.238	12,172.477	238.690	2,123.543	1,469.261	48,295.209
Urban Arterial	80,633.485	18,019.783	695.006	3,290.113	3,184.486	105,822.872
Urban Interstate	38,372.192	7,265.506	346.003	1,572.613	5,188.615	52,744.929
Urban Other	47,462.978	12,031.074	262.973	1,995.491	793.459	62,545.975
2	102,317.575	19,369.021	669.198	3,683.845	3,284.450	129,324.089
Rural Arterial	7,309.897	1,806.959	72.505	495.503	372.679	10,057.542
Rural Interstate	4,695.168	1,014.173	56.251	280.643	533.630	6,579.864
Rural Other	8,599.914	2,446.979	76.038	603.400	298.473	12,024.805
Urban Arterial	41,002.226	7,024.977	214.561	1,106.878	608.372	49,957.013
Urban Interstate	24,294.251	3,568.661	185.803	702.945	1,347.966	30,099.626
Urban Other	16,416.120	3,507.272	64.039	494.476	123.331	20,605.238
3	831,798.463	226,999.903	6,274.314	38,911.724	62,443.344	1,166,427.749
Rural Arterial	99,045.487	33,847.309	853.946	6,020.198	9,954.758	149,721.698
Rural Interstate	60,807.820	16,954.380	784.441	3,449.638	20,170.334	102,166.613
Rural Other	104,195.560	38,837.777	922.285	6,881.630	4,778.102	155,615.355
Urban Arterial	286,972.423	68,991.874	1,622.023	11,128.811	9,259.945	377,975.075
Urban Interstate	128,970.507	30,297.857	1,016.417	5,226.790	14,737.913	180,249.485
Urban Other	151,806.666	38,070.707	1,075.202	6,204.656	3,542.292	200,699.523
4	69,962.445	18,001.568	99.375	1,660.805	4,823.793	94,547.987
Rural Arterial	9,394.728	2,931.631	20.265	247.230	915.966	13,509.819
Rural Interstate	3,624.472	857.857	12.693	104.191	689.670	5,288.884
Rural Other	7,512.484	3,107.282	4.546	252.310	488.709	11,365.331
Urban Arterial	29,725.355	5,692.169	33.571	581.899	1,268.242	37,301.237
Urban Interstate	11,398.468	2,601.995	28.300	267.274	1,425.988	15,722.025
Urban Other	8,306.937	2,810.635	-	207.901	35.218	11,360.691
5	187,276.324	40,164.319	1,585.871	8,858.397	14,365.355	252,250.265
Rural Arterial	29,166.287	7,896.345	280.810	2,031.508	3,106.958	42,481.908
Rural Interstate	14,100.153	2,980.894	194.054	919.745	4,758.350	22,953.198
Rural Other	25,707.647	6,426.714	239.210	1,742.493	1,015.664	35,131.728
Urban Arterial	63,167.416	11,524.719	478.089	2,527.600	2,065.579	79,763.404
Urban Interstate	27,232.586	6,710.331	221.803	1,002.589	3,122.641	38,289.950
Urban Other	27,902.235	4,625.315	171.904	634.462	296.162	33,630.077
6	55,933.755	27,815.150	462.544	2,797.015	7,502.814	94,511.279
Rural Arterial	10,499.871	7,035.578	122.128	764.575	2,186.659	20,608.811
Rural Interstate	6,606.522	3,026.633	54.393	351.011	3,018.520	13,057.079
Rural Other	6,157.461	5,315.492	95.668	537.486	876.911	12,983.018
Urban Arterial	18,840.905	7,427.276	109.021	580.893	603.087	27,561.182
Urban Interstate	7,808.644	1,860.979	32.047	288.747	651.352	10,641.770
Urban Other	6,020.352	3,149.191	49.287	274.303	166.286	9,659.419
7	39,203.433	16,268.682	163.380	3,263.318	2,655.273	61,554.084
Rural Arterial	3,799.037	1,953.920	19.130	456.525	394.951	6,623.562
Rural Interstate	3,302.918	1,381.109	17.362	308.120	591.747	5,601.255
Rural Other	3,963.446	2,201.943	17.277	508.098	330.430	7,021.195
Urban Arterial	15,509.561	5,674.335	58.761	1,083.654	604.165	22,930.476
Urban Interstate	7,634.792	2,738.777	28.163	464.150	528.867	11,394.749
Urban Other	4,993.679	2,318.598	22.687	442.771	205.113	7,982.847
8	68,639.914	26,488.572	761.506	6,869.749	9,289.014	112,048.756
Rural Arterial	9,396.810	5,018.872	140.683	1,358.451	1,593.023	17,507.840
Rural Interstate	6,487.680	3,155.938	74.891	748.073	3,545.053	14,011.634
Rural Other	7,229.228	4,638.608	124.737	1,261.134	1,016.722	14,270.428
Urban Arterial	21,745.139	6,551.912	194.217	1,467.093	995.912	30,954.273
Urban Interstate	9,527.176	3,884.008	63.028	1,031.525	1,668.656	16,174.393
Urban Other	14,253.881	3,239.235	163.950	1,003.473	469.650	19,130.188
9	260,482.111	104,720.560	1,615.655	17,133.808	28,068.647	412,020.781
Rural Arterial	31,553.921	16,696.547	233.001	3,314.790	6,600.044	58,398.303
Rural Interstate	18,920.528	7,625.758	225.897	1,444.271	7,812.748	36,029.202
Rural Other	24,719.519	15,486.602	213.761	3,357.862	3,572.955	47,350.700
Urban Arterial	110,004.623	36,168.935	507.190	5,199.245	4,839.161	156,719.154
Urban Interstate	40,774.431	13,053.516	245.108	2,077.673	4,329.993	60,480.721
Urban Other	34,509.089	15,689.201	190.698	1,739.968	913.746	53,042.701
10	238,897.915	62,432.858	1,229.024	10,830.288	12,881.574	326,271.659
Rural Arterial	15,978.958	5,763.398	133.389	1,366.605	1,925.327	25,167.676
Rural Interstate	10,489.477	3,444.831	63.402	811.895	2,802.008	17,611.613
Rural Other	9,758.448	3,722.067	98.764	692.820	468.759	14,740.859
Urban Arterial	123,835.992	29,261.720	538.559	5,189.753	4,373.754	163,199.779
Urban Interstate	51,892.235	12,148.063	192.499	1,904.365	2,612.487	68,749.649
Urban Other	26,942.805	8,092.778	202.411	864.849	699.240	36,802.083
Grand Total	2,084,654.324	601,087.814	14,754.565	104,960.105	163,358.097	2,968,814.904

Splitting VMT among States, Highway Functional Classes, and 13 FHWA Vehicle Classes

The USDOT study team used available 2012 and 2013 classification data in the newer "TMAS" format, as well as some 2011 and 2012 classification data in the older "VTRIS" format. We processed all the files and summarized total counts by the 13 FHWA vehicle classes for each station. We obtained data from a total of 1,756 classification stations, although some of the stations had much less than the hoped for 24/7/365 data. We used station description files to assign a highway functional class to each station in each State and compiled tables of total vehicle counts for each functional class and State. After assembling this data, we found that the data covered about 40 percent of the functional class / state combination, so we opted to use older, more complete data to cover the gaps. Using the combination of new and old data as well as observed differences in truck percentages as we move to the lower functional classes, we derived a preliminary (unadjusted) estimate of vehicle class proportions for the 13 classes on each highway functional class in each State. FHWA publishes annual estimates of travel by highway type and state (VM-2 table). We applied the preliminary set of vehicle class proportions to the traffic volumes from the January 2014 FHWA website version of the 2012 VM-2 table to convert the vehicle class proportions into preliminary estimates of VMT. As described in the next section, we used WIM data to refine and expand these preliminary estimates. Splitting VMT into 28 Vehicle Classes Used in CTSW Study In FHWA's classification data, vehicles are classified based solely upon the measured number of vehicle axles and their axle spacings. The advantage WIM measurements offer is that the number of vehicle axles and their spacing are also measured along with the weight of each axle. On the other hand, virtually all the WIM data we obtained came from vehicles traveling in only one lane of a multilane facility, so was very likely biased in the population of vehicles observed. Further, light vehicles were usually filtered out of weight compilations, so we could not use WIM data to derive truck percentage estimates. The team assumed, however, that the right-lane / other-lanes biases were similar for subclasses of the 13 FHWA classes, thus allowing reasonably accurate splitting or reassignment of each class.

As with past studies that have evaluated the effects of truck size and weight policy, the 2014 CTSW Study needs to classify heavier trucks into more categories than are included in the 13-class scheme to allow evaluation of differential changes in travel patterns for particular vehicle configurations (seven-axle triples vs. nine-axle triples, for example). Further, the axle weight distributions for subsets of some of the 13 classes are apt to vary substantially among themselves. Better differentiation among the subsets allows a higher degree of precision in the analysis.

The 2014 CTSW Study used 28 vehicle classes, listed in the table below.

Table G2. Vehicle Classifications Used in the 2014 CTSW Study
Class	Name	Description
1	Auto / MC	Auto and motorcycle
2	LT4	Light truck with 4 tires
3	SU2	Single-unit truck with 6 tires
4	SU3	Single-unit truck with 3 axles
5	SU4+	Single-unit truck with 4 or more axles
6	CS3	Tractor-semitrailer with 3 axles
7	CS4	Tractor-semitrailer with 4 axles
8	3S2	3-axle tractor, 2-axle tandem-axle semitrailer
9	Oth CS5	Other tractor semitrailer with 5 axles
10	3S3	3-axle tractor, 3-axle tridem-axle semitrailer
11	Oth CS6	Other tractor semitrailer with 6 axles
12	CS7+	Tractor-semitrailer with 7 or more axles
13	CT3/4	Truck-trailer with 3 or 4 axles
14	CT5	Truck-trailer with 5 axles
15	CT6	Truck-trailer with 6 axles
16	CT7	Truck-trailer with 7 axles
17	CT8	Truck-trailer with 8 axles
18	CT9+	Truck-trailer with 9 or more axles
19	DS5	Double trailer truck with 5 axles
20	DS6	Double trailer truck with 6 axles
21	DS7	Double trailer truck with 7 axles
22	DS8	Double trailer truck with 8 axles
23	DS9+	Double trailer truck with 9 or more axles
24	TS7	Triple trailer truck with 7 axles
25	TS8	Triple trailer truck with 8 axles
26	TS9+	Triple trailer truck with 9 or more axles
27	Bus2	Bus with 2 axles
28	Bus3	Bus with 3 axles

The team constructed a detailed vehicle classification algorithm that built upon the weight/spacing algorithm used for compiling the LTPP WIM data. By using a combination of axle weights and spacings, we could much more accurately assign vehicles to the correct class.

The team drew upon two sources of WIM data: (1) data submitted to FHWA by each State as part of its traffic monitoring program, and (2) data collected at each LTPP WIM site and compiled by FHWA. The state-supplied data came from 451 WIM stations and included nearly 400 million vehicle observations; the LTPP data included about 250 million weight observations from 19 sites. Most WIM data were from 2010 to 2013.

The team applied the classification algorithm to all the truck weight observations and cross-tabulated the axle-spacing-only, initial 13 classes with the assignment of the same vehicles based on the 28-class, weight-and-spacing algorithm. We developed a cross-tabulation array for each State that allowed us to reassign the 13-class VMT estimates into more accurate 28-class estimates for each State and functional class. Three States, Alaska, North Carolina, and North Dakota, did not have sufficient WIM data to develop their own reassignment arrays, so we used substitute reassignment arrays from the nearby states of Washington, South Carolina, and South Dakota, respectively.

The team proportionally adjusted each of the 28 vehicle class VMTs in each State and functional class such that we precisely matched the FHWA control totals for each region and highway type.

Adjusting VMT to 2011 Published Control Totals

In addition to the VM-2 table described in the previous section, FHWA publishes annual estimates of travel by broad type of vehicle in the VM-1 table. Since the 2014 CTSW Study had settled upon 2011 as the year of analysis, the study team adjusted the 2012 control total estimates to match the published control totals for 2011. Because the year-to-year changes were relatively small, and because we had relatively little interest in travel estimates for the predominant two broad classes (auto/motorcycle and light truck), the team opted for an easily-replicable, three-step adjustment approach rather than a more complicated iterative-proportional-splitting technique.

FHWA first multiplied the VMT estimates for all vehicles in each state and functional class by the ratio of the corresponding 2011 to 2012 VM-2 table estimates. We then calculated ratios of VMT from the 2011 VM1 table (the version posted on the FHWA website on January 22, 2014) to the grand totals for all the vehicles in each broad type of vehicle. Finally, we adjusted auto / motorcycle VMT as needed so that total VMT for all vehicles in each FHWA calibration cell (region / highway type combination) remained unchanged.

Operating Weight and Axle Weight Distributions

The study team used the same WIM data described in a previous section to derive operating gross weight (OGW) and axle weight distributions for use in various phases of the 2014 CTSW Study. The OGW distributions consist of estimates of proportions of VMT in each 2,000-lb. OGW increment with upper bounds from 2,000 to 198,000 lbs., as well as a final increment of 198,001 lbs. and up. There is a unique OGW distribution for each of the 10 regions. For individual vehicle classes, OGW distributions are assumed to be the same on all highway functional classes within a region. This assumption was necessary because there was insufficient WIM data to develop separate OGW distributions by highway class. The two graphs below provide a good overview of the overall distribution of vehicle classes and operating weights considering all highway travel in the base year. The first graph excludes travel by light vehicles and two-axle trucks to highlight the larger truck classes. Note the dominance of the common 3-S2 configuration when considering all travel on all highways.

Figure G3. VMT by OGW and Truck Class, All Highways

The next graph removes the two most common classes (SU3 and 3S2) to show the relative importance of the remaining truck classes.

Figure G4. VMT by OGW and Truck Class, All Highways without SU3 and 3S2

Axle weight distributions consist of numbers of axle per vehicle falling into each of four axle types (steering axle, single load axle, tandem load axle, and tridem load axle) and 40 weight groups for each type of axle (centered on 1,000-lb. categories for single axles, 2,000-lb. categories for tandem axles, and 3,000-lb. categories for tridem axles). For example, weight group 1 for single axles covers axles from 1 to 1,500 lbs.; group 2 includes axles from 1,501 to 2,500 lbs., and so on. Group 40 includes single axles operating at 39,501 lbs. and above. Tandem axle group 1 includes axles from 1 to 3,000 lbs., group 2 axles from 3,001 to 5,000 lbs., etc.

Each OGW of each vehicle class in each region has a unique axle weight and type distribution. The figure below illustrates a sample of tandem axle weight distributions for selected 3-S2 vehicles in one traffic region. Note the range of prevalent axle weights within a given operating weight group-an important factor to consider when evaluating the relative impacts of a particular configuration operating a particular gross vehicle weight.

Figure G5. 3-S2 Tandem Axle Weights by OGW (Kips)

For the bridge analysis, all axle weights and types for all vehicle classes are grouped together, and the 12 functional classes are grouped into three highway types for each of two regions. For the pavement analysis, the 28 vehicle classes are grouped into 8 classes, all OGWs in each class are grouped together, and the 12 functional classes are grouped into 3 highway types. Other phases of analysis require other groupings of the data.

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