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TABLE 5
METHOD OF FRATAR FACTORING LONG DISTANCE OD TABLES
Long Distance
Trip Purpose Production Attraction Balance To
Business Household Total employment Production
Tourist Household Retail/service employment Production
Other Household Households and total employment Production
was handled. OD data from the ATS were available only for Gross state product: $214 billion
county-to-county trips. The OD table was expanded to TAZs No. of zones: 4,720
by apportioning the trips by zonal households, zonal em- External zone structure: Halo
ployment, or both depending on the trip purpose and trip end. Internal zone structure: TAZs
No. of links: 34,500
Traffic assignment was accomplished with a static user- No. of signals: 3,900
equilibrium technique, with trucks preloaded to the network Travel modes: Automobile, truck, intercity bus/rail
and weighted by passenger car equivalent factors that de- Trip purposes:
pended on terrain. Delay came from BPR curves as a func- Home-based work
tion of free flow speed and capacity. Free flow speeds were Home-based nonwork
drawn from a table, and these speeds varied by functional Nonhome-based
classification, terrain type, number of lanes, and posted speed Long trip
limit. Capacity per lane was determined from number of Trip productions: Rates per household based on household
lanes, terrain, and functional class. Forecasts can be made for size, automobile ownership, and area type
a full day or for shorter periods within a day. Trip attractions: Rates per employment categories and
households
It is well known that large zones can lead to lumpy traffic Trip distribution: Gravity expression
assignments. Kentucky's traffic assignment method divided Mode split: Fixed shares for short trip purposes
TAZs into smaller subzones in order to improve the smooth- Multinomial logit for long trip purpose
ness of the results. Subzones were built around highway Assignment: Static equilibrium with feedback to distribution
routes within zones with the number of trips allocated to a Delay estimation: BPR travel time volume curves
subzone being in proportion to the mileage of each route. For Truck models: Commodity based for freight trucks;
some trip purposes the mileage was weighted such that routes empirical for non-freight trucks
of higher functional classes got more trips. Major data: Census, NHTS, CTPP, own surveys
Time frame: Seven years of continuous improvement
Validation results were not available at the time of this following 3 years of initial development
writing. Sources for this case study were: Kentucky response Computation time: 2 h
to Peer Exchange questionnaire (2004), Kentucky response In-house staff: 0.5 FTE
to Synthesis questionnaire (Feb. 2005), and Wilbur Smith
Associates (2005a).
The ISTDM covers all 92 counties in Indiana and parts of
adjacent states. A detailed network was developed for areas
CASE STUDY 2: INDIANA PASSENGER
COMPONENT within the state of Indiana, including all state jurisdictional
highways (more than 19,500 links) and additional local
The Indiana Statewide Travel Demand Model (ISTDM) streets (more than 11,500 links). A less detailed network was
(Bernardin, Lochmueller & Associates, Inc. and Cambridge used for areas outside Indiana, as shown in Figure 9. Data
Systematics, Inc. 2004) was developed principally to assist from INDOT's updated Road Inventory Data (RID 2000)
corridor-level economic development studies. ISTDM was re- were incorporated into the network including number of
cently expanded from a more localized model for the 26-county lanes, shoulders, medians, access control types, traffic and
I-69 study area in southwestern Indiana. The local network was truck count data, and functional classifications.
broadened to include the entire state, the TAZ structure was re-
fined, traffic signals were integrated into the network, and new A total of 4,720 TAZs were created with external stations
procedures for estimating free-flow speed and roadway capac- representing the areas in neighboring states (Figure 10). The
ities were developed. The model structure for the passenger TAZ structure was developed to generally conform to the
component was similar to that of a four-step UTP model. roadway network and previously developed TAZs from the
CTPP. New zones were created by subdividing CTPP zones.
Indiana Statewide Passenger Component Summary More than 10,000 centroid connectors (a maximum of three
State population: 6.2 million per zone) were added to the network using a fully automated
State area: 36,420 square miles process.
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locate state jurisdictional highway signals (gray dots in Fig-
ure 11), and the INDOT's crash database for 1997 through
1999 was used to locate signals on local streets (black dots in
Figure 11). Therefore, signals on local roads without a crash
were missing from the ISTDM network.
A new procedure was developed to estimate free-flow
speed based on detailed geometric features and functional
types of the roadway. The data were obtained from the RID
2000 and the original I-69 speed survey database. Nonlinear
regression analysis was conducted to define free-flow speed
based on posted speed for each unique facility type (number
of lanes, divided/undivided, area type, and access control
type). Figure 12 gives the formulas developed for major fa-
cility types.
Highway Capacity Manual 2000 (HCM 2000) procedures
were followed to calculate speed reduction factors based on
the limiting factors from HCM 2000. The speed reduction
factors were applied to estimate peak-hour roadway capaci-
ties. Daily capacities were then obtained by factoring the
hourly capacities with the inverse of time-of-day factors (i.e.,
the percentages of daily traffic in the peak hour). Figure 13
FIGURE 9 Indiana Statewide Travel Demand Model network. gives an example of curve-fitted capacity adjustment factors
for lateral clearance. A similar procedure was used for all
Traffic signals in the entire state were located on the net- capacity-reduction factors.
work. Signal information integrated to the network includes
signal location, approach priority, and number of upstream
signals. Almost 3,900 traffic signals were located on the net-
work. INDOT's traffic signal data from 1997 was used to
ISTDMnet
INDOT Inventory
New Signals from Crash Data
FIGURE 10 Indiana Statewide Travel Demand Model ISTDM FIGURE 11 Traffic signals in Indiana Statewide Travel
TAZ structure. Demand Model network.
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Area 1,2
Free-Flow Speed Condition Note
Type
2-lane 2-way undivided highways
2
Rural 0.009751 · PSPD + 30.03397 25 PSPD 55
25 PSPD < 25 No or
117.640917 · PSPD
0.0015+0.001279·PSPD
98.065483 25 PSPD 55 Partial
Suburban
25 PSPD < 25 Access
6.189 + 0.9437 · PSPD 25 PSPD 55 Control
Urban
25 PSPD < 25
2-lane 2-way divided highways
2 1
(0.000017 · (PSPD 72.323105) + 0.019702)
25 PSPD 55
Rural + 19.835323
25 PSPD < 25 No
0.857638 41.803252 / PSPD
3.180682·PSPD 84.105587 · e 25 PSPD 55 Access
Suburban Control
25 PSPD < 25
1
(0.119687 0.023365 · ln(PSPD)) + 0.373821 · PSPD 25 PSPD 55
Urban
25 PSPD < 25
Multilane undivided highways
2 1
(0.000017·(PSPD 72.323105) + 0.019702)
25 PSPD 65
Rural + 19.835323
25 PSPD < 25
3.180682 · PSPD
0.857638
84.105587 · e
41.803252 / PSPD
25 PSPD 55
Suburban
25 PSPD < 25
1
Urban
(0.119687 0.023365 · ln(PSPD)) + 0.373821 · PSPD 25 PSPD 55
25 PSPD < 25
Multilane divided highways
2 3
2.836165 · PSPD 0.071256 · PSPD + 0.000744 · PSPD 25 PSPD 50
Rural 16.0359 + 0.8223 · PSPD 50 PSPD 65
25 PSPD < 25
2 1
No or
(0.000071 · (PSPD 64.166165) + 0.035258) Partial
Suburban 25 PSPD 55
+ 9.061039 · ln(PSPD) Access
Control
25 PSPD < 25
1
Urban
(0.081714 0.016217 · ln(PSPD)) 25 PSPD 55
25 PSPD < 25
Full acess controlled highways
64.00 PSPD = 55
67.06 PSPD = 60
70.21 PSPD = 65
73.30 PSPD = 70
1 2
Note: Free-flow speeds in mph. PSPD: Posted speeds in mph
FIGURE 12 Estimation formulas for free-flow speed.
Subsequently, the free-flow speed and roadway capacities size and automobile ownership was used for trip production
were adjusted to account for signal delays by a process that estimation. Trip attractions were related to employment cate-
first estimates control delays, d, at signals using a simplified gories and number of households. Attraction trip rates as de-
version of the HCM 2000 uniform delay term: rived from linear regression are shown in Table 6. Year 2000
Census household data, the 1995 Indiana Travel Survey, and
C g 2 2001 NHTS data were used for model development. The Cor-
d= 1 - * PF
2 C ridor 18 Model dataset was adopted for external long purpose
trips. Stratification curves were developed to breakout the
where C is the cycle length, g is the green time, and PF is the households into categorical groupings to apply the cross-clas-
progression factor. The delay is then used in an empirical for- sification trip rates. The curves were calibrated using the
mula to create capacity-reduction factors for links with signals. CTPP TAZ level data. Figure 14 presents an example of the
stratification curves.
ISTDM trip generation models were developed for four
trip purposes (home-based work, home-based other, non- Gravity expressions were used for ISTDM trip distribu-
home-based, and long purpose) and for three area types (ur- tion. The friction factors were calibrated by trip purposes us-
ban, suburban, and rural). Cross classification of household ing the 1995 Indiana Household Survey and the 20012
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1.0000
0.9900
0.9800
Adjustment Factor
0.9700
0.9600
0.9500
0.9400
0.9300
0.9200
0.9100 0 ft
2 ft
0.9000 Lateral
75 72.5 4 ft Clearance
70 67.5
65 62.5 6 ft
60 57.5
Free-Flow Speed 55
FIGURE 13 Capacity-reduction factors for lateral clearance for two-lane
freeways.
NHTS dataset (see Figures 15 and 16). Socioeconomic ad- "Freight and non-freight trucks were estimated separately.
justment factors (k-factors) were also validated to adjust trip For freight trucks, base year 1993 truck trip tables from the
distributions not explained by friction factors. ISTDM im- Indiana University study were factored up to year 2000 lev-
plemented a single feedback loop of congested times to the els by commodity group." Non-freight truck trip tables were
gravity expressions. estimated from truck ground counts after first removing
freight trucks.
Fixed-mode shares for home-based work, home-based
other, and nonhome-based trips by area types (urban, subur- The ISTDM used a multiclass assignment approach for
ban, and rural) were calculated from the 1995 Indiana House- traffic assignment, with truck trips and automobile trips
hold Survey and the 2001 NHTS data. Automobile occupancy loaded to the network at the same time. Two trip tables were
rates were also obtained from the 1995 survey. For the long developed for truck trips: freight truck trips and non-freight
trip purpose, a multinomial logit expression was adapted from truck trips. The traffic assignment procedure was run twice
the California High Speed Rail Study Model and then recali- by including a feedback loop to trip distribution so that the
brated for the ISTDM for a division of trips between automo- gravity expression could use travel times based on the ini-
bile and intercity bus/rail hybrid. Table 7 shows the calibrated tially assigned roadway volumes. BPR travel time and vol-
model parameters. ume curves were specified by functional classification.
TABLE 6
TRIP ATTRACTION RATES BY TRIP PURPOSE
Trip Purpose Demographic Category Rate
Home-Based Work Employment in retail, FIRE, education, services, and government sectors 1.400
Employment in non-retail; construction; manufacturing; agriculture, 1.120
forestry, and fisheries; and transportation sectors
Home-Based Other Employment in retail sector 4.850
Employment in FIRE, education, services, and retail sectors 3.200
Employment in education sector 1.750
Households 1.650
Nonhome-Based Employment in retail sector 4.490
Employment in FIRE, education, services, and government sectors 1.130
Employment in non-retail, construction, manufacturing, and transportation 0.380
sectors
Households 0.590
Long Total employment 0.023
Employment in FIRE, education, services, and government sectors 0.090
Employment in agriculture, forestry, and fisheries; mining; construction; 0.030
manufacturing; non-retail; and FIRE sectors
Employment in retail and services sectors 0.020
Notes: FIRE = finance, insurance, and real estate
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1 TABLE 7
0.9 REVALIDATED MULTINOMIAL LOGIT EXPRESSION
0.8
PARAMETERS (long trip purpose)
Percent Distribution
0.7 Original Adjusted
0.6 Variable Values Values
0.5 Cost ($) 0.0276 0.0276
H1 IVTT--Line Haul Travel Time (min) 0.0069 0.0069
0.4
HH2 OVTT--Access/Egress Time (min) 0.0083 0.0083
0.3 Bias Constant 0.87 1.15
0.2 HH3
HH4
0.1
0 adjusting volumedelay functions, and modifying centroid
1 1.2 1.4 1.6 1.8 2 2 .22 2 .4 2 3 3 .6 3 .8 .2 .4 3 .6 3 .8 4 connectors.
Average Persons per Household
Overall, the ISTDM shows basecase forecasted volume
FIGURE 14 Household size stratification curves. (Source:
Bernardin, Lochmueller & Associates, Inc. and Cambridge as being close to actual volumes, as shown in Figure 17. The
Systematics, Inc., 2004 and Indiana response to Peer RMSEs in Figure 17 are similar to what might be seen in an
Exchange questionnaire, Longboat Key, Florida, September urban model. The systemwide RMSE is 39.45%.
2004.) H1 = one-person household; HH2 = two-person
household; HH3 = three-person household; HH4 = four-person The ISTDM also includes a post-processor that uses the
household.
output of the travel model to estimate speeds, levels of ser-
vice, crashes, and other measures of effectiveness.
50000
45000
40000 HBW The ISTDM paid particular attention to its socioeconomic
Friction Factor
HBO
35000 forecasts, which underlie the traffic forecasts. Zonal popula-
30000 tion forecasts were developed by first establishing county
25000 NHB
20000 control totals and then distributing the totals to TAZs using
15000 an accessibility-based regression model. Historical data from
10000
5000 Woods & Poole economics forecasts (April 2004), Indiana
0 State Data Center forecasts by county, and the Regional Eco-
nomics Model, Inc. (REMI) forecast for the state of Indiana
3
7
11
15
19
23
27
31
35
39
43
Trip Length in Minutes were examined to produce county-level population. Inde-
pendent variables in the regression model included:
FIGURE 15 Short trip friction factors. (Source: Indiana
response to synthesis questionnaire February 2005.)
HBW = home-based work; HBO = home-based other; · Total population,
NHB = nonhome-based. · Total households,
· Population density,
The ISTDM model was validated by comparing the base · Population under age 17,
year observed daily traffic counts to the model estimates. · Percent of households with head of household over age 65,
Statistics used for validation included: percent RMSE, · Household workers,
systemwide average error, mean loading errors, and total · Average household income,
VMT errors. Once possible sources of model errors were · Accessibility to wealth (by place of residence),
identified, the components were revaluated and corrected. · Accessibility to unoccupied housing units,
Adaptations included modifying trip production rates, ad- · Accessibility to schools,
justing friction factors or k-factors in the gravity expression, · Accessibility to university enrollment,
120
700000000
600000000 100
Percent RMS Error
Friction Factor
500000000
80
400000000
300000000 60
200000000 40
100000000
20
0
50
62
74
86
98
110
122
134
146
158
170
182
194
0
0 20,000 40,000 60,000 80,000 100,000 120,000
Trip Length in Miles
Average Volume in Range
FIGURE 16 Long trip friction factors. (Source: HBA
Specto Incorporated and Parsons Brinckerhoff Ohio FIGURE 17 Validation accuracy for the Indiana model.
2005.). (Source: Hunt and Abraham 2003.)
