Section 7.1 Highway Assignment Validation

The ARC model employs a standard equilibrium methodology to assign vehicle trips to the transportation network using the bi-conjugate Frank-Wolfe algorithm. The assignment is assumed to reach convergence when the relative gap is less than $10^{-4}$ for three successive iterations. The highway assignments use a generalized cost function that includes travel time, toll, and distance. These values are converted to cost using value-of-time (VOT) and auto operating costs. The generalized cost function is as follows:

Cost = (time * VOT) + toll cost + (distance * operating cost)

The passenger car VOT used in the assignments is $21.50 and is based on average wage rates in the Atlanta region. The truck VOT is $36.00 and was based on a review of other truck models throughout the United States. The auto operating cost is $0.1729 per mile and includes the costs associated with fuel, maintenance, and tires using information from AAA. The AAA costs are broken down in Figure 7-1. The operating cost for trucks is $0.5360 per mile based on a fuel cost of $3.34 per gallon and a fuel efficiency of 6.2 miles per gallon. The truck costs were obtained from the American Transportation Research Institute (http://atri-online.org/wp-content/uploads/2017/10/ATRI-Operational-Costs-of-Trucking-2017-10-2017.pdf).

Figure 7-1. Auto Operating Cost

The ARC model includes highway assignments split into five time of day periods as follows:

Early AM = 3:00am to 6:00am
AM Peak = 6:00am to 10:00am
Midday = 10:00am to 3:00pm
PM Peak = 3:00pm to 7:00pm
Evening = 7:00pm to 3:00am

Each assignment includes the following vehicle classes:

SOV (non-toll eligible)
HOV 2 car (non-toll eligible)
HOV 3+ car (non-toll eligible)
SOV (toll eligible)
HOV 2 car (toll eligible)
HOV 3+ car (toll eligible)
Commercial vehicle
Medium Truck
Heavy Truck: I-285 by-pass
Heavy Truck: remaining

Section 7.1.1 Free-Flow Speeds and Capacities

Prior to the highway assignment, network link free-flow speeds and capacities are calculated based on the link facility type and area type. The area types used in the model in the subsequent tables are as follows:

ATYPE1 = CBD
ATYPE2 = Urban Commercial
ATYPE3 = Urban Residential
ATYPE4 = Suburban Commercial
ATYPE5 = Suburban Residential
ATYPE6 = Exurban
ATYPE7 = Rural

For free-flow speeds, data from the FHWA’s National Performance Management Research Data Set (NPMRDS) from 2019 were joined to ARC’s network using the Traffic Message Channels (TMCs). Average free-flow speeds were initially calculated by facility type and area type and were then modified such that the speeds for a given facility type increase as the area types transition from urban to rural. In cases where the facility type or area type did not include data, the free-flow speeds were asserted based on similar facilities. The resulting free-flow speed lookup table is provided in Table 7-1. In addition to the lookup table, several other characteristics determine the final free-flow speed including:

Ramps identified as “loop” ramps - free-flow speed set to 35 mph
Principal arterial speeds varied by number of lanes for CBD area types
Links with observed speed - free-flow speed is computed as the average of the observed early AM speed and the look table speed

Table 7-1 Free-Flow Speed Lookup Table

Facility Type	NAME	ATYPE1	ATYPE2	ATYPE3	ATYPE4	ATYPE5	ATYPE6	ATYPE7
0	Centroid connector	7	11	11	11	11	14	14
1	Interstate/freeway	62	63	63	63	64	65	66
2	Expressway	43	46	49	52	55	58	61
3	Parkway	43	46	49	52	55	58	61
4	Freeway HOV (buffer)	64	65	65	65	66	67	68
5	Freeway HOV (barrier)	64	65	65	65	66	67	68
6	Freeway truck	62	63	63	63	64	65	66
7	System to system ramp	50	50	50	55	55	55	55
8	Exit ramp	35	35	35	35	35	35	35
9	Entrance ramp	35	35	35	35	35	35	35
10	Principal arterial	23	26	31	35	41	48	53
11	Minor arterial	21	26	29	33	38	43	48
12	Arterial HOV	21	26	29	33	38	43	48
13	Arterial truck	21	26	29	33	38	43	48
14	Collector/local	17	23	24	26	30	35	45

Hourly capacities were based in part from general Level-of-Service (LOS) E Highway Capacity Manual assumptions and were then asserted such that as area types transition from urban to rural, the hourly capacities increase. Similarly, the capacities decrease from limited access facilities (e.g., interstates) to facilities with less restricted access (e.g., arterials). The LOS E hourly capacities used in the model are provided in Table 7-2.

Table 7-2 LOS E Hourly Capacities

Facility Type	NAME	ATYPE1	ATYPE2	ATYPE3	ATYPE4	ATYPE5	ATYPE6	ATYPE7
0	Centroid connector	10,000	10,000	10,000	10,000	10,000	10,000	10,000
1	Interstate/freeway	1,900	1,900	2,000	2,000	2,050	2,100	2,100
2	Expressway	1,200	1,200	1,300	1,350	1,400	1,450	1,450
3	Parkway	1,150	1,150	1,250	1,300	1,350	1,400	1,400
4	Freeway HOV (buffer)	1,900	1,900	2,000	2,000	2,050	2,100	2,100
5	Freeway HOV (barrier)	1,900	1,900	2,000	2,000	2,050	2,100	2,100
6	Freeway truck	1,900	1,900	2,000	2,000	2,050	2,100	2,100
7	System to system ramp	1,300	1,400	1,500	1,600	1,700	1,700	1,700
8	Exit ramp	800	850	850	850	850	900	900
9	Entrance ramp	900	900	950	950	1,000	1,050	1,100
10	Principal arterial	1,000	1,050	1,100	1,150	1,200	1,250	1,300
11	Minor arterial	900	900	950	1,000	1,000	1,050	1,100
12	Arterial HOV	1,000	1,050	1,100	1,150	1,200	1,250	1,300
13	Arterial truck	900	900	950	1,000	1,000	1,050	1,100
14	Collector/local	750	800	800	850	850	900	900

During the calibration of the highway assignment, the model had difficulty in replicating observed interstate speeds near major system-to-system interchanges. This is primarily due to the fact that the user equilibrium highway assignment does not account for operational issues occurring in these segments, weaving for example, or the presence of queues. As a means to improve the model’s ability to predict congested speeds at these locations, a network attribute (WEAVEFLAG) was introduced to identify the interstate links adjacent to the major interchanges. The link capacities at these locations are subjected to a modification in the originally calculated capacity when the number of lanes is greater than four. The equation is structured as follows in those cases:

Weave section capacity = Initial Capacity * $0.98^{(lanes-1)}$

Finally, to compute the period level capacity, rather than multiply the hourly capacity by the number of hours in a time period, adjustments were made to reflect the peaking that occurs within the modeled time periods. These period level adjustments were made based on available GDOT hourly traffic count data and are as follows:

Early AM = 1.25
AM Peak = 3.66
Midday = 4.70
PM Peak = 3.66
Evening = 3.91

Section 7.1.2 Volume Delay Functions

The calibration of the highway assignment included updating the volume delay functions (VDF curves). These curves are a modified version of the BPR function with coefficients that vary by facility type. The general formula for the VDF curves is as follows:

$T_c$ = T0 * A * V/C + D * ( $V/C^{B}$ )

where,

$T_c$ = congested time
T0 = free-flow time
V/C = volume to capacity ratio
A, B, D = calibrated coefficients, see Table 7-3

Graphical representation of the VDF curves is provided in Figure 7-2.

Table 7-3 VDF Curve Parameters

Facility Type	A	B	D
Freeway Basic	0.1	6.0	0.60
Freeway Weave	0.2	5.5	1.25
Expressway	0.0	4.0	1.00
Parkway	0.0	4.0	1.25
Ramp	0.1	4.0	1.00
Principal Arterial	0.1	4.0	0.45
Minor Arterial	0.1	4.0	0.45
Collector	0.1	4.0	0.45

Figure 7-2. VDF Curves

Section 7.1.3 Vehicle Miles Traveled

Validation of the highway assignment results included comparisons of regional vehicle miles traveled (VMT). To compare regional VMT, GDOT summaries of average annual daily traffic (AADT) were summarized by functional classification for each county. As the model is designed to estimate an average weekday, the AADT-based VMT was converted to represent average weekday VMT using the following conversion factors developed during the 2019 model validation efforts:

13-county interstates = 1.03
13-county non-interstate = 1.07
8-county interstates = 1.003
8-county non-interstate = 1.065

After converting the GDOT VMT at the county-level, the observed and estimated data were compared at the regional level as shown in Table 7-4. While the data is provided for both collectors and local roads, the network does not include all of these facilities in the region which explains the large differences in VMT. However, when viewing interstates, principal arterials, and minor arterials, the model is within 6% of the observed VMT.

Table 7-4 Observed vs Estimated Regional VMT

Functional_Classification	Observed	Estimated	Delta
Interstate/Freeway/Ramps	59,645,000	65,867,000	10%
Principal Arterial	30,184,000	25,739,000	-15%
Minor Arterial	44,812,000	35,615,000	-21%
Collector	20,094,000	21,363,000	6%
Local	49,257,000	11,709,000	-76%
Total	203,992,000	160,292,000	-21%
Arterial and Above	134,641,000	127,221,000	-6%
Collector and Above	154,735,000	148,584,000	-4%

Section 7.1.4 Traffic Counts

GDOT maintains an extensive traffic counting program which includes daily volume counts, vehicle classification counts, and hourly counts. Data from these count locations were used to obtain the 2019 daily counts at more than 5,000 locations. The daily counts were compared against the model in several ways including:

Volume groups
Facility type
Area type

The statistical summaries for these comparisons included the RMSE, % RMSE, and volume-to-count ratios. The analysis is provided in Tables 7-5, 7-6, and 7-7 below. As shown in these tables, the model matches GDOT counts very well at the regional level, particularly for higher volume roadways such as interstates and principal arterials. As expected, the model is less accurate for lower volume roadways. The region-wide % RMSE across all facilities is 38%.

Table 7-5 Highway Validation Statistics by Volume Group

Volume Group	Observations	Observed Counts	Estimated Volumes	RMSE	%RMSE	Acceptable RMSE	Preferred RMSE	Volume/Count Ratio
< 2,500	2,885	3,541,640	4,132,064	1,301	106%	100%	100%	1.17
2,500 - 4,999	2,152	7,925,940	7,504,394	1,997	54%	100%	100%	0.95
5,000 - 9,999	2,695	19,216,110	17,806,297	2,808	39%	45%	35%	0.93
10,000 - 24,999	2,504	38,718,820	33,317,968	4,817	31%	30%	25%	0.86
25,000 - 49,999	405	13,759,200	12,769,696	7,967	23%	25%	15%	0.93
50,000 - 74,999	114	7,197,760	6,662,816	10,916	17%	19%	10%	0.93
75,000 - 99,999	141	12,139,780	11,187,334	13,131	15%	19%	10%	0.92
>= 100,000	121	15,385,950	14,385,764	14,670	12%	19%	10%	0.93
Total	11,017	117,885,200	107,766,333	4,070	38%	45%	38%	0.91

Table 7-6 Highway Validation Statistics by Facility Type

Facility Type	Observations	Observed Counts	Estimated Volumes	RMSE	%RMSE	Volume/Count Ratio
Interstate/Freeway	774	45,472,940	42,427,775	10,334	18%	0.93
Principal Arterial	1,437	21,649,350	20,060,945	4,392	29%	0.93
Minor Arterial	3,825	28,441,560	26,395,847	3,026	41%	0.93
Collector	3,917	10,313,140	8,191,053	1,944	74%	0.79
Ramps	1,064	12,008,210	10,690,713	4,573	41%	0.89
Total	11,017	117,885,200	107,766,333	4,070	38%	0.91

Table 7-7 Highway Validation Statistics by Area Type

Area Type	Observations	Observed Counts	Estimated Volumes	RMSE	%RMSE	Volume/Count Ratio
1- Very high density urban / CBD	465	12,373,270	11,253,591	6,787	26%	0.91
2- High density urban	818	13,127,180	11,479,741	5,313	33%	0.87
3- Medium density urban	1,426	20,107,780	17,578,437	5,367	38%	0.87
4- Low density urban	1,848	22,851,800	20,679,033	4,005	32%	0.90
5- Suburban	3,994	39,693,840	36,294,436	3,806	38%	0.91
6- Exurban	1,209	5,672,200	5,933,307	2,115	45%	1.05
7- Rural	1,257	4,059,130	4,547,788	1,774	55%	1.12
Total	11,017	117,885,200	107,766,333	4,070	38%	0.91

The observed and estimated daily volumes were also graphed using a scatterplot which is provided in Figure 7-3. As illustrated, the correlation coefficient of 0.95 and the trendline indicate the model generally reasonably estimates daily volumes when compared to observed counts.

Figure 7-3. 2019 Daily Observed Counts vs Estimated Counts

Volume comparsions were also made at over 20 screenlines in the model and key freeway and arterial segments. Tables 7-8, 7-9, and 7-10 show the statistics by screenlines, freeway segments, and arterial segments respectively. As shown, the model matches the counts very well.

Table 7-8: Highway Validation Statistics by Screenline

Screenline	Observations	Observed Counts	Estimated Volumes	Difference	%Difference
Alcovy River	18	122,160	101,235	-20,925	-17%
Beltline	76	1,650,500	1,580,190	-70,310	-4%
Central Atlanta - North of I-20	89	1,252,190	1,079,424	-172,766	-14%
Chattahoochee River	48	1,518,640	1,649,883	131,243	9%
Corridor South of Marietta	14	431,320	392,154	-39,166	-9%
East Region N/S	14	171,520	168,721	-2,799	-2%
Flint River	22	160,460	172,650	12,190	8%
GA 400 Corridor in Roswell	6	215,680	223,112	7,432	3%
GA 400 Corridor north of Buckhead	8	207,280	206,786	-494	0%
I-20 Corridor East of Douglasville	12	176,760	186,612	9,852	6%
I-20 Corridor East of I-285	12	289,980	240,715	-49,265	-17%
I-75 Corridor North of Jonesboro	14	360,940	297,337	-63,603	-18%
I-75 South of Locust Grove	6	144,740	127,706	-17,034	-12%
I-85 Corridor North of Norcross	14	507,300	481,547	-25,753	-5%
I-85 Corridor South of Fairburn	8	147,440	135,005	-12,435	-8%
I-985 Corridor South of Gainesville	8	98,500	109,592	11,092	11%
Lake Lanier	10	94,240	99,835	5,595	6%
North Atlanta - East/West	74	976,920	919,591	-57,329	-6%
Outside of I-285	107	2,581,100	2,193,590	-387,510	-15%
South Atlanta - East/West	70	888,380	752,435	-135,945	-15%
SR 20 Corridor West of Cumming	6	45,680	44,751	-929	-2%
West Region N/S	26	203,920	221,526	17,606	9%
Total	662	12,245,650	11,384,397	-861,253	-7%

Figure 7-4. Screenlines

Table 7-9: Highway Validation Statistics by Freeway Segment Types

FWYSEG	Observations	Observed Counts	Estimated Volumes	Difference	%Difference
GA 400: Inside the Loop	4	298,920	319,426	20,506	7%
GA 400: Outside the Loop	14	1,317,580	1,387,250	69,670	5%
I-20: Inside the Loop	35	3,052,980	2,686,706	-366,274	-12%
I-20: Outside the Loop - Eastside	12	920,880	789,356	-131,524	-14%
I-20: Outside the Loop - Westside	16	1,057,780	1,119,446	61,666	6%
I-285 - Eastside	35	3,246,140	2,865,486	-380,654	-12%
I-285 - TopEnd	20	2,498,420	2,463,658	-34,762	-1%
I-285 - Westside	28	2,320,340	2,134,353	-185,987	-8%
I-75/I-85: Inside the Loop	28	4,345,660	4,091,440	-254,220	-6%
I-75: Inside the Loop - Northside	23	2,050,110	1,935,071	-115,039	-6%
I-75: Inside the Loop - Southside	10	741,200	649,750	-91,450	-12%
I-75: Outside the Loop - Northside	18	1,998,100	1,884,064	-114,036	-6%
I-75: Outside the Loop - Southside	14	1,252,140	973,388	-278,752	-22%
I-85: Inside the Loop - Northside	19	1,783,730	1,824,442	40,712	2%
I-85: Inside the Loop - Southside	16	1,137,060	1,016,268	-120,792	-11%
I-85: Outside the Loop - Northside	18	2,219,500	2,284,175	64,675	3%
I-85: Outside the Loop - Southside	6	474,880	403,804	-71,076	-15%
Total	316	30,715,420	28,828,083	-1,887,337	-6%

Figure 7-5. Freeway Segments

Table 7-10: Highway Validation Statistics by Arterial Segment Types

ARTSEG	Observations	Observed Counts	Estimated Volumes	Difference	%Difference
CH James Pkwy/Thornton Rd	16	322,240	287,345	-34,895	-11%
Cobb Dr/Atlanta Rd	18	311,880	212,908	-98,972	-32%
Covington Hwy	22	273,700	197,400	-76,300	-28%
Fairburn Rd/Campbellton Rd	28	338,720	291,663	-47,057	-14%
Five Forks Trickum Rd	14	106,040	113,103	7,063	7%
Flat Shoals Pkwy/Browns Mill Rd	14	151,620	144,542	-7,078	-5%
Fulton Industrial Blvd	14	217,000	228,821	11,821	5%
GA 138: I-85 to I-75	30	390,160	295,405	-94,755	-24%
GA 78	26	360,820	383,771	22,951	6%
Jonesboro Rd	24	270,300	168,744	-101,556	-38%
Lavista Rd	20	277,180	183,170	-94,010	-34%
Metropolitan Pkwy/Northside Dr	26	328,460	299,586	-28,874	-9%
Panola Rd	24	198,660	198,515	-145	0%
Pleasant Hill Rd	10	191,720	237,087	45,367	24%
Ponce De Leon Ave	26	256,060	293,615	37,555	15%
Rockbridge Rd	18	160,780	181,384	20,604	13%
Stockbridge Hwy/Henry Blvd	28	367,540	343,252	-24,288	-7%
Thornton Rd/Camp Creek Pkwy	16	327,800	211,859	-115,941	-35%
Total	374	4,850,680	4,272,170	-578,510	-12%

Figure 7-6. Arterial Segments

In addition to comparing the model against all vehicle counts, GDOT’s vehicle classification percentages were used to evaluate the performance of the model against truck counts. These counts were compared against the model estimates by facility type, area type, and inside/outside I-285. The validation summaries are provided below in Tables 7-11, 7-12, and 7-13.

Generally, the model’s estimation of total truck traffic, medium trucks, and heavy trucks matches the observed data as evidenced by the overall volume-to-count ratios. When viewing by area type, the model appears to overestimate medium and heavy trucks in the CBD; however, there are relatively few count locations which included the two truck types in this area type. As shown in Table 2-10, the model appears to be estimating the heavy truck traffic inside I-285 reasonably well which is important given that heavy trucks without a destination inside I-285 are prohibited from using I-75/I-85 to travel through the city of Atlanta.

Table 7-11 Truck Validation Summaries by Facility Type

	All Truck Count Locations		Medium and Heavy Truck Count Locations
Facility Type	Observations	Volume/Count Ratio	Observations	Medium Truck Volume/Count Ratio	Heavy Truck Volume/Count Ratio
Interstate/Freeway	192	1.24	192	1.78	0.94
Principal Arterial	1,298	0.98	1,298	0.91	1.15
Minor Arterial	1,476	0.85	1,476	0.75	1.17
Collector	1,338	0.69	1,338	0.55	1.40
Ramps	62	1.06	62	0.96	1.25
Total	4,366	1.01	4,366	0.99	1.06

Table 7-12 Truck Validation Summaries by Area Type

	All Truck Count Locations		Medium and Heavy Truck Count Locations
Area Type	Observations	Volume/Count Ratio	Observations	Medium Truck Volume/Count Ratio	Heavy Truck Volume/Count Ratio
1- Very high density urban / CBD	111	1.38	111	1.49	1.23
2- High density urban	295	1.05	295	1.02	1.09
3- Medium density urban	528	1.09	528	1.10	1.07
4- Low density urban	625	1.05	625	1.03	1.07
5- Suburban	1,596	0.95	1,596	0.90	1.03
6- Exurban	596	0.90	596	0.85	1.00
7- Rural	615	1.00	615	0.99	1.00
Total	4,366	1.01	4,366	0.99	1.06

Table 7-13 Truck Validation Summaries Inside/Outside I-285

	All Truck Count Locations		Medium and Heavy Truck Count Locations
Location	Observations	Volume/Count Ratio	Observations	Medium Truck Volume/Count Ratio	Heavy Truck Volume/Count Ratio
Outside I-285	3,747	1.01	3,747	0.98	1.06
Inside I-285	619	1.04	619	1.04	1.04
Total	4,366	1.01	4,366	0.99	1.06

In addition to the above summaries, a scatterplot as provided in Figure 7-7 was generated to illustrate graphically the comparison between the estimated versus observed truck volumes.

Figure 7-7. Estimated vs Observed All Trucks

Two additional data sources were used as another means to validate the truck model related to origins and destinations as listed below:

Freight Analysis Framework Version 5 (FAF5): Experimental County-Level Estimates
Regional Integrated Transportation Information System (RITIS): Trip Analytics

The FAF5 database provides estimates of the commodity flow tonnage between counties throughout the United States. The Georgia database was used for the analysis, and the counties within ARC’s model boundary were extracted for the comparisons. It is important to note the ARC’s model estimates truck trips, not commodity flows meaning it is not possible to directly compare the model versus the FAF5 commodity flow data. Rather than a direct comparison, the relative shares of each origin and destination county were tabulated from FAF5 and the model estimated truck trips and are provided in Figure 7-8 and Figure 7-9.

Figure 7-8. FAF5 versus ARC Based on Origin County

Figure 7-9. FAF5 versus ARC Based on Destination County

The RITIS Trip Analytics dataset for Georgia uses INRIX and allows for extracting the data for both medium and heavy trucks. Both vehicle classifications were extracted and compared to the model estimated medium and heavy trucks in the same fashion as previously described for the FAF5 comparison (relative shares by origin and destination county). The comparisons for heavy trucks are provided in Figure 7-10 and Figure 7-11, while the comparisons for medium trucks are provided in Figure 7-12 and Figure 7-13.

Generally, the model matches the Trip Analytics data reasonably well for the heavy truck origins and destinations by county (Figure 7-14 and Figure 7-15). For both origins and destinations, the model underestimated the shares for Bartow, Clayton, and Henry counties while overestimating Cobb and Gwinnett counties.

As shown in the medium truck comparisons (Figure 7-16 and Figure 7-17), the model matches the origin and destination shares quite well as compared to Trip Analytics. The model slightly overestimated Clayton, Cobb, Fulton, and Gwinnett counties while slightly underestimating DeKalb and Henry counties.

Figure 7-10. Heavy Trucks RITIS Trip Analytics versus ARC Based on Origin County

Figure 7-11. Heavy Trucks RITIS Trip Analytics versus ARC Based on Destination County

Figure 7-12. Medium Trucks RITIS Trip Analytics versus ARC Based on Origin County

Figure 7-13. Medium Trucks RITIS Trip Analytics versus ARC Based on Destination County

Through the ARC/GDOT Eastern Transportation Coalition partnership, access to the Altitude by Geotab platform was accessible for extracting additional origin / destination data for medium and heavy trucks. The Geotab data was processed and compared in the same manner as the FAF5 and RITIS Trip Analytics data for both medium and heavy trucks. The comparisons for heavy trucks are provided in Figure 7-14 and Figure 7-15, while the comparisons for medium trucks are provided in Figure 7-16 and Figure 7-17.

The model matches the Geotab heavy truck origin and destination shares reasonably well at the county-level as indicated in Figure 7-14 and Figure 7-15. As compared to Geotab, the model predicted a higher share of origins and destinations for Clayton and Cobb counties while predicting a lower share for Fulton, Hall, and Henry counties.

For medium trucks, the model replicated the Geotab origin and destination shares quite well at the county-level as shown in Figure 7-16 and Figure 7-17. As compared to Geotab, the model resulted in slightly higher origin and destination shares for Clayton, Cobb, and DeKalb counties while predicting slightly lower shares for Fulton County.

When viewing the model results relative to GDOT truck counts along with the FAF5, RITIS Trip Analytics, and Geotab origin/destination data, it can be concluded that the truck model is reasonably validated for 2020 conditions.

Figure 7-14. Heavy Trucks Geotab versus ARC Based on Origin Countyy

Figure 7-15. Heavy Trucks Geotab versus ARC Based on Destination County

Figure 7-16. Medium Trucks Geotab versus ARC Based on Origin County

Figure 7-17. Medium Trucks Geotab versus ARC Based on Destination County

Section 7.1.5 Speeds

The 2015 base year model update included calibrating the volume delay functions (VDF) based on the observed traffic counts and observed speeds from the National Performance Management Research Data Set (NPMRDS). As part of the 2020 base year model update, speed data representing average weekdays (Tuesday, Wednesday, and Thursday) from October 2019 were averaged to reflect the ARC time-periods and compared with the model estimates. The results are provided in the scatterplots below in Figure 7-18 through Figure 7-22.

As expected, the model matches the speeds in the off-peak periods well, with the R squared values ranging from 0.86 to 0.89 in the early AM, midday, and evening periods. The AM and PM peak period speeds do not match as closely with the observed speeds which is primarily a function of the static user equilibrium assignment procedures which cannot account for operational characteristics such as signal timing, merge/diverge/weaving, and queue formations that exist in real world conditions. However, in the context of a regional planning model, ARC’s assignment matches the AM and PM peak speeds reasonably well as evidenced by the plots.

When viewing the updated base year 2020 model’s performance relative to observed traffic counts and observed speeds, it was concluded that updates to the previously calibrated volume delay functions was not necessary.

Figure 7-18. Early AM Observed vs. Estimated Speeds

Figure 7-19. AM Peak Observed vs. Estimated Speeds

Figure 7-20. Midday Observed vs. Estimated Speeds

Figure 7-21. PM Peak Observed vs. Estimated Speeds

Figure 7-22. Evening Observed vs. Estimated Speeds

Section 7.2 Transit Assignment Validation

The ARC model assigns transit trips to the network using the Public Transport (PT) module in Cube. The trips are assigned to the single best path using PT’s algorithms for each time period, mode of access, and premium only vs. premium/non-premium transit modes. The assignment results are then aggregated to daily totals for comparison against data provided by the regional transit operators. The overall summary of total boardings by operator is provided in Table 7-14. As shown, the model matches total regional boardings well. The model is within 2% of observed ridership for MARTA rail, 14% for MARTA buses, and 3% for the University shuttles. These account for approximately 95% of total regional transit ridership.

Table 7-14 Regional Transit Boardings

Operator/Mode	Observed	Modeled	Difference	%Difference
ATL XPRESS	7,973	6,495	-1,478	-19%
CATS Local Bus	63	144	81	129%
CobbLinc Express Bus	658	745	87	13%
CobbLinc Local Bus	9,095	12,984	3,889	43%
GCT Express Bus	1,738	1,801	63	4%
GCT Local Bus	4,105	4,547	442	11%
HAT (Gainesville Express) Local Bus	552	573	21	4%
MARTA Bus	160,340	137,219	-23,121	-14%
MARTA Rail	200,577	197,211	-3,366	-2%
MARTA Streetcar	740	2,196	1,456	197%
Connect Douglas Local Bus	68	362	294	432%
Free shuttle (University or activity center)	42,510	43,967	1,457	3%
HCT Local Bus	4	10	6	150%
Total	428,423	408,254	-20,169	-5%

Section 7.2.1 MARTA Rail

MARTA rail boardings were further summarized at the station level, accounting for both entries and transfers between lines. Table 7-15 provides a comparison of boardings by station. The model estimates align closely with observed boardings at Five Points and Airport stations, the two highest ridership stations in the rail network. Additionally, the model shows strong alignment with observed boardings at other high-ridership stations.

A bar graph of the station boardings is provided in Figure 7-23. Since the total at Five Points is much higher than the other stations, it was removed from the graph to provide a closer look at the remaining stations.

Table 7-15 MARTA Rail Station Entries and Boardings

STATION	Observed Boardings	Estimated Boardings	Difference	%Difference
AIRPORT	10,016	9,894	-122	-1%
ARTS CENTER	7,394	8,180	786	11%
ASHBY	2,022	2,393	371	18%
AVONDALE	2,962	2,055	-907	-31%
BANKHEAD	1,065	826	-239	-22%
BROOKHAVEN-OGLETHORPE	2,151	1,597	-554	-26%
BUCKHEAD	3,832	3,863	31	1%
CHAMBLEE	3,717	3,138	-579	-16%
CIVIC CENTER	2,638	4,270	1,632	62%
COLLEGE PARK	8,558	7,049	-1,509	-18%
DECATUR	3,080	2,091	-989	-32%
DOME-GWCC-PHILIPS ARENA-CNN	2,198	1,738	-460	-21%
DORAVILLE	4,841	4,007	-834	-17%
DUNWOODY	3,849	4,711	862	22%
EAST LAKE	1,501	764	-737	-49%
EAST POINT	6,310	7,102	792	13%
EDGEWOOD-CANDLER PARK	1,320	877	-443	-34%
FIVE POINTS	49,828	48,939	-889	-2%
GARNETT	1,480	2,046	566	38%
GEORGIA STATE	4,324	4,292	-32	-1%
HAMILTON E HOLMES	5,760	5,984	224	4%
INDIAN CREEK	4,452	3,190	-1,262	-28%
INMAN PARK-REYNOLDSTOWN	1,918	2,495	577	30%
KENSINGTON	5,053	4,096	-957	-19%
KING MEMORIAL	1,523	1,930	407	27%
LAKEWOOD-FT MCPHERSON	2,270	2,689	419	18%
LENOX	2,463	2,539	76	3%
LINDBERGH CENTER	9,412	11,404	1,992	21%
MEDICAL CENTER	1,693	2,849	1,156	68%
MIDTOWN	6,399	7,574	1,175	18%
NORTH AVENUE	5,641	5,612	-29	-1%
NORTH SPRINGS	6,545	5,698	-847	-13%
OAKLAND CITY	3,525	3,743	218	6%
PEACHTREE CENTER	9,856	7,055	-2,801	-28%
SANDY SPRINGS	3,007	2,100	-907	-30%
VINE CITY	839	988	149	18%
WEST END	5,827	6,578	751	13%
WEST LAKE	1,316	853	-463	-35%
Total	200,585	197,209	-3,376	-2%

Figure 3-1. 2019 Observed vs Estimated MARTA Rail Boardings

Figure 7-23. 2019 Observed vs Estimated MARTA Rail Boardings

Section 7.2.2 Buses

As previously mentioned, the model matches overall regional bus totals well, but a review of the individual bus routes was also performed by preparing scatterplot comparisons. Three scatterplots were developed which include all buses except shuttles (Figure 7-24), one for just MARTA buses (Figure 7-25), and one for the non-MARTA buses (Figure 7-26).

As shown in the figures, the model slightly under-estimates boardings on MARTA buses, but well within the limits for a regional model.