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35 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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36 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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37 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+0.001279PSPD 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 3.180682PSPD 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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38 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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39 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.)