APPENDIX 4-A:

RTC 2004 Regional Travel Demand Model Package 2 for 2009-2030 RTP

Travel Demand Model

Transit Processing and Mode Choice Modeling Capabilities

Prepared for:

Regional Transportation Commission of Southern Nevada

2009-2030 RTP Travel Demand Model - RTC 2004 Regional Travel Demand Model Package 2

Transit Processing and Mode Choice Modeling Capabilities - Basic Procedures Introduction The RTC 2004 Regional Travel Demand Model Package 2 A (hereafter short for The RTC 2004 Model Pack 2) will be used for the 2009-2030 RTP. The RTC 2004 Model Pack 2 adds transit processing and mode choice modeling capabilities to the RTC 2004 Regional Travel Demand Model (hereafter short for The RTC 2004 Model) that was used in the 2006-2030 RTP. The RTC 2004 Model represents an evolution of travel forecasting models and model components specifically developed for the Las Vegas Valley portion of Clark County. Model components for trip generation, trip distribution, and auto occupancy models were re-estimated based on the 1996 regional travel survey. In addition, 2000 Census data were used to update socioeconomic sub-models for trip generation and to re-expand the 1996 travel survey data. (for model details, refer to RTC 2004 Regional Travel Demand Model). The RTC 2004 Model has been updated to the RTC 2004 Model Pack 2 by including additional and optional transit processing and mode Choice modeling capabilities. This document provides a description of the basic procedures included in the RTC 2004 Model Pack 2: • Transit network coding procedures • Transit path-building and skimming procedures • Mode choice procedures • Transit assignment procedures The simplified mode split procedure in the RTC 2004 Model continues to be used for forecasts not requiring detailed mode choice processing. Results from the detailed procedures described in this document can be aggregated and summarized at the district level to provide input for the simplified mode split procedures. If the proposed changes are sufficiently large (e.g. the widening of an interstate freeway), it might be reasonable to use the simplified mode split for initial planning forecasts and then use the detailed transit modeling and mode choice procedures described in this chapter for final alternative testing. Transit Network Description For the base year or currently, the Citizens Area Transit (CAT) provided public transportation services for the Las Vegas Valley. Fixed-route local bus service provide the bulk of the existing transit system with limited stop service and express service

being. Most bus routes provided 30 minute headways throughout the day; several routes offered 24-hour service but with 45 minute or 60 minute headways during the “owl” service. In 2004, the monorail serving the Las Vegas Strip and the MAX bus rapid transit (BRT) demonstration project on North Las Vegas Boulevard were opened. The transit routes, including local bus, limited bus, express bus and premium services for the base year and future years were coded into the TransCAD network for the model. The following Figure1 shows the transit system that existed in 2002-2003 as an example.

Figure 1: 2002-2003 CAT Bus Routes Para-transit services are also provided in the region. CAT provides a several fixed routes that serve retirement communities, shopping areas, and establishments which provide services to seniors. The routes offer limited services two days each week. These routes are not considered part of the modeled transportation system. While all current transit services provide relatively balanced services throughout the day, both peak and off-peak transit networks are coded for the modeling process. This approach provides the capability to analyze future alternatives that vary service by time-of-day.

Transit speeds have been coded to reflect the schedule nature of transit service but also taking into consideration prevailing roadway speeds for mixed flow operations. Scheduled time points for the transit service were recorded on the coded transit network for the morning peak, afternoon peak, and off-peak periods. The travel times on links between time points were estimated by prorating the scheduled travel times over those links on a route-by-route basis. The resulting average speeds on links were summarized by facility type, area type, and time-of-day for each route. Modeled congested speeds are updated within the modeling process by running several iterations of the entire modeling process until final assigned roadway speeds reasonably match the roadway network speeds used for trip distribution and mode choice. Transit speeds for premium transit services will be estimated for each alternative based on the operating characteristics of the alternative, station spacing, and station dwell times. In general, transit speeds for premium transit services will be unaffected by changes in modeled congested speeds on roadways. Transit Access Coding Walk Access Substantial effort was expended to determine appropriate walk access coding conventions for the RTC 2004 Pack 2. Since walk access distance data were not recorded for the 2002 on-board survey, the “best” walk access coding distance was determined through multiple assignments of the observed trip tables derived from the 2002 survey data to the 2002 network. The resulting modeled boardings by line were compared to observed boardings by line. This effort was part of the determination of transit path-building parameters. The best results were obtained when walk access was coded to all transit within 1.25 miles of a TAZ. Walk access distances were varied by trip purpose and income group being modeled. The variation in walk access distances reflect the fact that lower income groups are more likely to be captive and, thus, more likely to walk longer distances to access transit than higher income groups. About 80 percent of the linked transit trips summarized from the 2002 CAT on-board survey were made by travelers reporting low or lower-middle incomes. The walk access distances also reflect that residents do not walk long distances to transit for non-home-based trips and, likewise, that visitors do not walk long distances to transit. Two different transit networks (peak and off-peak) are each used for path-building. Value-of-time is also varied for the path-building process. Two different value-of-time assumptions are used for the visitor paths. In total, five different sets of walk access to transit skims are built. Table 1 shows the walk distances for transit path-building for different trip purposes and different income groups.

Table 1: Walk Distances for Transit Path-Building

Trip Purpose Income Group Network Used

Maximum Walk

Access Distance (Miles)

Maximum Walk

Transfer Distance (Miles)

Walk Speed (MPH)

Home-Based Work Low & Lower Middle Peak 1.25 0.25 3 Home-Based Work Upper-Middle & High Peak 0.5 0.25 3 Home-Based Non-Work All Off-Peak 0.5 0.25 3 Non-Home-Based All Off-Peak 0.5 0.25 3 All Visitor All Off-Peak 0.5 0.25 3 Walk access is allowed to occur over the coded roadway network provided the network link is not part of a limited access facility (interstate, freeway, expressway, system ramp or ramp). In all cases, transfer walk times are limited to one-quarter mile (five minutes). All walk access and walk transfer are assumed to occur at a three mile per hour average walking speed. Drive Access While there is very little formal park-and-ride facilities and existing auto access to transit in the Valley, park-and-ride might be a very important component of future alternatives. The recently opened (2003/2004) South Strip Transit Terminal provides parking along with facilitating transfers between CAT fixed routes and bus lines. For future alternatives, drive access will be allowed to premium transit stations with proposed formal park-and-ride lots. Bicycle, drop off and other access will also be considered as “walk” access. Transit Path-Building Impedances for path-building consider fares and weighted travel times. Since relationships between fares, in-vehicle time, wait-time, and walk time vary by trip purpose (and income group for home-based work trips), maintaining complete consistency between path-building and mode choice parameters would require runs of transit path-builder for trip purpose and income group for which the mode choice model has been calibrated. This would imply building 13 different sets of transit paths for walk access to bus, alone. For the RTC 2004 Pack 2, some simplification has been assumed so that only five sets of walk access to bus transit paths need to be built. Since transit path-building considers transit fares paid, the current CAT system fare policy must be understood. That fare policy (as obtained from the RTC web-site) is as follows:

Full Fare, Reduced Fare, Full and Reduced Passes, and Transfers. Based on an analysis of 2003 revenue and boarding information, the average fare for local bus service taking into account of discounts and passes was calculated; the average fare paid for express buses were calculated too. The modeled fares are

probably more consistent with the auto operating costs represented to the model. The “full fare” method has been used for path-building and modeling purposes. For the mode choice model application, fares are expressed in 1995 dollars. Value-of-time can be determined from the mode choice model. Again, for simplification, values-of-time have been combined for a number of trip purposes. All weighted time values were converted to monetary terms and combined with fares for path-building purposes. Improved model results were obtained if the added transfer penalties were also included in the mode choice models. Through calibrations, an added transfer penalty of seven minutes per transfer was found to produce reasonable results. The path-building weights will be used for transit path-building and transit assignment along with the trip purposes that will be modeled using each set of transit impedances. The transit assignments show that there is a high correlation between the modeled boardings by line and the observed boardings by line. See Table 2 as an example. Table 2: 2002 Transit Assignment Summary Measure Observed (Summarized from

2002 CAT On-Board Survey) Assigned Observed

Trip Tables Number of Linked Trips 109,282 109,282 Total Boardings 142,744 150,360 Boardings per Linked Trip 1.31 1.38 Boardings by Type of Service Local Bus 139,836 147,412 Limited Bus 2,320 2,642 Express Bus 588 306 Coefficient of Determination (R2) 0.96 Root Mean Squared Error on Boardings 837 Percent Root Mean Squared Error 26%

Figure 2: Assignment of Observed 2002 Linked Trips vs.

Observed Boardings by Route Mode Choice Resident Trip Purposes Resources were not available for the full development of mode choice models for the Las Vegas Valley. Thus, the decision was made to transfer a model to the region and update the model constants to reproduce observed transit ridership for the region. Several donor models were considered. The Las Vegas Travel Demand Model was prepared by PBQ&D in 2001. This mode choice model was estimated based on the spring 1996 household survey supplemented with data from an on-board transit passenger travel survey performed in October and November 1995. Since the PBQ&D model offers the benefit of being estimated using local data, the use of the PBQ&D mode choice model as the donor model was reasonable. Figure 3 shows the structure of the PBQ&D model. The following changes were made to the PBQ&D model structure shown in Figure 3: • The 2004 RTC Model is a trip-based model that models all trips made in motorized

vehicles. Trips made using non-motorized modes are not modeled. • Premium transit services such as the monorail and MAX BRT system are being

planned and implemented in the Las Vegas region. Some of the service will offer multiple transit options within a corridor. For example, both the monorail and buses provide transit service on the Strip. Thus, a local / premium nest under the walk to transit access mode has been added to the mode choice model. Since park-and-ride lots are normally associated with premium transit services offered to outlying

areas, there is little need to provide a local / premium nest under the drive access mode.

Figure 3: PBQ&D Mode Choice Model Structure

The analysis of toll roads is an increasing requirement in many regions, especially in fast growing areas like Las Vegas. A nesting structure for the testing of toll road alternatives could be added to the model under the drive alone, shared ride 2, and share ride 3+ nests. The toll road nest would need to be adapted from work performed elsewhere (e.g. Southern California). Since the average wage rates for each income group were known, the coefficients of cost for the home-based work mode choice model could be varied by income group to be more consistent with FTA guidelines. It’s interesting to note that the $6.12 per hour rate is within the FTA guidelines. Several changes were made to the original PBQ&D mode choice models for consistency with FTA guidelines or to enhance consistency with transit path-building procedures. Figure 4 shows the general mode choice model structure used for the 2004 RTC Park 2. The structure shown in Figure 4 is used for all resident trip purposes.

Figure 4 : 2004 RTC Mode Choice Model Structure Visitor Trip Purposes The PBQ&D visitor models were adapted for use with the 2004 RTC Pack 2 with Mode Choice Analysis. The mode choice model structures and parameters for the three general types of visitor models were transferred for the 2004 RTC Pack 2. The generic multinomial mode choice structures for the three types of visitor models (multi-day visitors, single day visitors, and airport related trips) are shown in Figure 5 and Figure 6. Unlike, the models for residents of the region, the visitor models forecast all person trips, not just person trips made in motorized vehicles. Thus, walk skims are required for the mode choice model implementation. The walk skims are built over the coded roadway network links except freeway or ramp links. Several other “exotic” modes are included in the visitor mode choice models: taxi, shuttle bus, and tour bus. And, private auto must consider both autos privately owned by visitors and those rented by visitors.

Figure 5: 2004 RTC Pack 2 Mode Choice Model Structure – Multi-day & Single

Day Visitor Trips

Figure 6: 2004 RTC Pack 2 Mode Choice Model Structure – Airport Trips

The originally transferred visitor models included 12 trip purposes. Several of the trip purposes were combined for mode choice modeling purposes. However, efforts were made to combine only those purposes that were relatively similar both in terms of the type of trip being modeled and in terms of model coefficients. Table 3 shows the original trip purposes and the combined purposes. As with the resident-based mode choice models, a coefficient on the number of transfers was added to the visitor and airport models. The coefficient for each transfer was set to be equivalent to seven minutes of in-vehicle travel time. Mode choice model parameters and mode specific constants for the visitor and airport models are shown in Table 4. Note that the taxi base fare is fixed and could effectively be incorporated into the taxi mode constant. However, incorporating the fixed values as input constants allows for the consideration of policy changes such as a cutting the frequency of shuttle service if fixed guideway transit to the airport is provided or changing the “drop fare” for taxi service. Table 3: Combination of Visitor Trip Purposes for Mode Choice Original Trip Purpose Combined Trip Purpose Hotel-Based Convention Hotel-Based Business Hotel-Based Convention / Business

Hotel-Based Gaming Hotel-Based Gaming Hotel-Based Other Hotel-Based Other Non-Hotel-Based Gaming Non-Hotel-Based Other Single-Day Non-Airport-Based Business Single-Day Non-Airport-Based Other

Non-Hotel-Based

Resident Airport Resident Airport Visitor Airport Airport-Based Business Airport-Based Other

Vistor Airport

Two distinct types of visitor travel or travelers can be identified based on the values-of-time: travelers with very high values-of-time and travelers with “normal” values-of-time. The low value-of-time suggests that the trips are made at a more leisurely pace with travelers being less willing to substitute higher cost and fast modes for lower cost and slower mode. The airport trips are also associated with low values-of-time for several

reasons, such as the value-of-time for airport trips is in the range for noted for resident-based trips. Mode Choice and Transit Assignment Validation Results Table 4 summarizes the observed and modeled linked transit trips and transit mode shares by trip purpose. The observed linked transit trips by purpose were estimated from the expanded 2001/2002 on-board transit travel survey data. Table 4 simply shows that the calibration process was successful in reproducing the observed targets. Table 5 summarizes the observed and modeled boardings by the path-building techniques defined previously. The modeled boardings per linked trip are higher than the observed boardings per linked trip. As discussed in the section describing path-building parameters, the boardings per linked trip could be reduced by increasing the maximum walk access distances. When new on-board survey data are collected for the Las Vegas region, a concerted effort should be made to obtain good data on walk access and egress distances since reliable observed data could lead to improvements to the modeling process. Table 4: 2002 Observed and Modeled Transit Trips by Purpose

Trips Mode Shares Trip Purpose Observed Modeled Observed Modeled

Home-Based Work Low Income 17,920 17,896 29.8% 29.8% Lower Middle Income 12,377 12,376 16.9% 16.9% Upper Middle Income 5,303 5,303 2.9% 2.9% High Income 3,599 3,599 0.8% 0.8% Total Home-Based Work 39,199 39,174 5.2% 5.2% Home-Based School 2,471 2,464 0.7% 0.7% Home-Based Shop 9,304 9,275 2.1% 2.1% Home-Based Other 33,820 33,810 1.8% 1.8% Non-Home-Based 11,558 11,486 0.9% 0.9% Total Resident Trips 96,352 96,209 2.1% 2.1% Hotel-Based Convention / Business 257 257 0.4% 0.4% Hotel-Based Gaming 5,994 5,948 4.1% 4.1% Hotel-Based Other 3,343 3,338 2.2% 2.2% Non-Hotel-Based 3,308 3,302 1.9% 1.9% Airport-Based Resident 0 51 0% 0.4% Airport-Based Visitor 85 85 0.1% 0.1% Total Visitor Trips1 12,987 12,981 2.1% 2.1% Total Trips 109,339 109,190 2.1% 2.1% 1 Includes resident-based airport trips.

Table 5: 2002 Observed and Modeled Transit Boardings by Path Set Boardings Boardings / Trip Trip Purposes Path-Set

Observed Modeled Observed Modeled Home-Based Work Low & Lower Middle Income 1 41,833 51,300 1.39 1.69 Upper Middle & High Income 2 11,673 16,521 1.32 1.86 Home-Based Non-Work1 3 59,331 71,336 1.31 1.57 Non-Home-Based & Visitor – Low Value-of-Time2 4 18,608 23,344 1.25 1.56

Visitor – High Value-of-Time3 5 11,299 13,692 1.19 1.43 Total Boardings 142,744 176,194 1.31 1.61 1 Includes home-based school, home-based shop, and home-based, other trips. 2 Includes hotel-based other, airport-based resident and airport-based visitor trips. 3 Includes hotel-based convention/business, hotel-based gaming, and non-hotel-based

trips.

. Figure 7: Assignment of Modeled 2002 Transit Trips vs. Observed Boardings by Route

Figure 8: Assignment of Modeled 2002 Linked Trips vs. Assignment of Observed 2002 Linked Trips by Route Figures 7 and 8 compare the modeled and observed boardings on a route-by-route basis. Figure 7 compares the assignment of the modeled trip tables with the observed boarding counts from 2002. Figure 8 compares the assignments of the modeled trip tables to the assigned observed trip tables summarized from the 2002 on-board survey. The information in the above tables and the following figures suggest that the modeling process is reasonably reproducing observed travel Table 6 summarizes assignment statistics comparing the observed boardings with the assignment of the observed trip tables summarized from the 2002 on-board survey and the assignment of the modeled trip tables. Table 6: Transit Assignment Summary

Trip Tables Assigned Measure Observed

Boardings Observed Modeled Number of Linked Trips 109,339 109,339 109,190 Total Boardings 142,744 150,360 176,194 Boardings per Linked Trip 1.31 1.38 1.61 Boardings by Type of Service Local Bus 139,836 147,412 170,743 Limited Bus 2,320 2,642 3,878 Express Bus 588 306 1,572

MODELING PREMIUM TRANSIT AND DRIVE ACCESS TO TRANSIT Premium Service Descriptions Two new transit services were initiated in the Las Vegas region in 2004: The MAX service connecting the Downtown Transit Center with Nellis Air Force Base via North Las Vegas Boulevard was opened on June 30, 2004. The Strip Monorail service was opened between the MGM Grand Hotel and Casino on Tropicana and the Sahara Hotel and Casino on July 15, 2004. These two new services provided a unique opportunity to test the ability of the transit processing procedures and calibrated mode choice models to properly model new transit services. Premium Service Calibration Process Since detailed transit trip purpose or origin-destination data were not available for the services, the specification of the correct nesting structure and alternative specific constant was developed by running the following tests and comparing the resulting boardings to observed boardings for the late-2004 through early-2005 time period: • Both services were coded and modeled as local transit services with no special

treatment other than correctly specifying speeds, headways, and fares. • Both services were coded as above but were modeled as premium transit services,

including separate transit path-building and mode choice; the “local service” alternative specific constants were used.

• Both services were coded as above but were modeled as premium transit services, including separate transit path-building and mode choice; “premium service” alternative specific constants were specified via trial and error to best match boardings.

Premium Service Path-Building and Modeling Procedures The same basic path-building parameters for local transit paths were used. Fare weights for local bus use were used when premium paths were built. Fares for local buses were set as $5.00 boarding fares with additional $5.00 transfer fares between local buses. In contrast, boarding fares for premium transit modes were set to the proper values (i.e. $0.99 in 1995 dollars for the MAX and $2.39 in 1995 dollars for the monorail). Transfers to or from premium transit routes were free. Premium transit fares were also adjusted after the premium path-building to remove the artificially high local bus boarding and transfer fees. Also, the skims from the premium-only paths are compared to the skims using premium and local service and the “best” impedances are used for mode choice.

Premium Service Calibration Results The modeling of MAX and monorail as premium transit services was determined to be the desirable approach. A number of iterations of the calibration process were made with different values for the premium alternative specific constants and with the path-building procedures. A reasonable calibration was obtained for the MAX boardings with no difference between the values of the constants used for local and premium service. The alternative specific constants for walk to local and walk to premium transit show that there is no difference between the local and premium constants for each trip purpose. Figure 9 and Table 7 compare the modeled and observed boardings on a route-by-route basis for 2004/2005. The validation to the 2004/2005 provides two benefits: • Validation of the premium transit modeling • Validation for a year other than the year used for model calibration Figure 9 can be compared to Figure 7 for a qualitative comparison of the match between modeled and observed transit boardings by route for the original 2002 and the 2004/2005 validations. With the exception of the underestimation of the Monorail, the two figures show similar patterns for the modeled versus observed trips.

Figure 9: Assignment of Modeled 2004/2005 Transit Trips vs. Observed Boardings by Route

The match between the modeled and observed ridership in two corridors is important in the determination of the validity of the models. Specifically, the reproduction of the transit ridership in the North Las Vegas Boulevard corridor demonstrates the ability of the model to estimate ridership on premium services while the reproduction of transit ridership in the Strip Corridor is crucial in showing the reproduction of ridership resulting from the visitor market. In total, observed transit ridership in the corridor is quite reasonably reproduced. In December 2005, ridership on the Deuce was just under 50,000 boardings per day and stabilized around 35,000 to 40,000 per day in early 2006. The ridership on the Deuce demonstrates the volatility of transit ridership in the Strip corridor. The model results suggest the need for a premium transit alternative specific constant representing substantial travel time savings. The results obtained for the MAX, the Strip Corridor, and the Monorail were all considered in making decisions regarding the final “validated” model for the region. While the MAX ridership was overestimated with no equivalent travel time savings for premium transit, ridership on the Monorail was substantially underestimated. It is very unlikely that Monorail will be considered in any of the alternatives to be considered for New Starts so the estimation of Monorail ridership becomes less important. Table 7 summarizes assignment statistics comparing the observed and modeled boardings. Results from the 2002 validation are also shown for a direct comparison of the model validation results for the two years. Table 8 summarizes the modeled transit trips and transit mode shares for 2002 and 2004/2005. The results shown in Table 8 coupled with the validation results summarized in Table 7 suggest that the model is reasonably sensitive to changes in socioeconomic characteristics and transportation supply.

Table 7: 2004/2005 Transit Assignment Summary 2004/5 Results 2002 Results3

Measure Observed Modeled Observed Modeled

Number of Linked Trips1,2 112,791 112,791 109,339 109,190 Total Boardings1,2 154,902 183,309 142,744 176,194 Boardings per Linked Trip1,2 1.37 1.62 1.31 1.61 Boardings by Type of Service Local Bus 142,503 170,676 139,836 170,743 Limited Bus 6,139 5,443 2,320 3,878 Express Bus 600 1,597 588 1,572 MAX 5,660 5,593 – – Strip Monorail 22,000 2,285 – – Coefficient of Determination (R2) 2 0.89 0.96 Root Mean Squared Error on Boardings2 2,170 1,671

Percent Root Mean Squared Error2 62% 50% 1 Actual number of linked transit trips for 2004/2005 is unknown. The modeled number of

transit trips was assumed to be correct. 2 The value excludes 2,285 assumed monorail trips (one trip per Monorail boarding)

since Monorail ridership was estimated from information included in a press release rather than being based on actual passenger counts. The Monorail Company does not publish actual ridership data.

3 Source: Table 10-18. Table 8: 2002 and 2004/2005 Modeled Transit Trips by Purpose

Modeled Trips Mode Shares Trip Purpose 2002 2004/2005 2002 2004/2005

Home-Based Work Low Income 17,896 18,519 29.8% 28.6% Lower Middle Income 12,376 12,448 16.9% 15.8% Upper Middle Income 5,303 5,026 2.9% 2.5% High Income 3,599 3,271 0.8% 0.7% Total Home-Based Work 39,174 39,264 5.2% 4.9% Home-Based School 2,464 2,477 0.7% 0.6% Home-Based Shop 9,275 9,631 2.1% 1.9% Home-Based Other 33,810 35,886 1.8% 1.7% Non-Home-Based 11,486 11,993 0.9% 0.8% Total Resident Trips 96,209 99,250 2.1% 1.9% Hotel-Based Convention / Business 257 461 0.4% 0.7% Hotel-Based Gaming 5,948 6,925 4.1% 4.2% Hotel-Based Other 3,338 4,116 2.2% 2.4% Non-Hotel-Based 3,302 4,054 1.9% 2.1% Airport-Based Resident 51 108 0.4% 0.8% Airport-Based Visitor 85 163 0.1% 0.2% Total Visitor Trips1 12,981 15,826 2.1% 2.3% Total Trips 109,190 115,076 2.1% 1.9% 1 Includes resident-based airport trips. Drive Access to Transit Since there has been very little use of the facility for park-and-ride access to transit, as for the 2002 calibration year, drive access to transit could not be calibrated based on

2004-2005 model results. Nevertheless, since future transit alternatives might make use of park-and-ride access to attract choice riders, modeling procedures must be specified. The following summarizes modeling assumptions regarding the coding and modeling of drive access to transit: • Drive access will be coded only to formal park-and-ride lots. • Maximum drive access distances will be five miles to all park-and-ride lots except for

end-of-line stations; at end-of-line stations, drive access of up to fifteen miles will be allowed.

• Drive access distances and times will be determined from the coded roadway network.

• For mode choice, drive access cost will be determined using the same cost per mile as for the auto mode.

• Drive access will be modeled for all home-based trip purposes using the nesting structure shown in Figure 10-5; drive access will not be forecast for non-home-based trips.

• Drive access to transit will not be forecast for non-resident trip purposes. • Drive access time will be considered as out-of-vehicle travel time for mode choice;

for the home-based work purposes, the out-of-vehicle time coefficient is 2.0 times the in-vehicle time coefficient and for the home-based non-work trip purposes, the out-of-vehicle time coefficient is 3.0 times the in-vehicle time coefficient.

The drive access to transit alternative specific constants have been estimated. The proposed regional fixed guideway system, including park-and-ride lots, was coded for the 2005 network. Two sets of forecasts were made: • A forecast allowing only walk access to transit was performed to provide estimates

of transit ridership without park-and-ride. • A second set of forecasts allowing park-and-ride was performed. Park-and-ride

constants were varied so that approximately 15 to 30 percent of the total premium transit ridership for each purpose was by auto access.

Table 9 shows the target percentages of auto access by trip purpose along with the “calibrated” constants to achieve those percentages in the 2005 sensitivity test. The percentages shown in Table 9 should result in about 25 percent of all premium transit home-based work trips and about 15 percent of all premium transit home-based non-work trips being made by auto access. In comparison, in the Denver region which has a very well developed park-and-ride system, approximately 45 percent of the total systemwide home-based work trips are made using auto access and about 30 percent of the home-based non-work trips are made by auto access.

Table 9: Target Drive Access Percentages by Trip Purpose

Trip Purpose Target Drive

Access Percent

“Calibrated” Constant

Equivalent Minutes of In-Vehicle Travel Time (Compared to Walk

Access to Transit) Home-Based Work Low Income 15% -0.65066 -104.4 Lower Middle Income 20% -1.15773 -97.7 Upper Middle Income 25% -2.55443 -89.6 High Income 30% -3.38720 -85.1 Home-Based School 10% -4.05754 -153.1 Home-Based Shop 15% -2.40336 -140.1 Home-Based Other 15% -2.17282 -127.0 MODELING HIGH-OCCUPANCY VEHICLE LANES Modeling Issues By design, the mode choice model includes the capability to estimate shared ride auto use by group size. Model constants were calibrated to reproduce observed two-person and three or more person shared ride auto use by trip purpose for 2002. The estimates of the observed two-person and three or more person shared ride auto use were based on the 1996 household survey data. Future highway alternatives might include the provision of high-occupancy vehicle (HOV) lanes. Experience elsewhere has shown that HOV lane users value the travel time saved through the use of HOV lanes more highly than the simple difference between travel time on the HOV lanes and on general purpose lanes. If HOV lane traffic volumes increase to the point where HOV lanes are experiencing congestion, modification of the minimum carpool size allowed to use the HOV lanes can be made to reduce the HOV lane delay caused by congestion. The original mode choice model included coefficients for HOV travel time savings. The HOV time savings variable, as specified, is applied to any HOV time savings in excess of five minutes. But the coefficients could not be rigorously calibrated since HOV lanes were non-existent in the region in 1996 (when the data were collected). As a result, the model coefficients were specified based on models developed in other regions. The problem with forecasting HOV lane use is similar to that encountered with forecasting drive access to transit. The FTA suggested that sensitivity testing of results is important whenever drive access to transit is modeled. The same advice is applicable to the forecasting of HOV lane use. Table 10 summarizes the results of such testing for the region. The travel model was used to forecast HOV shares and HOV lane use for 2030 for the following scenarios: • A no-build scenario where no HOV lanes were assumed to exist in the region. • The build scenario where an extensive HOV lane system was implemented for the

region.

• The build scenario was modeled as above with the exception that the HOV time savings coefficient was applied to all increments of time saved, not just the increment in excess of five minutes. This was the most generous modeling approach for forecasting HOV travel.

• The build scenario was modeled as above with the exception that the HOV time savings coefficient not used. This was the most restrictive modeling approach for forecasting HOV travel.

The results summarized in Table 10 provide an idea of the sensitivity of the travel model to the HOV time savings coefficient. Since the coefficient cannot be rigorously calibrated, the sensitivity analysis as outlined above should be considered whenever planning and analysis of HOV lane alternatives is performed. In cases where HOV lanes are assumed to be in existence and an unrelated transit or roadway alternative is being analyzed, the model coefficient can be used as specified in Table 10. Table 10: HOV Time Savings Coefficient Sensitivity Testing TRIPS BY MODE NOBUILD BUILD DIFFERENCE % CHANGE drive alone 5,197,452 5,197,383 (69) 0.0% shared ride 2 3,140,653 3,142,550 1,897 0.1% shared ride 3+ 2,754,531 2,755,983 1,452 0.1% walk to local transit 211,198 208,097 (3,100) -1.5% walk to premium transit 31,348 31,490 142 0.5% drive to transit 4,704 4,382 (322) -6.8% Total 11,339,885 11,339,885 (0) 0% T:\BETH\RTP 09-30\DOC\Model Methodology-Mode Choice Sections 2006-05-05 Pk2_v1.doc

APPENDIX 4- B

NDOT TRAFFIC PROJECTIONS FOR THE RTC MODEL EXTERNAL TAZS

Clark County External AADT's 2000-2030 Source: NDOT Staff

TAZ # Description

2000 AADT (total)

2005 AADT (total)

2006 AADT (total)

2010 AADT (total)

2015 AADT (total)

2020 AADT (total)

2025 AADT (total)

2030 AADT (total)

1 US95, 5.9 mi N of SR-156 (0030374) 5,200 5,450 5,550 6,250 7,000 7,700 8,500 9,300

2 US93, at MP LN-25 S of Alamo (0170001) 1,600 1,500 1,550 1,650 1,750 1,850 2,000 2,200

3 IR15, 2 mi S of Valley of Fire Intch (0030730) 17,000 21,700 22,100 25,400 30,000 34,000 38,000 42,000

4 SR157, .2 mi W of US95 (0030368) 2,750 2,700 2,750 3,500 4,200 4,700 5,800 6,500

5 SR147, .4 mi E of Los Feliz St (0030734) 3,000 2,600 2,750 3,500 4,200 4,700 5,800 6,500

6 SR159, .1 mi E of rd to Red Rock Canyon (0030358) 2,850 4,550 5,300 6,400 8,000 9,800 11,500 13,000

7 SR564, 1.7 mi E of Las Vegas Pkwy (0033200) 3,200 2,900 2,900 3,700 4,400 4,900 6,000 6,500

8 SR160, E of SR159 (0030361) 8,750 9,550 9,750 12,000 15,000 17,000 19,000 21,000

9 US93, .3 mi E of Boulder Beach Hwy (0035220) 12,900 13,000 13,000 18,000 20,000 22,000 23,500 25,000

10 US95, .1 mi S of RxR Pass Intch (0031014) 7,600 12,300 12,700 13,000 16,000 19,000 21,000 24,000

11 IR15, at the NV/CA line (0031110) 35,000 39,500 40,500 45,000 50,000 55,000 60,000 66,000

Source: Regional Transportation Commission Staff