The Travel Demand 

Model 

Prepared for

Georgia Department of 

Transportation

Travel Demand 

Model for the Rome 

MPO 

Prepared for

Georgia Department of 

Transportation

By 

 

September 2011 

Travel Demand 

Rome 

Georgia Department of 
TABLE OF CONTENTS 

List of Tables ....................................................................................................................................... i 

List of Figures .................................................................................................................................... ii 

1.0 Introduction .................................................................................................................................1 

2.0 Model Update ..............................................................................................................................2

2.1 Highway Network Coding .............................................................................................................................. 3

2.2 Trip Generation ............................................................................................................................................. 8

2.2.1 Trip Purposes ........................................................................................................................................................ 8

2.2.2 Socioeconomic Data ........................................................................................................................................... 10

2.2.3 Household Stratification Model ........................................................................................................................ 11

2.2.4 Trip Production .................................................................................................................................................. 15

2.2.5 Trip Attraction Submodel .................................................................................................................................. 15

2.2.6 External-External Trips ..................................................................................................................................... 17

2.2.7 Special Trip Generators..................................................................................................................................... 17

2.2.8 Balancing Productions and Attractions ............................................................................................................ 18

2.3 Trip Distribution .......................................................................................................................................... 18

2.4 Mode Split .................................................................................................................................................... 19

2.5 Traffic Assignment ...................................................................................................................................... 21

2.5.1 Model Calibration ............................................................................................................................................... 22 

2.5.2 Delta Matrix Process ........................................................................................................................................... 31

Appendix A: Travel Demand Model Validation Sample Report ................................................33 
i Model Development Documentation 

List of Tables 

Table 2.1-1 Facility and Area Types...........................................................................................5 

Table 2.1-2 Hourly Capacities ....................................................................................................5 

Table 2.1-3 Free Flow Speeds ....................................................................................................6 

Table 2.1-4 Input Network Link Attributes ................................................................................7 

Table 2.1-5 Input Network Node Attributes ...............................................................................7 

Table 2.2.3-1 2000 Household Size CTPP Distribution ..............................................................12 

Table 2.2.3-2 2000 CTPP Household Income Distributions .......................................................13 

Table 2.2.3-3 Household Size/Income//Auto Ownership Distribution ........................................14 

Table 2.2.4-1 Trip Generation Trip Rates ....................................................................................15 

Table 2.2.5-1 Proportion of External-Internal Trips by External Station ....................................16 

Table 2.3-1 Terminal Time Criteria ..........................................................................................19 

Table 2.3-2 Average Trip Travel Times ...................................................................................19 

Table 2.4-1 Vehicle Occupancy Rates ......................................................................................20 

Table 2.5-1 Output Network Attributes ....................................................................................21 

Table 2.5.1-1 Summary of the Screenlines ..................................................................................25 

Table 2.5.1-2 Screenline Results .................................................................................................25 

Table 2.5.1-3 Model VMT Statistics ...........................................................................................30 
ii Model Development Documentation 

List of Figures

Figure 1.1-1 Travel Demand Models ...........................................................................................2 

Figure 2.1-1 FRUTS 2009 Highway Network .............................................................................4 

Figure 2.2.2-1 Traffic Analysis Zones ..........................................................................................11 

Figure 2.5.1-1 Screenline Locations .............................................................................................24 

Figure 2.5.1-2 Travel Demand Model Scatter Plot .......................................................................29 

Figure 2.5.1-3 Travel Demand Model Maximum Desired Deviation Chart.................................30

Figure 2.5.1-4 Travel Demand Model Maximum Desired Deviation Map ..................................31

Figure 2.5.2-1 Delta Matrix Process .............................................................................................32 
Rome Model 2009 Base Year Update 

1 Model Development Documentation 

1.0 Introduction 

Transportation system studies are done periodically by the Georgia Department of 

Transportation, Floyd-Rome Urban Transportation Study (FRUTS) and Rome Transit to 

determine what types of transportation improvements or investments would best serve the public. 

Georgia DOT and FRUTS are primarily responsible for technical studies pertaining to the 

roadway system while the Rome Transit conducts studies of transit service. 

The travel demand model is used to evaluate the performance of the roadway system in and 

around Rome by the Georgia Department of Transportation and FRUTS. The FRUTS model is a 

traditional urban area analysis tool that is used to identify where major improvements should be 

made to its principal thoroughfare system. Since there is usually more than one strategy 

proposed to address future congestion and safety concerns, the model is frequently used to study 

which combination of improvements provides the most end-user benefits. The output from the 

travel demand model is used to estimate mobile source emissions and perform the conformity 

analysis. 

There are two key inputs to the travel demand modeling process, socio-economic data and the 

transportation system. Socio-economic data such as population, household and employment by 

type represents land use. Future year projections of socioeconomic data were based on a 2009 

inventory of existing land uses including vacant land, as well as region wide forecasts of 

population, households and employment. Future year forecasts also considered planned major 

transportation improvements. Allocation of expected growth is then done using known 

development patterns and proposals as the basis, taking into consideration planned infrastructure 

improvements (new highways, sewer extensions, etc.). It is in this area of travel model 

development that land use and community planning are connected to the transportation planning 

process. Figure 1.1-1 shows the interaction between travel demand models and transportation 

system characteristics and population and employment characteristics 

The other key element of the travel model is referred to as the highway network. The highway 

network is a computer file containing links and nodes that represent roadway segments and 

intersections. Each link record in the file contains information describing these items: free-flow 

travel speed, distance, number of lanes, area type (density of population and employment); 

facility type (similar to functional classification) and capacity. Node records simply contain 

positional, two dimensional x and y coordinates to enable the network file to be displayed 

pictorially. 
Rome Model 2009 Base Year Update 

2 Model Development Documentation 

Figure 1.1-1 

Travel Demand Models 

Georgia DOT is responsible for the development, maintenance and application of the FRUTS 

travel model. GDOT has updated various components of the FRUTS travel model to ensure that 

the model is state-of-the-practice and includes technical procedures that would be needed in 

developing the 2040 LRTP. A detailed description of the FRUTS’ travel model is presented in 

Section 2. It includes explanations for how trips are estimated, how person trips are converted to 

vehicle trips, what attributes comprise the highway network and how trips are assigned onto the 

highway network. Each of the modeling steps involved in developing an urban travel model is 

described. These steps are as follows: Trip Generation; Trip Distribution; Mode Split; and 

Traffic Assignment. 

2.0 Model Update 

Several significant changes were made to the Rome area travel model. These changes are based 

on the original 2006 model and listed below. 

• Updated the model to reflect 2009 as the base year

• Updated the HPMS functional classification code to 2000 HPMS in the network 

• Updated screenline locations 

• Updated and validated the base year highway network 

Travel 

Demand 

Models 

Transportation 

System 

Characteristics 

Population and 

Employment 

Characteristics 

Forecasting 

Transportation 

System 

Performance 

Forecasting How 

Changes Might 

Affect Future 

Performance 

Source: Introduction to Travel Demand Forecasting 
Rome Model 2009 Base Year Update 

3 Model Development Documentation 

• Updated trip generation model 

• Updated trip distribution model 

• Updated the traffic assignment procedure (24-Hour daily) 

• Updated external trip model 

• Incorporated feedback loop assignment 

• Added the delta matrix post-process procedure to refine the final traffic assignment 

The general structure of the Rome model is standard, in comparison with other travel demand 

models used in urban areas that are similar in size to Rome. Descriptions of each principal model 

element are presented in the subsequent parts of this section. 

2.1 Highway Network Coding 

The base year model network was updated to reflect 2009 existing conditions. The 20069

highway network was closely examined and revised to reflect base year conditions for 2009. 

Projects that had been built in-between the current base year and previous one were included. 

The purpose of the highway network is to provide accurate routing paths based on the minimum 

time to travel from one traffic analysis zone to another. In effect, the highway network file is a 

simulation tool replicating the thoroughfare system in Rome MPO area. A graphical 

representation of the model highway network by facility type is presented in Figure 2.1-1. 
Rome Model 2009 Base Year Update 

4 Model Development Documentation 

Figure 2.1-1 

FRUTS 2009 Highway Network 
Rome Model 2009 Base Year Update 

5 Model Development Documentation 

Facility Type and Area Type. Individually and in combination these two link attributes 

provide the framework for organizing the network into sub-groups so that free-flow speeds and 

capacities can be assigned. In combination with the distance and number of lanes, these 

attributes constitute the base layer of highway network data needed to update and apply the 

travel model. The facility type and area type definitions used in the FRUTS highway network 

and modeling process are shown in the table below. 

Table 2.1-1 

Facility and Area Types 

Code Facility Type Code Area Type 

1 Interstate 1 High Density Urban 

2 Freeway 2 High Density Urban Commercial 

3 Expressway 3 Urban Residential 

4 Parkway 4 Suburban Commercial 

6 Freeway to Freeway Ramp 5 Suburban Residential 

7 Freeway Entrance Ramp 6 Exurban 

8 Freeway Exit Ramp 7 Rural 

11 Principal Arterial – Class I 

12 Principal Arterial – Class II 

13 Minor Arterial – Class I 

14 Minor Arterial – Class II 

15 One Way Arterial 

21 Major Collector 

22 Minor Collector 

23 One Way Collector 

30 Local Road 

32 Centroid Connector 

Capacity. Link capacities for the model network are obtained from a lookup table of per-lane 

hourly capacities based on facility type and area type. The final link capacity is calculated by 

multiplying the hourly capacity per lane by the number of lanes. The following table displays 

the hourly capacities per lane: 

Table 2.1-2 

Hourly Capacities 

Facility Type 

Area Type 

1 2 3 4 5 6 7 

Interstate 1900 1950 2000 2050 2100 2060 2020

Freeway 1600 1660 1730 1790 1850 1820 1780

Expressway 1300 1380 1450 1530 1600 1570 1540

Parkway 1170 1240 1310 1370 1440 1410 1380

Freeway to Freeway Ramp 1400 1530 1650 1780 1900 1860 1820

Freeway Entrance Ramp 900 1030 1150 1280 1400 1370 1340

Freeway Exit Ramp 800 810 810 820 820 810 790

Principal Arterial – Class I 1000 1030 1050 1080 1100 1080 1060

Principal Arterial – Class II 900 900 900 900 900 880 860
Rome Model 2009 Base Year Update 

6 Model Development Documentation 

Facility Type 

Area Type 

1 2 3 4 5 6 7 

Minor Arterial – Class I 800 810 810 820 820 810 790

Minor Arterial – Class II 630 630 640 640 640 630 610

One Way Arterial 760 760 770 770 770 760 740

Major Collector 520 530 540 550 560 550 540

Minor Collector 380 390 390 400 400 390 380

One Way Collector 460 470 470 480 480 470 460

Local Road 340 350 360 370 380 370 360

Centroid Connector 0 0 0 0 0 0 0

Speeds. Link speeds in the model network are derived from a speed lookup table based on 

facility type and area type. Assumed free-flow speed are approximately 5 mph faster than typical 

speed limits for the various roadway classes and area types, taking into consideration control for 

delay (i.e. traffic signals) if applicable. Peak and off-peak free-flow speeds were evaluated using 

observed speeds obtained from a travel time study conducted in the Rome area. Based on the 

initial study of the speeds, a revised speed table was developed. An analysis of the Rome data 

determined that Rome’s characteristics and data results are appropriate for use in all GDOT 

MPO models since the travel dynamics for these urban areas are similar. Final free-flow 

calibrated speeds are shown in the matrix below. 

Table 2.1-3 

Free Flow Speeds 

Facility Type 

Area Type 

1 2 3 4 5 6 7 

Interstate 55 60 60 60 60 70 70 

Freeway 50 55 55 55 55 60 60 

Expressway 50 50 50 50 55 55 55 

Parkway 45 50 50 50 50 55 55 

Freeway to Freeway Ramp 55 55 55 55 55 55 55 

Freeway Entrance Ramp 45 50 50 50 50 55 55 

Freeway Exit Ramp 22 23 30 31 34 40 48 

Principal Arterial – Class I 25 28 33 34 37 47 52 

Principal Arterial – Class II 23 26 31 32 35 45 49 

Minor Arterial – Class I 22 23 30 31 34 40 47 

Minor Arterial – Class II 21 22 27 30 32 38 45 

One Way Arterial 23 26 30 32 35 42 48 

Major Collector 17 18 21 27 29 34 42 

Minor Collector 14 15 18 24 26 30 40 

One Way Collector 17 18 21 27 29 34 42 

Local Road 14 14 17 18 22 28 35 

Centroid Connector 14 14 17 18 22 28 35 

Network Link Attributes. All input network link attributes are included in the following table. 

While most of them are not directly involved in the model process, they provide assistance in 
Rome Model 2009 Base Year Update 

7 Model Development Documentation 

link attributes summary for post model result processing and for model calibration and 

validation. 

Table 2.1-4 

Input Network Link Attributes 

Attribute Name Description 

Distance Roadway Link Length in miles 

Lanes Number of Lanes 

Ftype Facility Type 

*UAB Urbanized Area Code, 1990 Census Geography 

Screenline Screenline ID 

Roadname Roadway Name 

Cstation Traffic Count Station Number 

Fclass HPMS Functional Classification Code, 2000 Census Geography 

Lanesam Number of Lanes in AM Peak Direction 

Lanespm Number of Lanes in PM Peak Direction 

County County FIPS Code 

Twoway Oneway/Twoway Identification Code 

Count06 2006 AADT - Two Way (from GDOT QA/QC Database) 

Aadt09 2009 AADT - Two Way 

GDOT_PI GDOT Project Identification Number 

Local_PI Local Project Identification Number 

Open_date Model Year Open to Traffic – Construction Completed 

* Optional attribute 

Network Nodes Attributes. The network node contains four attributes designated to identify 

the accessibility of a node. Only the centroid nodes of the network are attached with these 

attributes which use non-zero to indicate the availability of transit. The level of the accessibility 

is shown in the following table. 

Table 2.1-5 

Input Network Node Attributes 

Attribute Name Description 

Transit 

1 - Centroid within 0.25 miles of transit access 

2 - Centroid within 0.50 miles of transit access 

3 - Centroid within 1.00 miles of transit access 
Rome Model 2009 Base Year Update 

8 Model Development Documentation 

2.2 Trip Generation 

Trip generation is the first step in the traditional four-step modeling process. It estimates the 

number of trips that will begin and end in each individual traffic analysis zone (TAZ). These 

are referred to as “trip ends”. Trip ends generated by households are referred to as productions. 

Trip ends calculated from employment or school enrollment figures are referred to as attractions. 

This process is accomplished by establishing relationships between trips and socioeconomic 

variables. The process estimates the number of trip ends, or productions and attractions, for each 

traffic zone by various trip purposes. Trip generation does not determine the origin and 

destination of each trip, only the total trips generated by each TAZ's socioeconomic 

characteristics. 

In 1997, GDOT contracted with a consulting firm to assist in developing a new standardized trip 

generation process for the state’s urbanized areas outside of Atlanta. The Trip Generation 

Update Project included a household travel survey and external travel survey in the Augusta 

metropolitan area. Household travel behavior by household size and income group is 

homogeneous from one urban area to another if transportation choices and land-use patterns are 

similar. The Augusta survey information was used to formulate and recommend a trip 

generation process that is considered transferable to the State’s other urbanized areas. 

The new trip generation process includes trip production and trip attraction sub-models. For all 

trips that have origins and destinations inside the FRUTS region, excluding trucks, the trip 

production sub-model applies trip rates through a cross-classification of household size 

(1,2,3,4+) and automobiles available (0,1,2,3+). Aggregate household data for each traffic 

analysis zone is disaggregated into sixteen cross-classified cells using a household stratification 

model. The household stratification model is also a product of the Trip Generation Update 

Project. This model breaks out the total number of FRUTS households into cross-classification 

cells using zonal income, Rome area specific data from the Census Transportation Planning 

Package (CTPP), and data from the Augusta household survey. The trip production sub-model 

applies regression equations for other trip purposes. The trip attraction sub-model applies 

regression equations for all trip purposes. 

Typically, there are three types of trips that travel demand models include: (1) Internal-Internal 

(I-I) trips whose origin and destination are inside the study area boundary; (2) Internal-External 

(I-E) trips that have exactly one trip end inside the study area; and (3) External-External (E-E) 

trips that have both trip ends outside of the study area. I-I trips follow the production and 

attraction logic of trip formulation. They are commonly grouped into trip purposes so their 

characteristics can be reproduced by the chain of sub-models in the four-step process. I-E and EE trips are developed separately using a different methodology that is heavily dependent on 

traffic counts observed on the principal roads leading into and out of the region. 

2.2.1 Trip Purposes 

Seven trip purposes were included in the trip generation process. These purposes are 

summarized below: 
Rome Model 2009 Base Year Update 

9 Model Development Documentation 

1. Home Based Work (HBW): All travel made for the purpose of work and which

begins or ends at the traveler’s home. 

2. Home Based Other (HBO): Any trip made with one end at the home except those

for the purpose of work or shopping. 

3. Home Based Shopping (HBS): Trips made for the purpose of shopping and which 

begins or ends at the traveler’s home. 

4. Non Home Based (NHB): Any trip that neither begins nor ends at home. 

5. Internal-Internal Truck (IIT): Internal trips made by commercial vehicles. 

6. Internal-External Passenger Car (IEPC): Internal trips beginning or ending 

outside the modeled area, excluding trucks. 

7. Internal-External Truck (IET): Internal truck trips beginning or ending outside the 

modeled area. 
Rome Model 2009 Base Year Update 

10 Technical Report 

2.2.2 Socioeconomic Data 

The FRUTS provided 2009 Base Year socioeconomic data for the model. For each of the traffic 

analysis zones (TAZ’s) in the model area, the following socioeconomic variables were collected 

for use in the trip generation model: 

Population: The total number of individuals that are residing in each traffic zone. 

Retail Employment: Number of employees working for retail businesses in a given 

traffic analysis zone where the business is located. 

Industrial Employment: Number of employees working for industrial based businesses 

in a given traffic analysis zone where the business is located 

Service Employment: Number of employees working for service based businesses in a 

given traffic analysis zone where the business is located. 

Wholesale and Warehouse Employment: Number of employees working for Wholesale 

and wharehouse based businesses in a given traffic analysis zone where the business is 

located. 

Total Employment: The total number of employed persons in those traffic zones with 

employment. 

Income: Average household income in TAZ in 2000 dollars (per 2000 Census). 

Total Households: Total number of occupied households in a given traffic analysis zone. 

School Enrollment: The total number of enrolled students in zones with educational 

facilities. 

Acres: Area of TAZ in acres. 

An illustrative picture of the zone boundary map is presented in Figure 2.2.2-1. There are 233 

internal zones in the FRUTS model with 26 additional zones called external stations. The 

external station zones represent the key facilities where travel in and out of the FRUTS region 

occurs such as US-27 and US-411. 
Rome Model 2009 Base Year Update 

11 Technical Report 

Figure 2.2.2-1 

Map of Traffic Analysis Zones 

2.2.3 Household Stratification Model 

The household stratification model subdivides the total number of households by TAZ into 

sixteen household strata defined by household size and the number of automobiles available. 

Stratification is done using zonal income, Rome area specific data from the Census 

Transportation Planning Package (CTPP), and data from the Augusta household survey. The 
Rome Model 2009 Base Year Update 

12 Technical Report 

model distributes the total households in a TAZ to each cross-classification cell by calculating a 

relative1probability that a household will be a particular size with a particular number of 

automobiles. The relative probability is calculated with the following equation: 

 P(i,j) = S * I * CF, where 

P(i,j) = Relative probability that a household will be size i and own j autos 

S = Household size factor from CTPP lookup table 

I = Income factor from CTPP lookup table 

CF = Composite household factor from Augusta household survey lookup table. 

An estimate of the number of households in a particular cross-classification cell is then 

calculated by multiplying the total number of households in the TAZ by the corresponding 

relative probability. The final number of households in each cross-classification cell is calculated 

by applying an adjustment factor to each calculated value. The adjustment factor is applied to 

insure that the sum of the resulting disaggregated households equals the original aggregate 

number of households. This process is represented mathematically with the following equations: 

HHij(est.) = HH * P(i,j), where 

HHij(est.) = Estimated number of households of size i that own j autos 

HH = Total number of households in the TAZ 

HHij = HHij(est.) * F, where 

HHij = Final number of households2

 of size i that own j autos 

F = HH / ΣHHij(est.), control total adjustment factor. 

The three lookup tables used in the household stratification model are shown on the following 

pages. 

Table 2.2.3-1 

2000 Household Size CTPP Distribution 

Computed 

Persons/HH HOUSEHOLD SIZES 

Ranges 1 2 3 4+

0.0 to 1.0 1.0000 0.0000 0.0000 0.0000

1.0 to 1.2 0.7812 0.2056 0.0133 0.0000

1.2 to 1.4 0.6898 0.2568 0.0331 0.0203

1.4 to 1.6 0.5752 0.3128 0.0687 0.0433

1.6 to 1.8 0.4839 0.3511 0.1021 0.0630

1.8 to 2.0 0.4141 0.3537 0.1279 0.1043

2.0 to 2.2 0.3487 0.3563 0.1464 0.1486

 

1 The term relative probability is used because the value is not technically a statistical probability. 

2 Not rounded to an integer value to eliminate problems with round off errors. 
Rome Model 2009 Base Year Update 

13 Technical Report 

Computed 

Persons/HH HOUSEHOLD SIZES 

Ranges 1 2 3 4+

2.2 to 2.4 0.2872 0.3471 0.1689 0.1968

2.4 to 2.6 0.2389 0.3274 0.1879 0.2458

2.6 to 2.8 0.1939 0.3140 0.1985 0.2935

2.8 to 3.0 0.1553 0.2947 0.2076 0.3424

3.0 to 3.2 0.1253 0.2749 0.2074 0.3924

3.2 to 3.4 0.1152 0.2489 0.1996 0.4363

3.6 to 3.8 0.1119 0.2116 0.1932 0.4832

3.8 to 4.0 0.1038 0.2042 0.1688 0.5232

4.0 to 4.2 0.1028 0.2032 0.1608 0.5332

Table 2.2.3-2 

2000 CTPP Household Income Distributions 

TAZ-Level Median HH 

Income 

Income Group 1 Income Group 2 Income Group 3 Income Group 4 

< $20,000 $20,000 - $39,999 $40,000 - $59,999 > $60,000 

$0 $2,499 0.8835 0.1165 0.0000 0.0000

$2,500 $4,999 0.8549 0.1168 0.0232 0.0050

$5,000 $7,499 0.8300 0.1318 0.0300 0.0081

$7,500 $9,999 0.7585 0.1468 0.0427 0.0521

$10,000 $12,499 0.6933 0.1826 0.0718 0.0523

$12,500 $14,999 0.6311 0.2131 0.0802 0.0756

$15,000 $17,499 0.5771 0.2465 0.0894 0.0870

$17,500 $19,999 0.5031 0.2938 0.1046 0.0985

$20,000 $22,499 0.4326 0.3321 0.1257 0.1096

$22,500 $24,999 0.3927 0.3387 0.1449 0.1236

$25,000 $27,499 0.3316 0.3581 0.1702 0.1401

$27,500 $29,999 0.3071 0.3488 0.1824 0.1617

$30,000 $32,499 0.2734 0.3395 0.1945 0.1926

$32,500 $34,999 0.2399 0.3356 0.2152 0.2093

$35,000 $37,499 0.2108 0.3322 0.2254 0.2316

$37,500 $39,999 0.1825 0.3143 0.2418 0.2615

$40,000 $42,499 0.1655 0.2840 0.2612 0.2893

$42,500 $44,999 0.1501 0.2688 0.2676 0.3134

$45,000 $47,499 0.1391 0.2550 0.2663 0.3396

$47,500 $49,999 0.1207 0.2387 0.2649 0.3758

$50,000 $52,499 0.1188 0.2142 0.2569 0.4101

$52,500 $54,999 0.1016 0.2012 0.2566 0.4407

$55,000 $57,499 0.0945 0.1894 0.2480 0.4682

$57,500 $59,999 0.0901 0.1853 0.2256 0.4990

$60,000 $62,499 0.0844 0.1684 0.2102 0.5371
Rome Model 2009 Base Year Update 

14 Technical Report 

TAZ-Level Median HH 

Income 

Income Group 1 Income Group 2 Income Group 3 Income Group 4 

< $20,000 $20,000 - $39,999 $40,000 - $59,999 > $60,000 

$62,500 $64,999 0.0766 0.1598 0.2025 0.5612

$65,000 $67,499 0.0688 0.1510 0.1948 0.5854

$67,500 $69,999 0.0653 0.1416 0.1926 0.6004

$70,000 $72,499 0.0601 0.1271 0.1833 0.6295

$72,500 $74,999 0.0535 0.1218 0.1698 0.6549

$75,000 $77,499 0.0512 0.1087 0.1636 0.6765

$77,500 $79,999 0.0485 0.1042 0.1551 0.6922

$80,000 $82,499 0.0446 0.0991 0.1465 0.7099

$82,500 $84,999 0.0405 0.0939 0.1455 0.7202

$85,000 $87,499 0.0364 0.0889 0.1359 0.7387

$87,500 $89,999 0.0350 0.0839 0.1238 0.7573

Table 2.2.3-3 

Size/Income//Auto Ownership Distribution 

Income 

Group 

Persons 

Per 

Household

Autos Available

0 1 2 3+

1 1 0.3063 0.6689 0.0248 0.0000

 2 0.0978 0.6578 0.2222 0.0222

 3 0.0733 0.6909 0.1628 0.0730

 4 0.1000 0.5694 0.1765 0.1541

2 1 0.2548 0.4776 0.2259 0.0417

 2 0.0400 0.2140 0.6320 0.1140

 3 0.1111 0.1256 0.6033 0.1600

 4 0.0900 0.1080 0.5942 0.2078

3 1 0.1833 0.6056 0.1578 0.0533

 2 0.0274 0.1677 0.6343 0.1707

 3 0.0900 0.1050 0.5033 0.3017

 4 0.0600 0.0438 0.3862 0.5100

4 1 0.0577 0.6654 0.2000 0.0769

 2 0.0694 0.1044 0.5322 0.2939

 3 0.0200 0.0581 0.5098 0.4121

 4 0.0189 0.0405 0.5405 0.4000
Rome Model 2009 Base Year Update 

15 Technical Report 

2.2.4 Trip Production 

The routine for computing trip productions uses cross-classified data from the household 

stratification model and applies trip rates to calculate Home Based Work, Home Based Other, 

Home Based Shopping, and Non Home Based Productions. Trip rates for each purpose are 

shown below. 

Table 2.2.4-1 

Household 

Size 0 Autos 1 Auto 2 Autos 3+ Autos

Home Based Work 

1 0.520 0.800 0.800 0.800

2 1.056 1.474 1.782 1.848

3 1.406 1.748 2.014 2.261

4+ 1.800 2.160 2.520 2.880

Home Based Other 

1 0.918 1.605 1.872 1.600

2 1.834 2.444 3.401 3.612

3 3.947 4.521 5.295 5.537

4+ 5.600 6.224 7.673 8.294

Home Based Shopping 

1 0.486 0.555 0.288 0.560

2 0.758 1.174 0.973 0.924

3 0.197 0.631 0.641 1.127

4+ 0.400 0.976 0.727 1.306

Non-Home Based 

1 0.676 1.040 1.040 1.040

2 1.152 1.608 1.944 2.016

3 1.850 2.300 2.650 2.975

4+ 2.200 2.640 3.080 3.520

Trip end productions for other purposes are calculated using the following regression equations: 

I-I Truck Productions = 0.388*hh + 1.206*retail + 1.362*(manuf + whole) + 0.514*service 

I-E Passenger Car Productions = 0.331*Households + 0.724*Total Employment 

I-E Truck Productions = 0.078*Retail Employment + 2.149*Wholesale Employment + 0.228*Manufacturing 

Employment 

2.2.5 Trip Attraction Sub-model 

The trip attraction routine to compute the estimated number of trips attracted to each TAZ uses 

the following regression equations: 
Rome Model 2009 Base Year Update 

16 Technical Report 

Home Based Work Attractions = 1.196*Total Employment 

Home Based Other Attractions = 0.5077*Population + 0.967*Total Employment + 1.5258*School 

Enrollment 

Home Based Shopping Attractions = 5.655*Retail Employment 

Non-Home Based Attractions = 0.293(Population) + 2.82108*(Retail Employment + Wholesale 

Employment) + 0.6984*Service Employment 

Internal Truck Attractions = Internal Truck Productions 

Internal-External Attractions = Based on counts and EE% (internal zones=0) 

Internal-External Truck Attractions = Based on counts, EE%, and Truck% (internal zones=0) 

The trip rates were subsequently refined system-wide on case by case basis during the model 

calibration process to reflect local variations. 

The total number of Internal-External (I-E) trips for each external station is calculated by 

subtracting the estimated number of External-External trips (based on an assumed percentage) 

from the station’s daily traffic volumes. Then the total I-E trips are separated into I-E truck trips

and other I-E trips based on an assumed truck percentage at each external station. The following 

table displays the percentages that are used to calculate I-E and E-E Attractions at each external 

station for truck and passenger cars. 

Table 2.2.5-1 

Proportion of External-Internal Trips by External Station 

 Trucks PC's 

Model 

Station 2009 Percent Percent Percent Percent Percent Percent

Number Counts Trucks PC's E-E I-E E-E I-E

234 190 5% 95% 1% 4% 10% 85%

235 1,980 20% 80% 2% 18% 9% 71%

236 1,310 5% 95% 1% 4% 10% 85%

237 9,080 10% 90% 2% 8% 14% 76%

238 8,790 20% 80% 3% 17% 13% 67%

239 410 5% 95% 0% 5% 4% 91%

240 1,630 5% 95% 1% 4% 10% 85%

241 16,790 10% 90% 1% 9% 10% 80%

242 1,430 0% 100% 0% 0% 11% 89%

243 524 0% 100% 0% 0% 4% 96%

244 262 5% 95% 0% 5% 4% 91%

245 520 5% 95% 1% 4% 10% 85%

246 315 0% 100% 0% 0% 4% 96%

247 6,360 10% 90% 2% 8% 14% 76%

248 419 0% 100% 0% 0% 11% 89%

249 10,940 10% 90% 1% 9% 10% 80%
Rome Model 2009 Base Year Update 

17 Technical Report 

 Trucks PC's 

Model 

Station 2009 Percent Percent Percent Percent Percent Percent

Number Counts Trucks PC's E-E I-E E-E I-E

250 419 0% 100% 0% 0% 4% 96%

251 2,020 0% 100% 0% 0% 11% 89%

252 1,410 5% 95% 1% 4% 10% 85%

253 315 0% 100% 0% 0% 4% 96%

254 3,420 5% 95% 1% 4% 15% 80%

255 315 0% 100% 0% 0% 4% 96%

256 160 5% 95% 0% 5% 4% 91%

257 5460 10% 90% 1% 9% 10% 80%

258 1,200 5% 95% 1% 4% 10% 85%

259 8,110 25% 75% 3% 22% 8% 67%

2.2.6 External-External Trips 

Two external-external (E-E) trip tables were estimated for the 2009 calibration; one for 

passenger cars and one for trucks. A matrix summarizing the distance in miles between all 

external stations was developed using the 2009 network with illogical movements eliminated. 

This distance matrix serves as a “seed” to develop the final E-E trip tables. The theory behind 

using distance between external stations to help predict external-external trips is that the greater 

the distance between external stations, the more likely there will be external-external trips 

between these external stations. For example, typically, the distance between two external 

stations on either end of an interstate facility would be longer and, likewise, the number of trips 

that will travel between the two external stations on either end of the interstate would be higher. 

The final 2009 external trip tables were estimated by applying the FRATAR procedure on the 

“seed” matrix to match the estimated E-E trips at each external station. Because E-E traffic 

volumes on collectors and local streets are relatively low, it is assumed these movements were 

negligible. 

2.2.7 Special Trip Generators 

Special trip purposes are used for zones or activity centers having trip rates that are not 

represented well by the standard trip generation process. Currently, there is no special trip 

generator that requires a special handling in the model for the Rome area. 
Rome Model 2009 Base Year Update 

18 Technical Report 

2.2.8 Balancing Productions and Attractions 

A TP+ script was developed for the trip generation process. Using 2009 socioeconomic data, the 

script calculates and balances the productions and attractions, writes the productions and 

attractions to a file, builds the E-E trip table, calculates Fratar factors, and applies the Fratar 

model to adjust the E-E table so that traffic volumes at external stations closely match traffic 

counts. 

For trip purposes in the FRUTS model, production and attraction trip ends are computed 

separately. As sucha result, the sum of productions across all zones does not necessarily equal 

the sum of attractions. In reality though, each trip has two trip ends; one is a production/origin 

and one is an attraction/destination. In theoryTherefore, it makes sense to equalize the sum of 

productions with the attractions across all zones which, in effect, “balances” the two types of trip 

ends. This balancing or reconciliation is performed in the trip generation script. The script uses 

the process listed below. 

Balancing Productions and Attractions 

1. Productions and Attractions are calculated for all internal TAZs by purpose. 

2. Zonal attractions for each trip purpose are proportionally adjusted so the total attractions 

equal the total productions by purpose (i.e. attractions balanced to productions) for all 

internal zones. 

3. Non-home based productions are set equal to non-home based attractions (NHB trip 

productions were generated in the “home” zone, but by definition, NHB trips do not 

begin or end at the home. Therefore, the assumption is that the attraction variables are a 

better indicator of total trips than home based characteristics). 

4. Attractions are balanced to productions for all internal zones (except NHB). 

5. Internal-External Attractions (including trucks) are calculated for external stations. 

6. I-E productions (including trucks) are balanced to the calculated attractions (assumes that 

since I-E attractions are based on traffic counts or external station projections, they 

provide the best controls). 

7. The I-E productions and attractions are appended to the I-I trip end file to produce the 

final productions and attractions. 

2.3 Trip Distribution 

Trips are calculated for persons, by trip purpose, from the production and attraction trip ends. 

The trip distribution step uses the gravity model process, which is commonly used for this 

purpose in urban models. The estimated number of trips between any two origin-destination 

zones will, in general, be proportional to the number of trip ends (mass) and inversely 

proportional to the travel time. The gravity model computes trips such that the resulting 

distribution matches an observed distribution of trips by travel time for each of the trip purposes. 

Minimum time paths for the network were calculated using the TP+ Hwyload function. These 

times include all turn prohibitory prohibitors and turn penalties. The minimum times were then 
Rome Model 2009 Base Year Update 

19 Technical Report 

adjusted to include the intrazonal times and terminal times. Intrazonal times,, representing the 

average time it takes to make a trip inside a particular TAZ, were created by the TP+ Matrix 

function using half of the average travel time to the nearest four TAZ’s. Terminal times were 

assigned based on the employment density of the origin and destination TAZ’s. At the trip 

origin, terminal time generally refers representsto the walk time from one’s residence to their car. 

At the destination end, it generally represents the time it takes to go from one’s car to their 

destination. The following table summarizes the terminal time criteria: 

Table 2.3-1 

Terminal Time Criteria 

Zone 

Employment Density 

(Total Employees per Acre) 

0-4.00 3.01-15.00 15.01-50.00 25.01-75.00 >75.00 

Origin 1.0 minute 1.0 minute 2.0 minutes 2.0 minutes 2.0 minutes 

Destination 1.0 minute 1.5 minutes 2.0 minutes 2.5 minutes 3.0 minutes 

Average trip travel times are displayed in the following table. These are typical trip travel times, 

found in urban areas the size of Rome. Home Based Work trips have the longest trip travel time 

at 16.1 minutes while Non Home Based trips have the shortest travel time at 11.9 minutes. The 

comparison of the model results with the target values is shown in Appendix B. 

Table 2.3-2 

Average Trip Travel Times 

Trip Purpose

Average Trip 

Travel Time 

(Time)

Home Based Work 16.1 

Home Based Other 14.6 

Home Based Shopping 13.8 

Non Home Based 11.9 

Trucks 12.2 

Gravity model input consists of a set of travel time impedance factors (friction factors), in 

addition to the production trip ends, attraction trip ends and minimum time skim. These 

parameters force the gravity model to produce sets of trips by trip purpose, whose distributions 

approximate an observed travel time distribution. 

2.4 Mode Split 

The mode split process determines what mode of travel will be used to make the trips between 

zones. A trip-end model split was used to determine the number of transit trips. Trip-end models 

are based on socio-economic characteristics within traffic zones (i.e., income, auto-ownership, 
Rome Model 2009 Base Year Update 

20 Technical Report 

etc.), rather than service characteristics between zones. Trip-end models can serve as a 

reasonable tool for estimating changes in regional transit ridership levels in response to changes 

in regional transit investments and policies. 

Since trip-end models are based on zonal attributes, the implied assumption is that transit 

ridership consists primarily of “captive” riders who must use transit, and a fixed share of those 

who choose to use transit over other available modes. A trip-end modeling approach has been 

developed that can be used to test the effectiveness of system-wide transit improvements. The 

general approach is as follows: 

1. Transit trip rates are calibrated to replicate base year transit ridership totals. Transit trip 

rates are stratified by auto ownership level (0 autos; 1 auto; 2 autos; 3+ autos)3. 

2. Each Traffic Analysis Zone (TAZ) is coded with attributes that designate whether a zone 

is within three transit service area buffers (0.25-mile, 0.5-mile and 1-mile). 

3. Transit service area attributes are used to estimate the percentage of households in each 

zone that have access to transit. 

4. Transit trips are estimated by applying transit trip rates to households that are within the 

transit service area. 

5. Highway person trip tables are estimated by subtracting transit trips from the total person 

trip tables estimated in the trip generation model.

Future regional-level transit policies can be tested, including transit service area expansion, fare 

adjustments and headway4 changes. Trip rates can also be factored to test the effects of attracting 

more choice riders. Base year operating costs and revenues are used to estimate the costs of the 

regional transit policies. 

Rome has a transit system serving the area; the Rome Transit. After the transit trips are 

calculated and subtracted, the remaining person trips are converted to vehicle trips for the 

internal trip purposes (HBW, HBO, HBS, and NHB). Average auto occupancy rates by purpose 

from various sources such as the Census Journey-to-Work data and other national travel surveys 

were used to estimate the Rome average auto occupancy rate. The other trip tables, internal 

truck and I-E and E-E passenger car and truck trips were calculated in terms of vehicle trips at 

their inception. Conversion to vehicle trip table enables comparison to vehicle counts and 

capacity analyses. 

The following trip table factors or vehicle occupancy rates were used in the FRUTS model: 

Table 2.4-1 

FRUTS Vehicle Occupancy Rates 

Trip Purpose Occupancy Rate 

Home Based Work 1.104 

Home Based Other 1.576 

 

3 Initial trip rates were calculated from the 1997 Augusta Household Travel Survey, and are scaled proportionally to produce observed trip totals. 

4 Using typical fare and headway elasticities.
Rome Model 2009 Base Year Update 

21 Technical Report 

Trip Purpose Occupancy Rate 

Home Based Shopping 1.394 

Non Home Based 1.495 

Internal Trucks No adjustment – already vehicle trips 

Internal-External No adjustment – already vehicle trips 

Internal-External Trucks No adjustment – already vehicle trips 

2.5 Traffic Assignment 

The last step in the modeling sequence is the assignment or simulation of the trip tables to logical 

routes in the highway network. Trip assignment for the FRUTS model was accomplished using 

the equilibrium assignment technique. The traffic assignment algorithm is iterative, running 

through successive applications until equilibrium occurs. Equilibrium occurs when no trip can 

be made by an alternate path without increasing the total travel time of all trips in the network. 

The equilibrium assignment is an iterative process that reflects travel demand assigned to 

minimum time paths as well as the effects of congestion. In each assignment iteration, traffic 

volumes are loaded onto network links and travel times are adjusted in response to the volume to 

capacity relationships. Final assigned volumes are derived by summing a percentage of the 

loadings from each iteration. The percentages reflect congested conditions that usually influence 

motorists' path selection for a portion of the day, not the entire day. 

During the model run, additional network link attributes are attached to the input network to 

store assignment results as well as values used in the traffic assignment. These additional 

attributes provide volumes, travel time, speed, and so on for each link, and can be used to 

summarize network-wide link statistics. A list of these attributes is shown in Table 2.5-1. 

Table 2.5-1 

Output Network Attributes 

Travel Demand Model – Output Network Attributes 

Attribute Name Description 

Taz Nearest Taz ID 

Taz Closest Associated TAZ# 

Atype Area Type 

Hcap Hourly Capacity (Vehicles per Hour) 

Hcapam AM Peak Hourly Capacity (Vehicles per Hour) 

Hcappm PM Peak Hourly Capacity (Vehicles per Hour) 

Capacity Daily Capacity (Vehicles per Day) 

Speed Freeflow Speed in Mile per Hour (Miles per Hour) 

Time_ff Free Flow Travel Time (Minutes) 

Time_cg Initially Estimated Congested Time (Minutes) 

Time_op Off-peak Travel Time (Minutes) 

Linkclass Link Classification Used in Assignment 
Rome Model 2009 Base Year Update 

22 Technical Report 

Travel Demand Model – Output Network Attributes 

Attribute Name Description 

V_1 Daily Volume (One-way) 

Time_1 Congested Link Travel Time 

Vc_1 Daily Volume over Capacity Ratio 

Cspd_1 Congested Speed (Miles per Hour) 

Vht_1 Vehicle Hour of Travel 

Vt_1 Daily Volume (Two-way) 

Count Base Year Traffic Count (One-way) 

V_hbw Home Based Work Volumes 

V_hbo Home Based Other Volumes 

V_hbs Home Based Shopping Volumes 

V_nhb Non-home Based Volumes 

V_trk Truck Volumes 

V_ie Internal-External Volumes 

V_ietrk Internal-External Truck Volumes 

V_eepc External-External Volumes 

V_eetrk External-External Truck Volumes 

V_totpc Daily Volume (One-way passenger) 

V_tottrk Daily Volume (One-way truck) 

Vmt_1 Total Daily Vehicle Mile of Travel 

Vhd_1 Total Daily Vehicle Hours of Delay 

Vcnt Daily Volume over Base Year Count Ratio 

2.5.1 Model Calibration 

Georgia DOT requires refinements to various model parameters until the base year (2009) model 

sufficiently replicates observed 2009-level travel patterns. The base year model was checked for 

accuracy by determining the percent error of assigned volumes compared to ground counts and 

by checking the reasonableness of the model’s Vehicle-Miles Traveled (VMT) statistics. Also, 

the model was tested along screenlines to indicate if there were any broad areas and corridors 

where trips appeared to be consistently overestimated or underestimated. Results from each of 

these tests are presented in the following tables and figures. 

Georgia DOT requires multiple validation checks to each of the major steps in the travel demand 

modeling process. Output modeled volumes are validated against traffic counts at several levels 

– regional, corridors (screenlines) and link-by-link. Regional evaluations include VMT, Root 

Mean Squared Error (RMSE) and R-Squared calculations for volume-count matching. Corridor 

evaluations are primarily screenline comparisons. Nationally recognized maximum desirable 

deviation standards are applied to analyze model performance at the link level. These include 
Rome Model 2009 Base Year Update 

23 Technical Report 

FHWA’s “Calibration & Adjustment of System Planning Models”, 1990 and the NCHRP Report 

365: “Travel Estimation Techniques for Urban Planning”, 1998. 

One of many steps in the validation process involves screenlines. Screenlines are defined by 

features such as railroads, creeks, and rivers. Since all roadways are not reflected in the travel 

demand model, these types of features serve to funnel traffic into corridors so that all trips can be 

analyzed where crossing of these features is possible. Figure 2.5.1-1 depicts the locations of 

screenlines used for the validation process. Where each screenline crosses a roadway can be 

identified by following the color coded links across the FRUTS area. 
Rome Model 2009 Base Year Update 

24 Technical Report 

Figure 2.5.1-1 

Screenline Locations 
Rome Model 2009 Base Year Update 

25 Technical Report 

Table 2.5.1-1 lists the results of the screenline analysis. All of the model volumes for the 

screenlines are well within the acceptable range of error when compared to the observed traffic 

volumes. 

Table 2.5.1-1 

Summary of the Screenlines 

Screenlines 

Target FRUTS 2009 

Range / Value Model 

All Counts +/- 12 % 6% 

1 +/- 36 % -3% 

2 +/- 29 % 9% 

3 +/- 25 % -7% 

4 +/- 29 % 13% 

5 +/- 29 % 10% 

6 +/- 28 % 9% 

7 +/- 29 % 7% 

8 +/- 26 % 5% 

9 +/- 27 % 9% 

Table 2.5.1-2 lists the results of the comparison between the FRUTS model assigned volumes 

and the observed volumes for each link within each screenline. In most cases, the largest 

differences between the model and observed counts occur on the less traveled facilities. 

Table 2.5.1-2 

Screenline Results 

Screenline 1: 

 2009 2009 Volume Percent Maximum

 Assign Traffic /Count Deviation Desirable

Road Name Volume Count Ratio From Base Deviation

SR 53 9,980 10,030 1.00 -0.50% 38.21%

SR 53 14,730 14,630 1.01 0.68% 32.41%

Old Bells Ferry Road 1,150 370 3.11 210.81% 161.13%

SR 1 9,000 11,340 0.79 -20.63% 36.22%

Old Dalton Rd. 2,360 1,970 1.20 19.80% 77.71%

Total 37,220 38,340 0.97 -2.92% 35.75%
Rome Model 2009 Base Year Update 

26 Technical Report 

Screenline 2: 

 2009 2009 Volume Percent Maximum

 Assign Traffic /Count Deviation Desirable

Road Name Volume Count Ratio From Base Deviation

SR 1 (North) 32,840 34,370 0.96 -4.45% 22.33%

Bells Ferry 1,190 710 1.68 67.61% 121.26%

SR 53 18,130 14,220 1.27 27.50% 32.82%

Calhoun Road (North) 6,590 6,270 1.05 5.10% 46.90%

SR 293 8,530 7,710 1.11 10.64% 42.86%

Wayside Rd. 2,870 1,220 2.35 135.25% 95.76%

Total 70,150 64,500 1.09 8.76% 29.32%

Screenline 3: 

 2009 2009 Volume Percent Maximum

 Assign Traffic /Count Deviation Desirable

Road Name Volume Count Ratio From Base Deviation

Broad Street 13,860 13,740 1.01 0.87% 33.31%

SR 101(South) 24,550 24,440 1.00 0.45% 25.91%

Horseleg Creek Rd. 4,120 3,760 1.10 9.57% 58.62%

SR 1/SR 20 28,010 32,260 0.87 -13.17% 22.96%

Billy Pyle Rd 1,270 1,020 1.25 24.51% 103.54%

Mays Bridge Rd 2,160 5,300 0.41 -59.25% 50.47%

Mt. Alto Rd 160 1,440 0.11 -88.89% 89.09%

Burnett Ferry Rd. 2,080 2,340 0.89 -11.11% 72.09%

Barker Rd 730 480 1.52 52.08% 143.84%

SR 1 Loop 12,570 11,850 1.06 6.08% 35.53%

Fosters Mill Road 3,480 3,500 0.99 -0.57% 60.48%

Total 92,990 100,130 0.93 -7.13% 24.80%

Screenline 4: 

 2009 2009 Volume Percent Maximum

 Assign Traffic /Count Deviation Desirable

Road Name Volume Count Ratio From Base Deviation

Callier Springs Rd. 4,520 1,980 2.28 128.28% 77.53%

CR 639 430 3,440 0.13 -87.50% 60.94%

Lindale Rd 7,390 7,550 0.98 -2.12% 43.25%

SR 1 (South) 14,740 18,420 0.80 -19.98% 29.31%

Spring Rd. 6,300 3,500 1.80 80.00% 60.48%
Rome Model 2009 Base Year Update 

27 Technical Report 

Screenline 4: 

 2009 2009 Volume Percent Maximum

 Assign Traffic /Count Deviation Desirable

Road Name Volume Count Ratio From Base Deviation

Chateau Dr 820 610 1.34 34.43% 129.56%

SR 101 10,580 11,180 0.95 -5.37% 36.45%

Old Lindale Rd 6,910 3,740 1.85 84.76% 58.75%

Total 51,690 50,420 1.03 2.52% 32.21%

Screenline 5: 

 2009 2009 Volume Percent Maximum

 Assign Traffic /Count Deviation Desirable

Road Name Volume Count Ratio From Base Deviation

SR 101 7,830 8,520 0.92 -8.10% 41.03%

Reeceburg Rd 1,090 1,030 1.06 5.83% 103.10%

SR 1 (South) 11,990 9,970 1.20 20.26% 38.31%

Bowman Rd. 2,100 2,980 0.70 -29.53% 64.87%

Wax Rd. 1,720 1,830 0.94 -6.01% 80.24%

US 411/SR 53 5,090 3,390 1.50 50.15% 61.33%

SR 100 3,990 2,510 1.59 58.96% 69.91%

Abrams Rd. 1,660 630 2.63 163.49% 127.75%

Total 35,470 30,860 1.15 14.94% 38.83%

Screenline 6: 

 2009 2009 Volume Percent Maximum

 Assign Traffic /Count Deviation Desirable

Road Name Volume Count Ratio From Base Deviation

Blacks Bluff Rd. 4820 3590 1.34 0.3426 0.5981

Horseleg Creek Rd. 880 630 1.40 0.3968 1.2775

Mays Bridge Road 1,860 930 2.00 1 1.078

Huffaker Rd 780 940 0.83 -17.02% 107.30%

SR 20 (West) 13,170 11,720 1.12 12.37% 35.70%

Total 21,510 17,810 1.21 20.77% 47.88%

Screenline 7: 

 2009 2009 Volume Percent Maximum

 Assign Traffic /Count Deviation Desirable

Road Name Volume Count Ratio From Base Deviation

SR 20 (West) 22,690 25,390 0.89 -10.63% 25.49%
Rome Model 2009 Base Year Update 

28 Technical Report 

Screenline 7: 

 2009 2009 Volume Percent Maximum

 Assign Traffic /Count Deviation Desirable

Road Name Volume Count Ratio From Base Deviation

Burnett Ferry Road 3,670 6,397 0.57 -42.63% 46.49%

Technology Parkway 21,850 22,520 0.97 -2.98% 26.85%

Horseleg Creek Rd. 1,920 2,730 0.70 -29.67% 67.40%

Total 50,130 57,037 0.88 -12.11% 30.73%

Screenline 8: 

 2009 2009 Volume Percent Maximum

 Assign Traffic /Count Deviation Desirable

Road Name Volume Count Ratio From Base Deviation

SR 1 26,100 35,670 0.73 -26.83% 21.97%

5th Avenue 8,010 7,200 1.11 11.25% 44.16%

2nd Avenue 30,910 28,730 1.08 7.59% 24.15%

SR 1 Loop 27,130 28,270 0.96 -4.03% 24.32%

Total 92,150 99,870 0.92 -7.73% 24.82%

Screenline 9: 

 2009 2009 Volume Percent Maximum

 Assign Traffic /Count Deviation Desirable

Road Name Volume Count Ratio From Base Deviation

US 27 27,360 25,810 1.06 6.01% 25.30%

SR 1 21,300 19,500 1.09 9.23% 28.59%

5th Avenue 13,160 16,520 0.80 -20.34% 30.74%

Riverside Parkway 5,860 6,600 0.89 -11.21% 45.86%

SR 53 14,610 14,800 0.99 -1.28% 32.25%

SR 293 13,510 9,800 1.38 37.86% 38.60%

Callier Springs Road 1,180 2,180 0.54 -45.87% 74.35%

Dean Avenue 11,890 14,690 0.81 -19.06% 32.35%

Maple Ave. 6,790 7,170 0.95 -5.30% 44.24%

Blacks Bluff Rd. 770 1,350 0.57 -42.96% 91.63%

Cave Springs Road 7,340 6,450 1.14 13.80% 46.33%

SR 1 (North Bound) 16,170 14,750 1.10 9.63% 32.30%

Total 139,940 139,620 1.00 0.23% 21.84%

Another way of viewing link validation is through the use of a scatter plot that depicts the 

relationship between traffic counts and modeled volumes. The following graphic depicts this 
Rome Model 2009 Base Year Update 

29 Technical Report 

relationship for the FRUTS 2009 network. The graphic indicates that the majority of modeled 

volumes are consistent with the traffic counts. It should be noted that it is normal to have 

outliers, both high and low. The R2

 value of 0.95 indicates the model sufficiently replicates base 

year travel characteristics. 

Figure 2.5.1-2 

 

The modeled traffic volumes summarized by facility type are shown in Table 2.5.1-3. The 

HPMS VMT is based on the Georgia Department of Transportation’s Office of Transportation 

Data “445 Report”. as well as on the data from South Carolina Department of Transportation. 

The HPMS VMT numbers represent the average annual daily VMT for the year 2009 for all of 

Columbia, Richmond, and Aiken County and parts of Edgefield County. The HPMS VMTs for 

Edgefield County were prorated based on its roadway mileage by functional classifications. 

The highway network and trip table are considered to provide a good representation of travel 

conditions on the existing system if the total percent error region-wide for the VMT is less than 

+/-5 percent. For the FRUTS model, the total percent error region-wide is less than about 3% 

excluding the local streets. Calculating the percent error by facility type indicates whether the 

model is loading trips in a reasonable manner. The FRUTS model is performing very well 

estimating traffic volumes for all of the facility types except for local roads. This is not 

surprising or alarming since most of the local roads in an urban area are usually not included in a 

R2 = 0.95

0

5000

10000

15000

20000

25000

0 5000 10000 15000 20000 25000

Modeled Volume

Count

Travel Demand Model Scatter Plot
Rome Model 2009 Base Year Update 

30 Technical Report 

regional travel demand model. These volumes are provided below just for informational 

purposes only. 

Table 2.5.1-3 

Travel Demand Model Maximum Desired Deviation Chart

 VMT 

Facility Type Model HPMS Difference Percent 

Principal Arterial 1,032,000 1,098,000 -66,000 -6.0%

Minor Arterials 630,000 591,000 40,000 6.7%

Collectors 398,000 366,000 32,000 8.9%

Local 365,000 138,000 227,000 164.1%

Total 2,426,000 2,193,000 233,000 10.6%

Comparing the deviation of assigned link volumes with the maximum desirable deviation is also 

a method for checking the model validation and calibration check. The higher the link traffic 

count, the smaller the maximum desired deviation allowed on that link. Generally, models should 

be able to replicate traffic volumes on higher designed facilities more accurately than those on 

lower designed facilities. Since Hhigher designed facilities have higher usage, they are 

frequently the and often are focuses of various transportation studies and strategies policy 

making. Therefore, how well the model assigns trips on these facilities is another indicator for 

how well the model is validated and calibrated and how useful the model would be. Figure 2.5.1-

3 shows the comparison of the maximum desired deviation curve and the model assigned 

volumes. Figure 2.5.1-4 illustrates the trip-loaded network. The model performed very well 

system-wide with almost all major facilities within the maximum desired deviation allowed. 

Figure 2.5.1-3 
Rome Model 2009 Base Year Update 

31 Technical Report 

Figure 2.5.1-4 

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

0 5,000 10,000 15,000 20,000 25,000

Percent Deviation (Model Volume -vs- Directional Count)

Traffic Count (Directional)

Travel Demand Model

Percent Difference from Counts Maximum Desirable Deviation "Maximum Desirable Deviation Neg"
Rome Model 2009 Base Year Update 

32 Technical Report 

2.5.2 Delta Matrix Process 

Due to the many variables involved, estimated traffic volumes from travel demand models will 

inevitably differ from observed traffic counts. As a result, it is usually necessary to post-process 

modeled volumes for use in traffic studies. National Cooperative Highway Research Program 

(NCHRP) Report 255 outlines a widely used methodology for post-processing model results, but 

like many approaches to refining travel demand models, the procedures are intended for specific 

projects or corridors and are not easily applied to an entire region. 

Exceed Maximum Desired Deviation 

(Over Assignment)

Within Maximum Desired Deviation

Exceed Maximum Desired Deviation 

(Under Assignment)
Rome Model 2009 Base Year Update 

33 Technical Report 

Matrix estimation techniques to post-process travel demand model volumes for an entire region 

have been developed for the GDOT MPO areas. This region level post-processing is done by 

developing a delta matrix, which is a trip table that is combined with the normal travel demand 

model trip table to produce traffic assignments that closely replicate observed traffic counts. 

Figure 2.5.2-1 outlines how a delta matrix is developed. The delta matrix process uses the travel 

demand model trip table as a seed for a matrix estimation process. The matrix estimation process 

attempts to closely replicate observed traffic counts, while also controlling the trip ends and trip 

lengths possessed by the seed matrix. This is accomplished by iteratively assigning a trip table, 

adjusting the trip table to match traffic counts, then applying a tri-proportional fitting process to 

match trip ends and trip lengths. Once a trip table is produced that sufficiently matches the traffic 

counts, a delta matrix is produced by subtracting the initial seed trip table from the estimated trip 

table. 

Figure 2.5.2-1 – Delta Matrix Process 

Conceptually the resulting delta matrix represents the localized factors that the regional travel 

demand modeling process does not reproduce well. Future travel demands are post-processed by 

applying the same local corrections that are represented in the delta matrix without adjustment 

since similar localized issues cannot be identified for future conditions. Therefore, the delta 

matrix is simply added to future trip tables before assigning the trips. 
Rome Model 2009 Base Year Update 

34 Technical Report 

Appendix A: Travel Demand Model Validation Sample Report 

Travel Demand Model Validation Report

Calibration Measure Target Range / Value Model 

Socio-Economic Data 

Persons / Household 2 - 4 2.7 

Workers / Household 1 - 3 1.2 

Trip Generation 

Person Trips Per Household 8.5 - 9.2 8.1 

Person Trips Per Person 3 - 4 3.0 

HBW Trips / Employee < 2 1.4 

Shopping Trips / Retail Employment - 5.7 

P/A Ratio Before Balancing (HBW) 0.9 - 1.1 1.2 

P/A Ratio Before Balancing (HBO) 0.9 - 1.1 1.1 

P/A Ratio Before Balancing (HBShop) 0.9 - 1.1 1.0 

P/A Ratio Before Balancing (NHB) 0.9 - 1.1 1.0 

Trip Distribution 

Average Trip Length (HBW) 16.0 - 17.7 16.1 

Average Trip Length (HBO) 14.4 - 15.9 14.6 

Average Trip Length (HBShop) 13.9 - 15.4 13.8 

Average Trip Length (NHB) 11.8 – 13.0 11.9 

Average Trip Length (Truck) N/A 12.2 

% Intrazonal Trips < 10% 4.0% 

Trip Assignment 

VMT-Freeway 0 0 

VMT-Arterials 1,689,000 1,663,000 

VMT-Collectors 366,000 398,000 

VMT-Total 2,055,000 2,061,000 

VMT / Household 58.4 58.5 

VMT / Person 21.4 21.4 

Screenlines

All Counts +/- 12 % 6% 

1 +/- 36 % -3% 

2 +/- 29 % 9% 

3 +/- 25 % -7% 

4 +/- 29 % 13% 
Rome Model 2009 Base Year Update 

35 Technical Report 

Travel Demand Model Validation Report

Calibration Measure Target Range / Value Model 

5 +/- 29 % 10% 

6 +/- 28 % 9% 

7 +/- 29 % 7% 

8 +/- 26 % 5% 

9 +/- 27 % 9% 

RMSE = Root Mean Squared Error (Vol) < 94% 28% 

% RMSE (0-5K) < 86% 45% 

% RMSE (5K-10K) < 45% 21% 

% RMSE (10K-15K) < 35% 7% 

% RMSE (15K-20K) < 30% 16%