# 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 ofTABLE 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 ............................................ ....33i 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 Distributi on ................................................ ..............12

Table 2.2.3-2 2000 CTPP Household Income Distribu tions ............................................. ..........13

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

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

................................................... .........................15

Table 2.2.5-1 Proportion of External-Internal Tri ps 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 ..............

................................................... ..........................30ii 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 ..............

................................................... ............................32Rome 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 Stu dy (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 stud ies of transit service.

The travel demand model is used to evaluate the per formance of the roadway system in and around Rome by the Georgia Department of Transporta tion and FRUTS. The FRUTS model is a traditional urban area analysis tool that is used t o identify where major improvements should be made to its principal thoroughfare system. Since t here is usually more than one strategy proposed to address future congestion and safety co ncerns, 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 sour ce emissions and perform the conformity analysis.

There are two key inputs to the travel demand model ing 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 o f socioeconomic data were based on a 2009 inventory of existing land uses including vacant la nd, as well as region wide forecasts of population, households and employment. Future yea r forecasts also considered planned major transportation improvements. Allocation of expected growth is then done using known development patterns and proposals as the basis, ta king 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 ar e connected to the transportation planning process. Figure 1.1-1 shows the interaction between travel demand models and transportation system characteristics and population and employmen t characteristics

The other key element of the travel model is referr ed to as the highway network. The highway network is a computer file containing links and nod es that represent roadway segments and
intersections. Each link record in the file contai ns 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, mai ntenance 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 tec hnical 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 involve d in developing an urban travel model is described. These steps are as follows: Trip Generat ion; Trip Distribution; Mode Split; and Traffic Assignment.



## 2.0 Model Update

Several significant changes were made to the Rome a rea 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 ForecastingRome Model 2009 Base Year Update

3 Model Development Documentation • Updated trip generation model

• Updated trip distribution model

• Updated the traffic assignment procedure (24-Hour d aily)

• Updated external trip model

• Incorporated feedback loop assignment

• Added the delta matrix post-process procedure to re fine 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 th is section.

2.1 Highway Network Coding
The base year model network was updated to reflect 2009 existing conditions. The 200 6 9 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 ac curate routing paths based on the minimum time to travel from one traffic analysis zone to an other. 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 faci lity 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 NetworkRome Model 2009 Base Year Update

5 Model Development Documentation Facility Type and Area Type. Individually and in combination these two link attr ibutes provide the framework for organizing the network in to sub-groups so that free-flow speeds and capacities can be assigned. In combination with th e distance and number of lanes, these attributes constitute the base layer of highway net work data needed to update and apply the travel model. The facility type and area type defi nitions 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 obtai ned from a lookup table of per-lane hourly capacities based on facility type and area t ype. The final link capacity is calculated by multiplying the hourly capacity per lane by the num ber 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 18 60 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 11 00 1080 1060 Principal Arterial – Class II 900 900 900 900 900 8 80 860Rome 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 79 0

Minor Arterial – Class II 630 630 640 640 640 630 6 10 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 fr om a speed lookup table based on facility type and area type. Assumed free-flow spee d are approximately 5 mph faster than typical speed limits for the various roadway classes and ar ea types, taking into consideration control for delay (i.e. traffic signals) if applicable. Peak a nd off-peak free-flow speeds were evaluated using observed speeds obtained from a travel time study c onducted 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 res ults are appropriate for use in all GDOT MPO models since the travel dynamics for these urba n 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 t he following table. While most of them are not directly involved in the model process, they provide assistance inRome Model 2009 Base Year Update

7 Model Development Documentation link attributes summary for post model result proce ssing 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 Ce nsus 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 Datab ase)
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 designate d to identify the accessibility of a node. Only the centroid node s of the network are attached with these
attributes which use non-zero to indicate the avail ability 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 accessRome Model 2009 Base Year Update

8 Model Development Documentation 2.2 Trip Generation

Trip generation is the first step in the traditiona l four-step modeling process. It estimates the number of trips that will begin and end in each ind ividual traffic analysis zone (TAZ). These are referred to as “trip ends”. Trip ends genera ted by households are referred to as productions. Trip ends calculated from employment or school enro llment figures are referred to as attractions. This process is accomplished by establishing relati onships 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 genera tion does not determine the origin and destination of each trip, only the total trips gene rated 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 a nd external travel survey in the Augusta metropolitan area. Household travel behavior by ho usehold size and income group is homogeneous from one urban area to another if trans portation choices and land-use patterns are similar. The Augusta survey information was used t o 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 produ ction 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 c ross-classification of household size (1,2,3,4+) and automobiles available (0,1,2,3+). A ggregate household data for each traffic analysis zone is disaggregated into sixteen cross-c lassified 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 f rom the Census Transportation Planning Package (CTPP), and data from the Augusta household survey. The trip production sub-model applies regression equations for other trip purpose s. The trip attraction sub-model applies regression equations for all trip purposes.

Typically, there are three types of trips that trav el 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 t he 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 com monly grouped into trip purposes so their characteristics can be reproduced by the chain of s ub-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 lead ing into and out of the region.

2.2.1 Trip Purposes

Seven trip purposes were included in the trip gener ation 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 dat a for the model. For each of the traffic analysis zones (TAZ’s) in the model area, the follo wing 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 bus inesses in a given traffic analysis zone where the business is l ocated.

Wholesale and Warehouse Employment : Number of employees working for Wholesale
and w h arehouse based businesses in a given traffic analys is zone where the business is
located.

Total Employment : The total number of employed persons in those tra ffic 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 tr affic analysis zone.

School Enrollment : The total number of enrolled students in zones wi th 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 additiona l 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 t otal 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 are a specific data from the Census Transportation Planning Package (CTPP), and data fr om the Augusta household survey. TheRome Model 2009 Base Year Update

12 Technical Report model distributes the total households in a TAZ to each cross-classification cell by calculating a
relative 1 probability that a household will be a particular s ize with a particular number of automobiles. The relative probability is calculate d 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 househ old survey lookup table.

An estimate of the number of households in a partic ular cross-classification cell is then calculated by multiplying the total number of house holds in the TAZ by the corresponding relative probability. The final number of household s 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 siz e i that own j autos
HH = Total number of households in the TAZ

HHij = HHij(est.) * F, where

HHij = Final number of households 2 of size i that own j autos

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

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

pages.

Table 2.2.3-1
2000 Household Size CTPP Distribution

Computed

HOUSEHOLD SIZES Persons/HH
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 pro blems with round off errors.Rome Model 2009 Base Year Update

13 Technical Report Computed

HOUSEHOLD SIZES Persons/HH
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 0.5371Rome 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 0.4000Rome Model 2009 Base Year Update

15 Technical Report 2.2.4 Trip Production

The routine for computing trip productions uses cro ss-classified data from the household stratification model and applies trip rates to calc ulate 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 calcula ted 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 Employ ment + 0.228*Manufacturing Employment

2.2.5 Trip Attraction Sub-model

The trip attraction routine to compute the estimate d 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-wid e on case by case basis during the model calibration process to reflect local variations.

The total number of Internal-External (I-E) trips f or each external station is calculated by subtracting the estimated number of External-Extern al 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 perce ntage at each external station. The following table displays the percentages that are used to cal culate 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 S tation

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 estima ted for the 2009 calibration; one for passenger cars and one for trucks. A matrix summari zing the distance in miles between all external stations was developed using the 2009 netw ork 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 pr edict external-external trips is that the greater the distance between external stations, the more li kely there will be external-external trips between these external stations. For example, typic ally, the distance between two external stations on either end of an interstate facility wo uld 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 e ach external station. Because E-E traffic
volumes on collectors and local streets are relativ ely low, it is assumed these movements were negligible.

2.2.7 Special Trip Generators

Special trip purposes are used for zones or activit y centers having trip rates that are not represented well by the standard trip generation pr ocess. Currently, there is no special trip generator that requires a special handling in the m odel 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, c alculates Fratar factors, and applies the Fratar model to adjust the E-E table so that traffic volum es at external stations closely match traffic

counts.

For trip purposes in the FRUTS model, production an d attraction trip ends are computed separately. As such a result , the sum of productions across all zones does not necessarily equal the sum of attractions. In reality thou gh , each trip has two trip ends; one is a production/ origin
and one is an attraction/destination. In theory Therefore , it makes sense to equalize the sum of productions with the attractions across all zones w hich, 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 proport ionally adjusted so the total attractions equal the total productions by purpose (i.e. attrac tions balanced to productions) for all
internal zones.
3. Non-home based productions are set equal to non-hom e 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 assumptio n is that the attraction variables are a better indicator of total trips than home based cha racteristics).
4. Attractions are balanced to productions for all int ernal zones (except NHB).
5. Internal-External Attractions (including trucks) ar e 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 o r 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 attractio n trip ends. The trip distribution step uses the gravity model p rocess, which is commonly used for this purpose in urban models. The estimated number of t rips between any two origin-destination zones will, in general, be proportional to the numb er 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 tr ips 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 thenRome Model 2009 Base Year Update

19 Technical Report adjusted to include the intrazonal times and termin al times. Intrazonal times

, , representing the average time it takes to make a trip inside a parti cular TAZ, were created by the TP+ Matrix function using half of the average travel time to t he nearest four TAZ’s. Terminal times were assigned based on the employment density of the ori gin and destination TAZ’s. At the trip origin, terminal time generally refers represents to 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 te rminal 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 minute s 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 foll owing 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 val ues 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 tim e impedance factors (friction factors), in addition to the production trip ends, attraction tr ip 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 trav el will be used to make the trips between zones. A trip-end model split was used to determin e 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 wh o must use transit, and a fixed share of those
who choose to use transit over other available mode s. A trip-end modeling approach has been developed that can be used to test the effectivenes s 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 aut os; 1 auto; 2 autos; 3+ autos) 3

. 2. Each Traffic Analysis Zone (TAZ) is coded with attr ibutes 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 estimat e the percentage of households in each
zone that have access to transit.
4. Transit trips are estimated by applying transit tri p rates to households that are within the
transit service area.
5. Highway person trip tables are estimated by subtrac ting transit trips from the total person trip tables estimated in the trip generation model.

Future regional-level transit policies can be teste d, including transit service area expansion, fare adjustments and headway 4 changes. Trip rates can also be factored to test th e effects of attracting more choice riders. Base year operating costs and r evenues are used to estimate the costs of the regional transit policies.

Rome has a transit system serving the area; the Rom e Transit. After the transit trips are calculated and subtracted, the remaining person tri ps are converted to vehicle trips for the internal trip purposes (HBW, HBO, HBS, and NHB). Av erage 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 occupan cy 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 e nables comparison to vehicle counts and capacity analyses.

The following trip table factors or vehicle occupan cy 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 pro portionally 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 tri ps Internal-External No adjustment – already vehicle t rips Internal-External Trucks No adjustment – already ve hicle trips

2.5 Traffic Assignment

The last step in the modeling sequence is the assig nment 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 a ssignment algorithm is iterative, running through successive applications until equilibrium o ccurs. 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 conges tion. In each assignment iteration, traffic
volumes are loaded onto network links and travel ti mes 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 refl ect 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 attri butes 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 t hese 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 Ho ur)
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 AssignmentRome 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 p arameters 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 assign ed volumes compared to ground counts and by checking the reasonableness of the model’s Vehic le-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 overestimat ed or underestimated. Results from each of these tests are presented in the following tables a nd figures.

Georgia DOT requires multiple validation checks to each of the major steps in the travel demand modeling process. Output modeled volumes are valida ted against traffic counts at several levels – regional, corridors (screenlines) and link-by-lin k. Regional evaluations include VMT, Root Mean Squared Error (RMSE) and R-Squared calculation s for volume-count matching. Corridor evaluations are primarily screenline comparisons. N ationally recognized maximum desirable deviation standards are applied to analyze model pe rformance at the link level. These includeRome 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 involve s screenlines. Screenlines are defined by features such as railroads, creeks, and rivers. Si nce all roadways are not reflected in the travel demand model, these types of features serve to funn el traffic into corridors so that all trips can be analyzed where crossing of these features is possib le. 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 acros s the FRUTS area.Rome Model 2009 Base Year Update

24 Technical Report Figure 2.5.1-1
Screenline LocationsRome Model 2009 Base Year Update

25 Technical Report Table 2.5.1-1 lists the results of the screenline a nalysis. 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
Model Range / Value

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 b etween 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 o ccur 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 t he use of a scatter plot that depicts the relationship between traffic counts and modeled vol umes. The following graphic depicts thisRome Model 2009 Base Year Update

29 Technical Report relationship for the FRUTS 2009 network. The graphi c 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 R 2 value of 0.95 indicates the model sufficiently rep licates 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 Tran sportation’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 an nual daily VMT for the year 2009 for all of Columbia, Richmond, and Aiken County and parts of Edgefield County. The HPMS VMT s for Edgefield County were prorated based on its roadway mileage by functional classifications

.

The highway network and trip table are considered t o provide a good representation of travel conditions on the existing system if the total perc ent error region-wide for the VMT is less than +/-5 percent. For the FRUTS model, the total perce nt error region-wide is less than about 3% excluding the local streets. Calculating the percen t 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 road s in an urban area are usually not included in a



### R2 = 0.95

0 5000 10000 15000 20000 25000

0 5000 10000 15000 20000 25000Modeled Volume

Count



### Travel Demand Model Scatter PlotRome Model 2009 Base Year Update

30 Technical Report regional travel demand model. These volumes are pr ovided 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 wi th 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 al lowed 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 H h igher designed facilities have higher usage , they are frequently the and often are focus es of various transportation studies and strategies policy making . Therefore, how well the model assigns trips on th ese 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 devia tion 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 2.5.1-3Rome 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 25,000Percent 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 traff ic 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. Nationa l Cooperative Highway Research Program (NCHRP) Report 255 outlines a widely used methodolo gy for post-processing model results, but like many approaches to refining travel demand mode ls, 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 re gion level post-processing is done by developing a delta matrix, which is a trip table th at is combined with the normal travel demand model trip table to produce traffic assignments tha t closely replicate observed traffic counts.

Figure 2.5.2-1 outlines how a delta matrix is devel oped. The delta matrix process uses the travel demand model trip table as a seed for a matrix esti mation process. The matrix estimation process attempts to closely replicate observed traffic coun ts, while also controlling the trip ends and trip lengths possessed by the seed matrix. This is accom plished by iteratively assigning a trip table, adjusting the trip table to match traffic counts, t hen 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 t he 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. F uture travel demands are post-processed by applying the same local corrections that are repres ented 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 R eport

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%