APPENDIX F-D 

Ridership Forecast Review

Desert Xpress Ridership Forecast Review 

Cambridge Systematics, Inc. i 

Desert Xpress Ridership Forecast 

Review

Ridership Forecast Review 

prepared for 

Circle Point 

135 Main St., Ste. 1600 

San Francisco, CA 94105 

prepared by 

Cambridge Systematics, Inc. 

100 Cambridge Park Drive, Suite 400 

Cambridge, Massachusetts 02140 

date 

February 29, 2008 

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Cambridge Systematics, Inc. conducted a review of the demand forecasts that 

have been developed for the proposed Desert Xpress high speed rail service 

between Victorville, CA and Las Vegas, NV. The forecasts were developed, and 

documented, by URS Corporation in 2005.1 In addition to the forecasting report, 

Desert Xpress also provided a number of other data files and memoranda to 

describe the forecasting process. Among the documents provided was a 

summary of another review of the forecasts conducted by Steer Davies Gleave 

earlier this year.2

To evaluate the previous forecasting efforts, we reviewed the provided 

documents and used available at-hand information to validate the analyses. It 

should be noted here, first, that the general forecasting methods that have been 

used appear to us to be quite reasonable and represent very good demand 

forecasting practices as we understand them. 

However, all demand forecasting efforts must contend to some extent with 

imperfect data, and all require analysts to make assumptions that others might 

find inaccurate or might question. Thus, it should not be surprising that our 

analysis (like the previous forecast review) raises a number of issues with the 

details of the forecasting effort that could affect the ridership forecasts in both 

material and minor ways. 

The forecast review is summarized in the sections below. We begin with a short 

review of the overall mode choice forecasting approach that was used. We then 

discuss the development of the overall travel market estimates. We continue 

with a discussion of the mode choice model development effort and the input 

assumptions used in the forecasts. Finally, we discuss the induced demand 

analysis that was conducted in the forecasting process. 

In each section, for the sake of context, we provide a very brief description of the 

URS forecasting methodology. Then we provide our review of the forecasting 

process as we understand it. This memo does not provide an updated forecast 

based on our suggested revisions, but where possible we identify the likely effect 

of changing the forecasting approach in the recommended way. 

Mode Choice Modeling Approach 

URS Methods

Desert Xpress ridership estimates were developed by determining the overall air, 

auto, and bus travel markets between Southern California and Las Vegas, and 

then by separately modeling the diversion of ridership from each mode to the 

 

1 URS Corporation, Desert Xpress Updated Ridership and Revenue Study: Draft Final Report

(December 22, 2005). Attached as Appendix B. 

2 Steer Davies Gleave, Desert Xpress Ridership and Revenue Audit: Technical Memorandum—

FRA Summary (September 2007). Attached as Appendix C. 

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Desert Xpress with binary mode choice models. Finally, additional analyses 

were conducted to assess induced demand for the Desert Xpress service. 

Our Review

We have used the binary modeling approach previously for forecasting high 

speed rail ridership, and have found it to be a reasonable approach for 

forecasting the demand for a new service and for accounting for the forecasts. 

However, in these previous efforts with this approach, several valid issues with 

the method have been raised: 

• The binary diversion approach can increase the difficulty of evaluating levelof-service changes to existing modes, because it will not capture shifts 

between the existing options. This could materially affect (positively or 

negatively) the rail forecasts when the rail mode is forecast to compete with 

the existing modes to different degrees. 

• The binary mode choice models rely solely on stated preference modeling 

methods. Revealed preference data on actual travel choices are used only in 

total market estimation and perhaps for model validation. Revealed 

preference data are freer of survey response biases than stated preference 

data, so having the revealed preference data in model estimation can 

improve the validity of the resulting models. 

• The binary diversion approach makes it more difficult to develop analytic 

estimates of induced demand based on the mode choice models, because the 

different binary models are not necessarily consistent with each other. 

These limitations do not invalidate the modeling approach, but should be 

considered when evaluating the model-generated forecasts. The alternative 

approach to the binary modeling technique is the development of a multinomial 

model (or preferably a nested model) that includes both the full set of existing 

modes and the new mode. 

It is difficult to assess the validity of the forecasts in aggregate by comparing 

them to other similar efforts, because new high speed rail systems (outside of 

Amtrak improvements) have not been implemented in the U.S. and because 

intercity corridors tend to be unique. Certainly, there are no corridors that are 

comparable to the Las Vegas-Southern California corridor, in terms of the 

reasons people travel and the geography of Las Vegas. However, it may be 

useful to compare the predicted mode shares of Desert Xpress with demand 

predictions for other intercity corridors. 

URS forecast that Desert Xpress will capture 22 percent of the Southern 

California – Las Vegas travel market (or 24 percent of the market with the higher 

speed EMU alternative), and that the shares diverted from auto, bus, and air 

would be very similar. These shares are based on the input assumptions about 

the relative times and costs of the mode options, so they would vary with 

different assumptions, as would forecasts for other travel corridors. However, 

for the Desert Xpress analysis, as well as other HSR corridor studies, the analyses 

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sought to find the best operating strategies, so there may be some validity, at 

least in a very simplistic way, to compare the mode share estimates of other 

corridors to those of Desert Xpress. 

The table below shows the mode share estimates reported from some recent high 

speed rail market estimation efforts. The initial set of forecasts shown are taken 

from the FRA Commercial Feasibility Study analyses. For these forecasts and the 

specific studies that follow them, the mode shares shown represent the shares of 

all longer distance trips within the corridors shown. Thus, auto trips between 

intermediate locations that could conceivably be captured by the rail services are 

included in the mode share denominators even though there is very little 

likelihood that rail would be considered for these shorter trips. Since Desert 

Xpress will be designed to serve two large trip ends and will capture 

intermediate trips only if it appears commercially feasible, the comparison with 

the systemwide reported shares is probably somewhat biased for the auto market 

shares. 

Forecast High Speed Rail Mode Shares From Some Recent Studies 

Corridor (with HSR Top Speed; and Study 

Year) 

Forecast Mode Share 

FRA CFS Corridors

North-South California (150; 1998) 8.6% from air; 4.3% from auto 

Los Angeles-San Diego (150; 1998) 19.8% from air; 0.7% auto 

Chicago Hub (150: 1998) 18.6% from air; 4.3% auto 

Chicago-Detroit (150; 1998) 17.6% from air; 2.8% auto 

Chicago-St. Louis (150; 1998) 22.2% from air; 5.2% auto 

Florida (150; 1998) 8.5% from air; 2.3% from auto 

Pacific Northwest (150; 1998) 32.0% from air; 3.5% auto 

Texas Triangle (150; 1998) 17.9% from air; 5.0% auto 

 

Specific Corridor Studies

California Statewide (250; 2007) 33% air; 6% auto; 27% rail 

Cleveland-Columbus-Cincinnati (150; 2001) 2.0% air; 1.7% auto; 16.2% bus 

Boston-Montreal (110; 2005) 18% from air; 0.2% auto 

Baltimore-Washington (300; 2003) 13% from air; 0.1% auto 

Tampa-Orlando (150; 2003) 12% from auto; minimal air service 

New York- Buffalo (150; 1995) 67% air; 6% auto; 29% rail 

New York – Boston (200; 1996) 50% air; 7% auto; 15% rail 

As noted above, the direct comparison of corridor forecasts is probably not 

completely reasonable. The wide range in forecasts are related to differing 

assumptions about times and costs of the HSR service and the competing modes, 

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as well as specifics of the travel corridors themselves. The URS forecasts of air 

and bus diversion (about 24 percent with the base EMU service) are well within 

the range of the other forecasts. 

The URS forecast diversions from auto of more than 20 percent for the base EMU 

service and DMU service are higher than forecast for the other corridors, but it 

should be noted that there are some peculiarities of the Las Vegas – Southern 

California market and the Desert Xpress service that may support the finding 

that Desert Xpress competes with autos to a greater degree than systems planned 

for the other corridors. In particular, the location of the Victorville terminal 

would be passed by virtually every auto traveler going between Southern 

California and Las Vegas. The vast majority of trip makers from Southern 

California to Las Vegas drive on I-15, which has two lanes in each direction for 

much of the route and which does not (and likely will not in the foreseeable 

future) have any credible parallel alternative highways across the Mojave Desert. 

In addition, the need for an automobile at the Las Vegas destination is lower than 

for other destinations in other corridors. A significant proportion of Las Vegas 

visitors travel only within walking distance of their hotels. A recent survey of 

Las Vegas visitors performed for the Regional Transportation Commission of 

Southern Nevada asked respondents to report all of their travel in Las Vegas 

over the previous 24 hours. Of the visitors staying at hotels in the Strip area, 22 

percent had not left the property where they were staying and 68 percent 

traveled only within the Strip area (n=1008). The same percentages for visitors 

from Southern California were 17 percent and 78 percent (n=214). 

The initial forecasts developed by URS were based on operating assumptions for 

a DMU technology service with a run time of 116 minutes, a $55 fare (2005$), and 

30 minute train headways. The forecasting model has since been used to analyze 

an EMU technology service with a run time of 100 minutes, a $50 fare (2005$), 

and 20 minute train headways. The EMU service is now considered to be the 

baseline service. The proposed high levels-of-service, especially for the EMU 

service, contribute to the higher forecasts of ridership when compared to other 

potential intercity rail corridors. 

 

Total Travel Market 

As noted above, the first step in the forecasting process is the development of 

forecasts of the potential ridership of the existing travel options. This was 

accomplished by developing base year estimates of the market size and then by 

applying growth rates to these market estimates to obtain future year projections. 

Auto Trips 

URS Methods

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Due to significant inconsistencies in available data on auto travel, URS 

conducted additional fieldwork to better establish estimates of the overall 

highway travel market for the Southern California – Las Vegas travel market. 

The traffic count data from the State Departments of Transportation indicated 

significantly more vehicles entering Nevada on I-15 than the Las Vegas 

Convention & Visitors Authority (LVCVA) estimates of Las Vegas visitors from 

Southern California. To better understand the differences and estimate the 

percentage of I-15 vehicles that had Las Vegas and Southern California trip ends, 

URS conducted additional field data collection along I-15 in Baker, California. 

URS collected three types of data in Baker on November 10, 11, and 12th of 2005: 

• Mainline classification counts; 

• Ramp license plate counts; and 

• Interviews at parking lots of service stations and restaurants (N=948). 

Based on these Baker origin-destination data and traffic count data, URS 

developed estimates of the relevant auto market. The URS analysis concluded 

that: 

• 89.2% of I-15 traffic was comprised of autos (estimated through manual 

classification counts); 

• 80.8% of the auto traffic was comprised of California vehicles going to Las 

Vegas (estimated through license plate/auto occupancy observation and 

origin-destination surveys); 

• The average auto occupancy was 2.46 persons per vehicle (estimated through 

license plate/auto occupancy observation); 

• 12.7% of the trip makers were making day trips (estimated from data 

collected for a subset of the origin-destination surveys); 

• 21.8% of the trip makers did not stay in public accommodations (estimated 

from data collected for a subset of the origin-destination surveys). 

The last two of these data items were used to reconcile the I-15 traffic data based 

market estimates with LVCVA visitor estimates. The URS analysis concluded 

that there were 5,389,500 auto visitor vehicle trips and 13,258,169 auto visitor 

person trips that used I-15 in 2004. The LVCVA estimated that there were 

7,346,595 auto visitor trips in 2004. However, the LVCVA estimates include 

much lower shares of day trippers and visitors not staying in public 

accommodations than URS measured in their surveys. So, for comparison 

purposes, URS showed the effect of removing the day trips and non-public 

accommodation trips. The result of this comparison was 8,678,074 for the URS 

estimate and 7,346,595 for the LVCVA estimate. The 13,258,169 person trip 

estimate was used for the total Southern California auto market estimate. 

Table 17 of the URS Forecasting Report shows the distribution of the I-15 Las 

Vegas travelers. The 13,258,169 annual auto visitor person trips consist of 78.7% 

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Los Angeles Metro trip ends and 3.1% Victorville area trip ends, as well as an 

additional 7.0% of the trip ends from the San Diego area. It is reasonable that the 

proposed rail service would compete for these trips. Another 2.9% come from 

the Barstow area, which could presumably also be served by the rail system as 

long as a Barstow station were present. The remaining 8.4 percent of trips have 

trip ends north of the Southern California catchment area. These areas include 

Red Bluff/Anderson, Bay Area, Sacramento/Folsom, Fresno, Bishop, and 

Bakersfield. These trips technically might be able to be served by a Barstow 

Station, but the likelihood that these long distance auto travelers, who have 

chosen to travel by auto for a very long time rather than by traveling by air, 

would decide to leave their vehicles behind in Barstow, rather than to bring their 

auto all the way to Las Vegas, is very small. 

If the 2004 auto trip estimates derived from the I-15 analyses are reduced to 

account for the non-Southern California travel, the comparison of the URS I-15 

derived estimates with the LVCVA estimates are quite similar to each other: 

8,678,074 - 1,109624 = 7,568,360 for the URS estimate and 7,346,595 for the 

LVCVA estimate (within 3 percent). 

Our Review

SDG endorsed the URS overall auto estimates, but in their revised forecasting 

effort they reduced the base demand levels to reflect the number of trips that 

take place during times-of-day that the proposed rail service would not be 

available. This reduction is quite reasonable, in our view. The total divertable 

auto market should reflect the proposed DXE operating plan.3 The Desert 

Xpress Enterprises operating plan is only at the preliminary draft stage, and 

could be adjusted to extend the operating hours, so the service could compete for 

the bulk of the trips in question. 

In our view, and based on our understanding of the analyses conducted, we 

believe there are some reasons that make the total auto market forecasts based on 

the Baker origin-destination analyses somewhat uncertain. 

First, we note that we were unable to reproduce the survey analyses reported by 

URS using the origin-destination survey data file provided (datafile3.xls). This 

may be due simply to our potential misunderstanding of the data, or our 

performing different data summaries than the forecasters. However, our 

calculated percentages of Las Vegas-Southern California trips, day trips, and 

non-public accommodation trips all vary by significant amounts from the results 

reported by URS. 

Second, an important potential problem with the use of the Baker origindestination survey data is that the surveys do not necessarily represent I-15 

travelers overall. As we understand it, the surveys were performed among 

 

3 SDG Summary, pg. 4. 

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travelers who had stopped in Baker for fuel and/or food, so the implicit 

assumption is that the characteristics of those that stopped in Baker are the same 

as those that did not stop there. We believe it is more likely that short trips are 

underrepresented in the survey, because people traveling shorter distances will 

have less need to stop along the way. It is difficult to say how the mix of Las 

Vegas travel and through trip travel could be affected by the survey limitation. 

Through-trip makers may stop in the Las Vegas area, because of the large 

number of food and fuel options, and then be less likely to stop in Baker, but on 

the other hand, Las Vegas visitors may be less inclined to make a stop in Baker 

than those traveling longer distances. However these tendencies play out, they 

could have a significant effect on the estimates derived from the survey, and the 

assumption that the travelers that stopped are similar to those that did not is 

somewhat speculative. It is possible that this question could be resolved through 

additional survey work using an alternative data collection approach as the 

Applicant takes the analysis to the Investment Grade level. 

Another potential problem is that the origin-destination work was performed on 

a Thursday, Friday, and Saturday, and expanded to Sunday and the other 

weekdays. The expansion to include Sunday has not been detailed for us, but 

the percentage of traffic that is made up of Las Vegas visitors was estimated to be 

the highest on Sunday. The Thursday survey data were used to expand Monday 

through Thursday. The I-15 traffic counts indicate that the average daily traffic 

volumes at the state border for Mondays through Thursdays are similar to each 

other. However, there is evidence that Monday through Wednesday travel to 

Las Vegas is much lower than Thursday or the weekend days. More than fourfifths of respondents to the RSG stated preference survey that was used for the 

mode choice modeling indicated that they traveled between Thursday and 

Sunday.4 For that sample, more of the respondents traveled on a Thursday than 

on Mondays, Tuesdays, and Wednesdays, combined. LVCVA estimates that 

Southern California residents are 25 percent more likely to arrive on Thursday 

than on Monday and 67 percent more likely to arrive on Thursday than on 

Tuesday. LVCVA estimates the number arriving on Wednesday to be roughly 

the same as on Thursday.5 By expanding the Thursday data to be representative 

of the four weekdays, visitor trips can be overestimated. 

 

4 CS analysis of data provided in DesertExpress_SPSurvey.dbf. 

5 LVCVA, Las Vegas Visitor Profile Calendar Year 2004 Southern California and International 

Visitors Version: January 1, 2004 to December 31, 2004. 

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Air Trips 

URS Methods

For the development of base year air passenger volumes, URS appears to have 

relied on a combination of sources, including McCarran Airport (LAS) 

enplanement data and the U.S. Department of Transportation Origin & 

Destination database (referred to as the FAA travel survey in the URS report). 

The U.S. DOT origin-destination ten-percent sample database includes actual 

ticket information for 10 percent of the tickets processed by large air carriers 

(airlines operating flights with more than 60 seats). Commuter and regional 

airlines are not required to provide the Department with ticket sample data, but 

sometimes do so because of integrated operations with large air carriers. 

URS chose not to rely solely on the U.S. DOT data because they found that “not 

all airports had a ten percent sample.”6 Instead, they used the ticket sample data 

to “establish the percentage of trips headed to LAS from each airport, and then 

multiplied this percentage by the annual air traffic activity reported for each 

airport for 2004”. 

Our Review

The discrepancy identified by URS was most likely primarily the result of 

missing commuter and regional airline data and of sampling error, rather than 

the result of unknown problems with the database. Therefore, the ten percent 

sample estimates for specific origin-destination pairs from the database are likely 

to be minimum numbers of passengers (within normal sampling boundaries). 

These passenger numbers are all higher than the URS estimates for Long Beach, 

Burbank, Ontario, and Orange County. 

One way to account for the missing commuter and regional airline passengers in 

the ten percent sample data is to examine another U.S. Department of 

Transportation database, the T-100 database. The T-100 Domestic Segment 

Database provides monthly supply and demand information on all direct flights 

between U.S. airports, and the T-100 Domestic Market Database provides 

demand summaries for passengers boarding and de-boarding aircraft. The T-100 

data are actual counts, as opposed to sample data, and include all scheduled 

airline services. However, the market definition used in the T-100 database does 

not allow one to capture true origin-destination travel, because individual 

segments for connecting traffic are included. By comparing the origindestination data and ten percent sample by airline, one can identify significant 

undercounts within the origin-destination data due to missing 

commuter/regional data. For the Las Vegas-Southern California airport pairs, 

 

6 URS Report, Page 19. 

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we did not find any major sources of missing commuter/regional data. 

Therefore, we maintain that the best publicly available estimate of the air 

passenger demand in this market is the U.S. DOT 10 percent sample data, 

balanced by direction. 

The differences between the URS air travel estimates and our recommended 

estimates is shown in the following table. 

Table 1. Year 2004 Air Passenger Estimates 

Airport Pair URS Estimate 

(2004) 

Estimate Based 

on U.S. DOT 

Data 

Difference 

LAX – LAS 745,569 672,740 -10% 

LGB – LAS 82,605 90,575 +10% 

BUR – LAS 326,145 377,900 +16% 

ONT – LAS 205,426 216,775 +6% 

SNA - LAS 213,571 242,295 +13% 

TOTAL 1,573,315 1,600,285 +2% 

Sources: URS Report (page 19); Cambridge Systematics, Inc analysis of U.S. DOT 

Aviation statistics, obtained from the Bureau of Transportation Statistics website, 

November 2007. 

The overall estimates are similar, but the distribution between Southern 

California airports vary measurably and will affect the assumed distribution of 

air passenger trip ends and therefore the high speed rail forecasts. Since the 

proposed Victorville rail service is likely to be more competitive in attracting 

passengers who would otherwise use airports that are more accessible to points 

in Southern California than LAX, it is reasonable that changing the distribution 

as the U.S. DOT data suggest would lead to higher ridership estimates for the rail 

service. A more detailed analysis would be required to confirm this supposition 

though. 

Bus Trips 

To analyze the potential diversion of passengers from charter bus services to the 

proposed high speed rail service, URS used past survey data and proprietary 

data from past URS analyses of the charter bus market. The surveys were used 

to size the overall market, and the past charter bus analysis allowed them to 

make conjectures about mode choice behavior. While the approach used was 

less analytical than for the air or auto diversion estimation, it is certainly 

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understandable because of the difficulty of obtaining market data for these bus 

services. These data are typically almost impossible to get, and the fact that URS 

had access to some of these data probably indicates that their analysis was 

superior to similar estimates for other U.S. intercity rail corridor passenger 

demand efforts. 

SDG notes that for their new ridership forecasts, they have taken a more 

conservative view on the amount of traffic that might be captured from bus, but 

they do not seem to have had any issues with the overall travel market size. 

Las Vegas Forecasts 

URS Methods

Long term forecasts were related to projections of Clark County population 

through 2040. URS forecasts growth of 4.0 percent for the 2015 to 2019 period; 

2.3 percent for the 2020 to 2029 time period; and 1.2 percent for the 2030 to 2040 

time period. 

The annual growth projections for the Clark County population cited by URS 

reduce over time from 4.9 percent in 2006 to 2.7 percent in 2015 to 1.3 percent in 

2025 and finally to about 1.1 percent for the longer term. According to URS, the 

Center for Business and Economic Research expects an annual growth for 2020 of 

1.2 percent, declining to 0.9 percent by 2035. 

URS forecast short term growth in Las Vegas visitation from Southern California 

by assuming the growth would be proportional to the increase in projected hotel 

rooms over time. URS assembled an inventory of hotel and casino development 

plans, and used these projections to forecast visitation through 2014. 

SDG deemed the growth forecasts to be reasonable. 

Our Review

The URS long-term growth rate is slightly higher than the sources, but the 

consistent strong growth rate for Las Vegas development and visitation over the 

past 30 years probably explains the optimism. We believe that the forecast 

assumptions fall in a reasonable range. 

Southern California Forecasts 

URS Methods

URS allocated the Southern California trip ends based on 2000 and 2004 

population estimates disaggregated to the ZIP code level that had been 

developed by the State of California Department of Finance. Population 

projections by County for 2012 were also obtained from the Department of 

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Finance. The projections were disaggregated to the ZIP code and “SuperZip” 

levels using the ZIP level relative growth rates between 2000 and 2004. 

Our Review

We believe a better source of small area forecasts for the Southern California 

region than the disaggregated Department of Finance estimates is available from 

the Southern California Association of Governments (SCAG). SCAG maintains 

these projections to support their regional travel demand modeling and other 

planning efforts. They are informed by the state’s county level forecasts, but 

they also rely on more detailed local data, such as building permit data and 

zoning regulations. Based on our data summaries of the SCAG demographic 

databases, the SCAG projections imply different growth rates for the ZIP level 

and SuperZIP level estimates of population than the URS projections, so it is our 

belief that the Southern California trip tables could be improved through the 

application of the SCAG data. The net effect of using these data on the high 

speed rail forecasts is difficult to say, but they should improve model accuracy. 

Assessment of the Potential Effects of the Identified Issues 

In summary, our review of the total travel market estimates performed by URS 

identified several issues that could materially affect the ridership forecasts. 

We believe there were potential biases in the auto trip data collection effort that 

could result in uncertainties in the estimates of base year trips. The issues noted 

above with the expansion of trips to the different days-of-week could cause auto 

trips to be overestimated by a few thousand trips per average day. 

The overall effects of relying only on stopped traffic in Baker for the auto 

analyses is potentially larger than the issue related to the survey days. However, 

the stopped-traffic issue could affect the estimates in either direction. Arguments 

could be made for either an underestimation or overestimation of Las Vegas 

travel due to this issue. 

Therefore, we would estimate that the combination of these issues means that the 

high confidence range of trips in the auto travel market probably ranges between 

about 10 percent more than the URS estimates to 20 percent less than the URS 

estimates. 

Overall air trip estimates were found to be similar to what we would estimate 

using the U.S. DOT data, but we believe the distribution of trips by Southern 

California airport will be different than forecast by URS, with fewer LAX trips 

and more trips from/to the other Southern California airports. Because air 

service from these other airports is projected to be slightly better than for LAX, 

the rail mode would be expected to be divert slightly fewer trips from air, all else 

being equal. This effect is likely to affect the total air travel market by something 

less than about 5 percent, so a high confidence range would probably be between 

five percent less than the URS forecasts up to the URS forecasts themselves. 

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Lacking any data to evaluate the bus trip estimates, we believe the URS estimates 

are probably reasonable. However, SDG’s recommendation to adjust the total 

travel markets (air, auto, and bus) downward to account only for trips made 

while the rail service is operating seems quite reasonable to us. The size of these 

adjustments may not be significant if the Desert Xpress operating service plan is 

adjusted to match the demand profile of the other modes. 

The URS forecasts for Las Vegas growth are higher than the sources they used in 

developing the forecasts, but we believe the forecasts are in a reasonable range 

based on the past trends. Because of the difficulty in forecasting growth in 

general, we believe sensitivity testing of potential growth scenarios as URS seems 

to have performed already are a good idea. Based on our review of SCAG data, 

it appears that the distribution of Southern California trip ends in the future 

could be significantly different than projected by URS. It is not possible to 

determine the quantitative effects of the different future distributions without reapplication of the model, but it would not be surprising for the growth 

assumptions to affect the forecasts by up to five percent or so. 

Mode Choice Model 

URS Methods

To estimate the mode diversion from auto and air to the proposed high speed 

rail system, URS developed stated preference models based on a web-based 

survey of 400 Southern California participants in an Internet survey panel. The 

survey work was conducted by Resource Systems Group, using their Survey 

Café Panel. The survey respondents consisted of people who had actually 

traveled between Southern California and Las Vegas, so they had direct 

knowledge of the relevant travel decisions. Based on the survey data, binary 

logit choice models were developed for the choice between auto and high speed 

rail and for the choice between air and high speed rail. 

The surveys consisted of screening questions to determine whether a potential 

respondent lived in Southern California and had ever traveled to Las Vegas. 

Those respondents who had made trips in the past were asked details about their 

most recent trips. They were then provided a description of the proposed high 

speed rail service and asked to complete six tradeoff exercises in which they 

were given hypothetical options to travel by their most recent mode or by the rail 

mode with varying travel attribute levels. The choice attributes and range of 

levels for each mode option are shown in the table below.7

 

7 LVHSR_CBCDesign.xls. 

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Table 2. Stated Preference Exercise Attribute Levels 

Current Mode - Auto Current Mode - Air Proposed Rail Service 

Access and Wait Time 

N/A Current Time * 0.9 Time to get to Victorville * 0.9 

Current Time * 1.0 Time to get to Victorville * 1.0 

Current Time * 1.1 Time to get to Victorville * 1.1 

Current Time * 1.2 Time to get to Victorville * 1.2 

Access Cost 

N/A Base Access Cost * 1.00 Cost to get to Victorville * 1.00 

 Base Access Cost * 1.05 Cost to get to Victorville * 1.05 

 Base Access Cost * 1.10 Cost to get to Victorville * 1.10 

 Base Access Cost * 1.15 Cost to get to Victorville * 1.15 

 

Main Mode In-vehicle Time 

Current Time * 0.9 Current Time * 0.9 Estimated Rail Time * 0.9 

Current Time * 1.0 Current Time * 1.0 Estimated Rail Time * 1.0 

Current Time * 1.1 Current Time * 1.1 Estimated Rail Time * 1.1 

Current Time * 1.2 Current Time * 1.2 Estimated Rail Time * 1.2 

Main Mode Cost 

Base Cost * 1.00 Base Cost * 1.00 $40 

Base Cost * 1.05 Base Cost * 1.05 $50 

Base Cost * 1.10 Base Cost * 1.10 $60 

Base Cost * 1.15 Base Cost * 1.15 $70 

Egress Time 

N/A 30 minutes 10 minutes 

45 minutes 20 minutes 

Egress Cost 

N/A $5 $5 

 $10 $10 

Service Frequency 

N/A 2 flights every hour 2 trains every hour 

1 flight every hour 1 train every hour 

1 flight every 2 hours 1 train every 2 hours 

1 flight every 4 hours 1 train every 4 hours 

Reliability – 1 out of 3 trips late 

Travel time * 1.05 Travel time * 1.05 Travel time * 1.03 

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Current Mode - Auto Current Mode - Air Proposed Rail Service 

Travel time * 1.1 Travel time * 1.1 Travel time * 1.05 

Travel time * 1.2 Travel time * 1.2 Travel time * 1.1 

Travel time * 1.3 Travel time * 1.3 Travel time * 1.15 

Reliability – 1 out of 20 trips late 

Travel time * 1.32 Travel time * 1.32 Travel time * 1.2 

Travel time * 1.4 Travel time * 1.4 Travel time * 1.25 

Travel time * 1.48 Travel time * 1.48 Travel time * 1.3 

Travel time * 1.56 Travel time * 1.56 Travel time * 1.35 

The levels for each attribute were varied according to a pre-established 

experimental design in order to measure how respondents make tradeoffs 

between the different attributes. 

The resulting mode choice models based on these hypothetical choices were 

summarized in the URS Report in Tables 21 and 22, and seem to have been 

updated since then for forecasting. The model parameters in the spreadsheet, 

HSR_MC13_LVTransParty_Alt1.xls are shown below. 

Table 3. Stated Preference Model Parameters 

Las Vegas High Speed Rail Stated Preference Survey

Draft model for binary choice between AUTO Travel and Las Vegas HSR

Variable Label Units Estimate Standard Error T-Ratio VoT

Travel Time Time Minutes -0.005 0.001 -6.3 8.55 $ 

Cost of Travel Cost $* -0.038 0.002 -18.3

Reliability (Average Minutes Late) RelAvMins Minutes -0.004 0.005 -0.9

Distance to Victorville AcDist Miles -0.003 0.003 -0.9

Frequency - 2 per Hour Freq2pH (0,1) 0.721 0.138 5.2

Frequency - 1 per Hour Freq1pH (0,1) 0.609 0.137 4.4

Frequency - 1 per 2 Hours Freq1p2H (0,1) 0.349 0.140 2.5

Frequency - 1 per 4 Hours Freq1p4H (0,1) 0.000 0.000 0.0

Current Mode Constant cmcon (0,1) 0.194 0.277 0.7

Number of Observations 2400

Number of Respondents 300

Log Likelihood -1299.21

Notes

Sensitivity to in vehicle time was found to be statistically similar to access and egress time

* Cost of travel is total cost of travel divided by party size

Reliability is specified for just the auto alternative and is in terms of average minutes late. It has the correct sign and a reasonable magnitude (i.e. it 

The model was NOT improved by transforming cost by household income

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16 Cambridge Systematics, Inc. 

Las Vegas High Speed Rail Stated Preference Survey

Draft model for binary choice between Air Travel and Las Vegas HSR

Variable Label Units Estimate Standard Error T-Ratio VoT**

In Vehicle Time IVTime Minutes -0.012 0.005 -2.4 30.44 $ 

Access, Wait and Egress Time AcEgTime Minutes -0.025 0.004 -5.6 60.69 $ 

Cost of Travel Cost $* -0.104 0.015 -7

Reliability (Average Minutes Late) RelAvMins Minutes -0.018 0.020 -0.9

Current Mode Constant cmcon (0,1) -0.243 0.317 -0.8

Number of Observations 408

Number of Respondents 51

Log Likelihood -204.21

Notes

A specification has not been found that shows frequency to be statistically significant

* Cost of travel is total cost of travel divided by party size and divided by log of household income in thousands

The model was significantly improved by transforming cost by 1/log(income), indicating that cost sensitivity is inversely proportional to the log of 

income

** VoT is evaluated at $70,000 per annum household income

Reliability (represented here as average minutes late) has the correct sign and a reasonable magnitude (i.e. it is similar to the other time 

coefficient in the model), but it is not statistically significant

RSG tested the sensitivity of the model to the coefficients by randomly varying 

the parameters according to the model estimates and their standard errors. 

Our Review

SDG concluded that the surveys provided “useful insights into the potential of 

capture of the new rail service,” but they raised doubts about the robustness of 

the SP analysis, and pointed out some concerns about the data collection and 

modeling effort, including: 

• High number of choice attributes in the survey added complexity to the 

choice exercises; 

• Representation of reliability in the choice exercises was also particularly 

complicated; 

• Attributes focused on time and cost components of choice, and did not 

address less quantitative choice attributes; 

• The rate of non-trading (respondents that chose the same mode in all 

exercises) was too high; 

• Choice model is not sufficiently sensitive to changes in relative generalized 

costs. 

Because of these concerns, SDG performed additional survey work to modify the 

mode choice models, but they did not report the resulting changes in the mode 

choice model parameters that they recommended. 

We agree with SDG’s overall concern and specific issues regarding the stated 

preference modeling. In addition to the SDG issues, we have the following 

concerns about the stated preference modeling. 

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Regarding survey sample: 

The web panel sample approach was likely chosen for ease-of-use, and the 

particular panel used has a good reputation, but because of the panel’s size, the 

screening for this survey was very limited. Anybody who had ever traveled to 

Las Vegas was included, regardless of when or how frequently they traveled. 

About 8 percent of the respondents had traveled to Las Vegas once, and almost a 

quarter of respondents had not made a Las Vegas trip within the past year. This 

probably means that the results of questions about trip details are more 

vulnerable to recall error than in most transportation surveys, and it raises the 

question of whether many respondents provided relevant choice information. 

Even with the liberal definition of Las Vegas traveling respondents, the sample 

sizes for the stated preference survey analysis are small. The stated preference 

analysis is based on 300 auto respondents, but only 51 air respondents. Ideally, 

we would like to see at least 150 to 200 respondents for each mode. This issue 

could be addressed with additional survey work when the Applicant takes the 

forecasts to Investment Grade. 

Eight choice exercise responses from each respondent were obtained to increase 

the base number of choices used in modeling, but the choices from a single 

individual will be correlated with each other. One consequence of this is that the 

model derived estimates of parameter standard errors are understated. This 

means that while the parameter estimates are unbiased, the level of uncertainty 

in their values is higher than is indicated in the URS and RSG documentation. 

Thus, unless RSG has recalculated the model parameter standard errors, their 

parameter sensitivity analyses understate the size of the model confidence 

intervals. The model results are less precise than the reported analysis indicates. 

Regarding choice exercise design: 

In general, the attribute ranges used in the stated preference choice exercises 

seem reasonable, except that the auto costs that were tested in the exercises 

appear to be higher than the costs that are typically assumed to be considered by 

auto travelers. The stated preference survey documentation provided in the file 

DX0321 Survey Info.doc indicates that auto operating cost levels for the tradeoff 

exercises were in a range between $0.25 to $0.29 per mile. Usually, auto travelers 

will consider their cost of travel to be only their out-of-pocket gas costs. Thus, in 

most intercity travel models, auto costs are generally in the range of $0.10 to 

$0.15 per mile. While higher per mile costs are more consistent with the true 

costs of driving (including operating, maintenance, and ownership costs), they 

are generally not considered by travelers for specific travel decisions. 

As a result of the presentation of the higher range of operating costs, the 

measured perceived value-of-time was constrained to be in the higher range. 

Since the resulting models appear to be generally reasonable for most parameters 

with the perceived auto cost of $0.26 per mile, it is possible that these model 

results are valid, but because a lower range of auto cost levels were not included 

in the experimental design, it is not possible to know whether more conventional 

F-D-17
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18 Cambridge Systematics, Inc. 

auto operating cost assumptions would change the modeling results. The auto 

costs presented in the choice exercises would have looked surprisingly high to 

respondents, and might have led to higher rejection of the auto option than 

would occur in reality, and may have led respondents not to consider the other 

attributes as much as they would if the experiment were more realistic to them. 

On the other hand, the survey research was performed at a time when gasoline 

prices were significantly lower than they are now and when auto operating costs 

were less in the forefront than they are now. As the analyses of the system are 

taken further, additional survey work could help to shed light on this issue. 

Regarding choice model estimation: 

The model parameters are all of the right mathematical sign, and were not 

constrained in model estimation. In addition, the relative magnitudes of the 

parameters compared with each other seem reasonable. The reported values-oftime are in line with expectations based on other intercity travel studies. 

We did find the reliability model parameter curious as it does not reflect the way 

in which the concept was presented to respondents in the choice exercises. 

Respondents made their hypothetical choices after being provided information 

about the distribution of delays (one in three chance of being some number of 

minutes late and one in 20 chance of being some other number of minutes late), 

but in model estimation this information has been transformed into a single 

average delay time. This transformation may have produced more desirable 

model specifications than analyzing the parameters as they were presented to 

respondents, but it is based on implicit assumptions about how people quantify 

reliability that are not fully supported by the survey data. 

The reported sensitivity runs performed by URS and RSG produced results that 

seem reasonable based on our understanding of intercity travel markets. For 

instance, a 10 minute increase in the assumed rail time resulted in a 4.7 percent 

decrease in rail ridership. A 20 minute increase in the assumed rail time resulted 

in a 11.2 percent decrease. The sensitivity to rail travel time is important because 

of the proposed use of Regina EMU train sets, a different train technology than 

URS initially studied that would reduce train travel times to about 100 minutes, 

rather than the 116 minutes used in the URS initial forecasts. The reasonableness 

of the model sensitivity runs for train travel times leads us to conclude that the 

mode choice model can correctly account for the general magnitude of increased 

ridership that would result from the use of the EMU train sets. 

Assessment of the Potential Effects of the Identified Issues

A number of potential mode choice modeling issues were raised by SDG and by 

ourselves. For most of the identified issues, the bottom line effects of the issues 

are not obvious. It would require additional data analyses and possibly 

additional data collected, such as SDG seems to have done, to address these 

issues. Therefore, unless additional analyses are performed, our review would 

F-D-18
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indicate a wider range of uncertainty in the mode choice model parameters than 

has been suggested by previous URS and RSG sensitivity analyses. 

For the most part, we cannot really determine whether improvements along the 

lines of the issues identified would have a positive or negative effect on the mode 

choice forecasts, and probably there would be some cancellation of the effects to 

some degree. Some improvements may increase potential rail mode shares, 

while others may dampen them. 

To test the sensitivity of the model to individual model parameters, RSG 

performed Monte Carlo simulation, varying parameter estimates based on 

assumed distributions of those parameters that were developed by examining 

the model estimation results. We would recommend that these analyses be 

performed assuming a much wider range of potential parameter estimates, 

reflecting the sampling, sample size, and error-measurement issues raised above. 

Model Application 

URS Methods

To develop forecasts of the mode diversions, URS applied the mode choice 

models using estimates of the characteristics of the competing modes. For 

application of the mode choice model, URS made numerous assumptions about 

the attributes of the rail system and the competing auto and air modes. 

The high speed rail attributes used in model application reflected the actual 

operating plans of the system at the time of the forecasting, while the air and 

auto assumptions are based on existing service levels, the survey input 

assumptions, and some other available sources. The treatment of the model 

input assumptions for each mode in the two choice models are summarized in 

the URS Report and in the spreadsheet HSR_MC13_LVTransParty_Alt1.xls. 

SDG’s subsequent analysis of the operating plan and train simulations led them 

to suggest improvements to the high speed rail assumptions, including 

significantly improved travel time between Victorville and Las Vegas of 100 

minutes, as compared to the initial URS forecasting assumption of 116 minutes. 

The baseline assumptions for the rail service were updated later to reflect an 

EMU based service. The revised forecasts are summarized in a January 7, 2008, 

memorandum from Desert Xpress Enterprises. 

SDG did not comment on the input assumptions for the other modes. 

Our Review

URS’s auto travel time assumptions were developed using highway network 

data from the Orange County Transportation Authority (OCTA) model with URS 

improvements to congested network speeds. For our review, we compared the 

network times with skim times from the SCAG model and to selected MapQuest 

F-D-19
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20 Cambridge Systematics, Inc. 

driving time estimates. The URS auto travel times to Victorville were found to 

be very reasonable. 

URS used real time congestion estimates and studies of specific bottleneck 

locations to quantify the level of auto delay that could be experienced by those 

traveling by auto to Las Vegas, Victorville, and the airports. This approach is 

judgmental, but is probably necessary as current planning tools do not account 

for non-recurring congestion well. The base delay estimates are probably in a 

reasonable range, generally, and the delays are all assigned west of Victorville, 

meaning that they are experienced by modeled high speed rail customers as well 

as modeled auto travelers. The Desert Xpress modeled ridership does not benefit 

from assumptions about auto congestion that might occur between Victorville 

and Las Vegas. Many survey respondents indicated that they experienced 

congestion on this segment of I-15, and such congestion may become more 

common as travel volumes increase over time. So, the assumption of no future 

delays on this segment can be considered conservative. 

We also believe that the URS assumptions regarding Las Vegas access to the 

Desert Xpress system and connectivity with the monorail are reasonable. 

On the other hand, we have identified the following issues with the model 

application input assumptions. 

We disagree with the way that URS has defined air delay for the model 

application. URS develops their definition by combining different components 

of terminal processing times, including security checkpoint delays, counter, and 

curbside delays; as well as auto access delays. However, these are not the 

components that most travelers would designate as delays. In our view, these 

time components should be considered in the definition of air service levels, but 

are more appropriately included as part of the access, egress, and wait time of 

the air journey, rather than as delay time. Because the airlines are operating on 

schedules, air passengers build the uncertainty in terminal times and potential 

access time into the amount of time that they arrive early at the airport. Because 

passengers have reserved this time already, increased variability in these times 

are only a problem if they are large enough to overcome the amount of time that 

the passengers have reserved for them (and cause them to miss or almost miss a 

flight). 

A better measure of air delay that more closely corresponds to the unexpected 

delay concept that was presented in the stated preference exercises is airline ontime performance. The U.S. Department of Transportation records the on-time 

performance of all domestic airline flights. For this review, we summarized the 

2004 airline on-time data for Las Vegas-Southern California flights. 

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Table 4. Airline On Time Performance, 2004 

Origin Destination Average Delay in 

Minutes 

Percent Delayed 

More than 15 

Minutes 

Percent Delayed 

More than 30 

Minutes 

BUR LAS 9.8 18.1% 10.3% 

LAS BUR 13.6 26.5% 13.9% 

LAX LAS 11.3 21.9% 12.4% 

LAS LAX 11.2 21.2% 12.3% 

LGB LAS 6.4 9.8% 5.5% 

LAS LGB 6.8 10.5% 6.0% 

ONT LAS 10.2 19.4% 11.3% 

LAS ONT 13.7 28.4% 15.4% 

LAS SNA 8.9 19.5% 10.3% 

SNA LAS 9.9 19.2% 11.3% 

All Airport Pairs 11.1 21.4% 12.1% 

Source: Cambridge Systematics, Inc analysis of U.S. Department of 

Transportation On-Time Performance Database, obtained from the Bureau of 

Transportation Statistics website, November 2007. 

We believe that air delay should be represented in the model in this way, rather 

than by the measures employed by URS. If the advance time that travelers build 

into trips to avoid missing flights is included, it is more appropriately included 

as a component of the access, egress, and wait time. 

At the same time, if the high speed rail system is operated using advanced 

ticketing, then high speed rail passengers will need to build a similar allowance 

into their trip time. While this allowance may not need to include any security 

checkpoint time, the longer access distances to Victorville, as opposed to the 

airports, will affect the variability in travel times, and therefore the amount of 

time that people will feel necessary to reserve. The current assumption of 10 

minute transfer times in Victorville is reasonable if passengers can board any 

train when they come to Victorville and if train headways are 20 minutes or less. 

It is unclear to us how the Desert Xpress reservation system will work, but it is 

our understanding that headways will only be this frequent on Fridays and 

Sundays, so many or most travelers would experience longer transfer times. The 

fare and reservations policies for the proposed system are still under 

development, and more sophisticated yield management based approaches are 

under consideation, so it is difficult to judge the reasonableness of the wait time 

assumptions at this point. 

As discussed above, travelers will rarely consider the full range of auto operating 

costs in their trip decisions. Most regional and intercity models assume auto 

operating costs that are about one-half of what was used by URS. Given that the 

model was estimated with the higher auto costs, it would be inappropriate to 

forecast the auto costs so much lower, but it is important to note that this input 

F-D-21
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22 Cambridge Systematics, Inc. 

assumption could have a significant negative effect on the assumed 

competitiveness of the auto mode. 

Finally, it is our belief that the aggregate manner in which the model is applied 

introduces bias into the forecasts. The estimated model relies on both household 

income and travel party size as part of the cost term. This means that even when 

trips begin and end at the same zones, the probability of selecting the high speed 

rail mode will be different for travelers with different party sizes and household 

incomes. In URS’s model application, zone average and median values of these 

characteristics are used, so the estimated variations in choice probabilities are 

lost. Typically, aggregation bias has a small but measurable overall effect on 

forecasts, and this bias may diminish the usefulness of the forecasts in 

understanding specific origin-destination travel markets. 

Assessment of the Potential Effects of the Identified Issues

The two forecasting issues that have been identified that we believe cause the 

estimated mode shares for rail to be overstated to some degree are the use of a 

very high auto operating cost and the measurement of travel delay for air and 

rail. 

Perceived auto costs are probably significantly lower than modeled. Since the 

model was developed using the higher costs per mile and the rail mode is 

affected by auto costs for the significant access portions of the trip, the effect of 

the higher auto costs in the model are muted to some extent, but the model 

appears to us to be penalizing auto travelers to a larger extent than other similar 

models would. Likewise, the way that delays and wait time are modeled does 

not capture how travelers would actually perceive these times and make choices 

based (in part) on these times. In our view, the auto cost assumptions and 

delay/wait time assumptions could affect forecasts of rail trips by more than 10 

percent. 

The model application zone aggregation issues raised above could affect the 

ridership estimates in either direction, so it is difficult to know how they could 

affect the estimates. 

Induced Demand 

URS Methods

Based on the Baker survey data results, URS determined that a special induced 

day trip market for the proposed high speed rail service might develop. The 

analysis of this potential market segment is described in a URS memorandum.8 

 

8 URS Corporation, Frequently asked Questions: Induced Day Trip Forecasting 

Methodology—Interim Report (October 12, 2006). 

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For their review, SDG “adjusted downwards the estimated level of induced 

number of day trips,” but they did not elaborate. 

Our Review

While it is likely that the introduction of a new high quality service such as that 

envisioned by Desert Xpress will induce a small number of intercity trips 

between Southern California and Las Vegas, we are not convinced the day trip 

analysis conducted by URS is the best means to estimate this induced demand. 

In our view, the Baker Survey data do not provide compelling justification for 

defining a new travel market, because 1) the survey sample size was smaller than 

we would like to draw market conclusions; and 2) the survey technique used to 

measure interest levels was necessarily simplified to accommodate the survey 

data collection procedures. Based on our analysis of the Baker survey data, a 

total of 115 Las Vegas traveler respondents were asked: 

“If there were a high speed train from California to Las Vegas, would you 

consider a one-day trip on this train?” 

Of these only 12 respondents were currently making day trips. 

Respondents to this question could not be provided with any additional 

information about the high speed rail system including any basic information 

about the routing, cost, or amenities, because the surveys were being personally 

administered. Because respondents did not receive a full description of the 

proposed service, it is very difficult to know what characteristics that 

respondents were assuming for the system. In our view, the survey question 

serves more as a referendum on high speed trains than a comprehensive 

measure of market interest. 

Furthermore, travelers already have a high-speed public mode option for 

completing one-day trips to Las Vegas—the air mode. The 2001 LAX airport 

access survey includes 292 Southern California residents traveling to Las Vegas, 

of which 20 reported making a day trip. Using the weighted estimates, about 8.5 

percent of Las Vegas air trips are day trips. These trips would be included in the 

overall air passenger demand estimates, and would be subject to the air-HSR 

mode choice model, so including them as a separate market is not really justified. 

Assessment of the Potential Effects of the Identified Issues

We believe it would be prudent to not include the URS induced day trip 

forecasts until more data on potential induced travel are obtained and analyzed. 

An alternative approach to estimating induced demand could be included in 

future analyses as the forecasts are taken to Investment Grade. 

Summary 

We have identified a number of potential areas of concern with the specific 

details of the forecasting methods as we understand them. Some of the issues, if 

F-D-23
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24 Cambridge Systematics, Inc. 

addressed, will decrease the ridership forecasts, while others will increase them, 

but the effects of some will be unknown unless they are implemented. The 

previous SDG review seems to concur with this overall conclusion. As 

documented above, in our view, most of the URS analyses are based on sound 

methods and have been implemented well, but several of the analyses rely on 

small sample surveys, and for some of these we have raised methodological 

concerns. 

Users of these forecasts should be aware of the potential high level of uncertainty 

and should exercise reasonable caution in interpreting model results. URS has 

done an admirable job in testing the sensitivity of the forecasts to different input 

assumptions already, but these efforts should be expanded with the idea that the 

uncertainties related to these inputs may be larger than what has been tested. 

Without additional analyses, it is impossible for us to accurately bound the 

potential level of uncertainty in forecasts our concerns raise, but the table below 

summarizes our rough estimate of how the issues raised could affect the URS 

forecasts. It is important to note that 1) these quantitative estimates are very 

rough measures of the potential magnitudes based on our review and 

professional judgment; and 2) these uncertainty levels are related to the issues 

we have raised only and do not encompass the full range of uncertainty that 

accompanies any travel demand forecasting effort. Travel demand forecasts rely 

on a number of explicit and implicit assumptions, and not all future states can be 

accounted for with these forecasts. 

Rough Estimation of the Quantitative Effects of the Issues Raised 

Area of Concern Potential Effect on Forecast 

Recommend not including induced day trip 

forecasts 

Reduces forecasts by 9 percent 

Additional uncertainty in total auto trip 

forecasts 

Auto trips between 10 percent higher to 20 

percent lower than URS forecasts 

Additional uncertainty in total air trip forecasts Air trips between URS forecasts and 5 percent 

lower than URS forecasts 

Additional uncertainty in Southern California 

catchment area socioeconomic distributions 

+/- five percent of the URS forecasts 

Mode choice modeling imprecision +/- 15 percent of the URS forecasts 

Improved auto cost and delay forecasting 

assumptions 

Reduces forecasts by between 0 and 10 

percent 

Additional uncertainty introduced by 

aggregate model application 

+/- five percent of the URS forecasts 

Use of EMU train set technology improving 

train times to 100 minutes 

Increases forecasts by 9 percent 

It would not be reasonable to assume that addressing all the issues listed would 

lead to the most pessimistic potential decreases or to the most optimistic 

F-D-24
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potential increases, but the issues raised would tend to have us conclude that 

addressing these issues of concern would more likely reduce the URS forecasts. 

Based on all the issues raised, we believe it would not be unreasonable to assume 

that the range of reasonable rail mode share forecasts could easily range from 

one-third less than the URS forecasts to one-fourth more than those forecasts. 

Based on this, we feel a reasonable point estimate forecast of about 10 percent 

lower than the initial URS estimate would be a reasonable estimate of the Desert 

Xpress ridership for future analyses, at least until better forecasting information 

is obtained. Based on the issues raised, the forecast reduction should come from 

auto trip diversion (and induced demand). Based on the reasons that we 

recommend reducing the forecasts, one approach for applying this simplified 

adjustment would be to reduce the projected diversion from auto for Southern 

California zones where auto is most competitive (as measured by the mode 

choice model utility estimates) by 12 percent and then to reduce the diversion 

from other zones by a lesser amount (about 5 percent). The table in Appendix A 

reflect rail passenger forecasts for the DEMU and EMU technologies, adjusted by 

the factors discussed above. 

Additional analyses could enhance the reliability of the forecasts and decrease 

the level of uncertainty. These additional analyses are often done when 

transportation projects that are at least partly funded by fares or tolls are 

subjected to Investment Grade analyses, and Desert Xpress Enterprises has 

indicated that they expect to do so for this proposed service. 

Like all ridership forecasts, the URS forecasts assume that the system is fully 

operational and that all travelers have knowledge of the system. In reality, it will 

take a matter of a few years for travelers to know about the system and to 

evaluate it for their own travel behavior decisions. Often there is an initial fewmonth test period where interested and motivated travelers try out the new 

system, but on balance the first few years of the service are likely to be 

characterized by a “ramp-up” period. For transportation infrastructure projects, 

a common “ramp-up” period assumption would be to say that the first year of 

service would achieve 50 to 60 percent of the forecast usage, and the second year 

would achieve 75 to 85 percent of the forecast usage. By the third year of regular 

service, we would expect a more steady state usage level to be achieved. The 

“ramp-up” period will be affected by many factors, including marketing and 

early successful operations. 

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Appendix A – Reduced Passenger Forecast Summaries 

Table A1 2012 Rail Passenger Forecasts Based On Application of 

HSR_MC13_LVTransParty_Alt1.xls – DEMU 

116 minutes; $55; 30 minute headway 116 minutes; $55; 30 minute headway 

 With recommended reductions 

SuperZip 

Diverted 

from Air 

Diverted 

from Auto 

Diverted 

from Bus SuperZip 

Diverted 

from Air 

Diverted 

from Auto 

Diverted 

from Bus

L02 175 988 0 L02 166 869 0

L05 32 10 44 L05 30 8 40

L09 0 108 30 L09 0 95 27

L12 591 459 0 L12 561 404 0

L14 0 85 0 L14 0 75 0

L16 60 148 75 L16 57 130 69

L18 44 220 20 L18 42 194 18

L20 0 94 0 L20 0 83 0

L21 131 319 0 L21 124 281 0

L22 0 64 30 L22 0 57 28

L23 96 217 122 L23 91 191 112

L24 0 283 0 L24 0 249 0

L27 13 252 0 L27 13 222 0

L30 18 134 31 L30 17 118 28

L31 51 122 0 L31 49 107 0

L32 24 309 33 L32 23 272 30

L33 14 78 0 L33 13 69 0

L34 21 351 25 L34 20 309 23

L35 25 322 0 L35 24 284 0

L36 24 242 60 L36 23 213 55

L38 430 691 63 L38 409 608 57

L39 13 224 0 L39 12 197 0

L42 87 86 281 L42 83 76 257

L43 32 111 0 L43 30 97 0

L44 94 170 0 L44 90 150 0

L45 46 173 22 L45 44 152 20

L50 72 327 64 L50 68 288 59

L51 60 175 0 L51 57 154 0

O1 63 489 0 O1 60 430 0

O12 0 97 0 O12 0 85 0

O13 11 173 0 O13 11 152 0

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116 minutes; $55; 30 minute headway 116 minutes; $55; 30 minute headway 

 With recommended reductions 

SuperZip 

Diverted 

from Air 

Diverted 

from Auto 

Diverted 

from Bus SuperZip 

Diverted 

from Air 

Diverted 

from Auto 

Diverted 

from Bus

O14 38 205 0 O14 36 181 0

O2 69 32 0 O2 66 29 0

O3 24 185 0 O3 22 163 0

O4 0 177 0 O4 0 156 0

O6 0 141 0 O6 0 124 0

O7 25 183 0 O7 24 161 0

O8 11 326 0 O8 10 287 0

O9 0 104 0 O9 0 91 0

R1 75 210 0 R1 71 185 0

R2 76 373 0 R2 72 328 0

R3 40 262 0 R3 38 231 0

SB1 105 153 0 SB1 100 135 0

SB2 0 342 0 SB2 0 301 0

SB3 0 208 0 SB3 0 183 0

SB4 0 127 0 SB4 0 112 0

SB5 0 237 0 SB5 0 209 0

V1 42 267 16 V1 40 235 15

V2 15 139 0 V2 14 122 0

(blank) 0 0 0 (blank) 0 0 0

SD1 0 673 0 SD1 0 592 0

Grand 

Total 2747 11866 915 

Grand 

Total 2610 10442 837

 

Notes: 2012 Average Friday Forecasts resulting from the application of the forecasting spreadsheet 

(without "Ramp-Up" assumptions). 

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Table A2 2012 Rail Passenger Forecasts Based On Application of 

HSR_MC13_LVTransParty_Alt1.xls – EMU 

100 minutes; $50; 20 minute headway 100 minutes; $50; 20 minute headway 

 With recommended reductions 

SuperZip 

Diverted 

from Air 

Diverted 

from Auto 

Diverted 

from Bus SuperZip 

Diverted 

from Air 

Diverted 

from 

Auto 

Diverted 

from 

Bus 

L02 224 1227 0 L02 213 1080 0

L05 41 12 55 L05 39 10 51

L09 0 134 37 L09 0 118 34

L12 722 569 0 L12 686 500 0

L14 0 106 0 L14 0 93 0

L16 78 184 96 L16 74 162 88

L18 58 273 26 L18 55 241 23

L20 0 116 0 L20 0 102 0

L21 163 392 0 L21 155 345 0

L22 0 79 38 L22 0 70 35

L23 119 267 151 L23 113 235 138

L24 0 347 0 L24 0 306 0

L27 17 311 0 L27 16 274 0

L30 24 166 39 L30 23 146 36

L31 65 151 0 L31 61 133 0

L32 32 383 42 L32 30 337 39

L33 18 97 0 L33 17 86 0

L34 28 435 32 L34 27 383 29

L35 33 401 0 L35 31 353 0

L36 31 299 75 L36 29 263 69

L38 554 859 79 L38 526 756 73

L39 17 280 0 L39 16 246 0

L42 108 107 349 L42 103 94 319

L43 42 137 0 L43 40 120 0

L44 118 211 0 L44 112 186 0

L45 60 214 28 L45 57 189 25

L50 90 403 80 L50 86 355 73

L51 66 213 0 L51 63 188 0

O1 82 603 0 O1 78 531 0

O12 0 119 0 O12 0 105 0

O13 15 214 0 O13 14 188 0

O14 49 254 0 O14 47 224 0

O2 91 40 0 O2 87 35 0

O3 31 228 0 O3 29 200 0

O4 0 218 0 O4 0 192 0

O6 0 175 0 O6 0 154 0

O7 33 226 0 O7 31 199 0

O8 14 404 0 O8 14 355 0

O9 0 129 0 O9 0 113 0

R1 92 258 0 R1 87 227 0

R2 92 458 0 R2 87 403 0

F-D-28
Desert Xpress Ridership Forecast Review 

Cambridge Systematics, Inc. 29 

100 minutes; $50; 20 minute headway 100 minutes; $50; 20 minute headway 

 With recommended reductions 

SuperZip 

Diverted 

from Air 

Diverted 

from Auto 

Diverted 

from Bus SuperZip 

Diverted 

from Air 

Diverted 

from 

Auto 

Diverted 

from 

Bus 

R3 49 324 0 R3 47 285 0

SB1 131 188 0 SB1 125 165 0

SB2 0 417 0 SB2 0 367 0

SB3 0 255 0 SB3 0 224 0

SB4 0 154 0 SB4 0 136 0

SB5 0 285 0 SB5 0 251 0

V1 56 333 21 V1 53 293 19

V2 20 174 0 V2 19 153 0

(blank) 0 591 0 (blank) 0 520 0

SD1 0 673 0 SD1 0 592 0

Grand 

Total 3464 15094 1147 

Grand 

Total 3291 13283 1049

 

Notes: 

2012 Average Friday Forecasts resulting from the application of the forecasting spreadsheet 

(without "Ramp-Up" assumptions). 

F-D-29
Desert Xpress Ridership Forecast Review 

30 Cambridge Systematics, Inc. 

Table A3 Rail Passenger Forecasts Based On Application of 

HSR_MC13_LVTransParty_Alt1.xls - DEMU 

116 minutes; $55; 30 minute headway 116 minutes; $55; 30 minute headway 

 With recommended reductions 

 

Diverted 

from Air 

Diverted 

from Auto 

Diverted 

from Bus Total Rail 

Diverted 

from Air 

Diverted 

from Auto 

Diverted 

from Bus Total Rail 

Average Friday Trips 

2012 1,648 7,120 549 9,317 1,566 6,265 502 8,334 

2013 2,286 9,873 761 12,920 2,172 8,688 697 11,556 

2014 2,972 12,834 990 16,796 2,823 11,294 906 15,023 

2015 3,091 13,348 1,029 17,468 2,936 11,746 942 15,624 

2016 3,214 13,882 1,071 18,167 3,053 12,216 980 16,249 

2017 3,343 14,437 1,113 18,893 3,176 12,705 1,019 16,899 

2018 3,476 15,015 1,158 19,649 3,303 13,213 1,060 17,575 

2019 3,615 15,615 1,204 20,435 3,435 13,741 1,102 18,278 

2020 3,699 15,974 1,232 20,905 3,514 14,057 1,127 18,698 

2021 3,784 16,342 1,260 21,386 3,595 14,381 1,153 19,128 

2022 3,871 16,718 1,289 21,878 3,677 14,711 1,180 19,568 

2023 3,960 17,102 1,319 22,381 3,762 15,050 1,207 20,018 

2024 4,051 17,495 1,349 22,896 3,848 15,396 1,235 20,479 

2025 4,144 17,898 1,380 23,422 3,937 15,750 1,263 20,950 

2026 4,239 18,309 1,412 23,961 4,027 16,112 1,292 21,432 

2027 4,337 18,731 1,445 24,512 4,120 16,483 1,322 21,925 

2028 4,437 19,161 1,478 25,076 4,215 16,862 1,352 22,429 

2029 4,539 19,602 1,512 25,652 4,312 17,250 1,383 22,945 

2030 4,593 19,837 1,530 25,960 4,363 17,457 1,400 23,220 

2031 4,648 20,075 1,548 26,272 4,416 17,666 1,417 23,499 

2032 4,704 20,316 1,567 26,587 4,469 17,878 1,434 23,781 

2033 4,760 20,560 1,586 26,906 4,522 18,093 1,451 24,066 

2034 4,818 20,807 1,605 27,229 4,577 18,310 1,468 24,355 

2035 4,875 21,056 1,624 27,556 4,632 18,530 1,486 24,647 

Annualization - Friday to Annual Trips 

Air 168 days 

Auto 252 days 

Bus 210 days 

F-D-30
Desert Xpress Ridership Forecast Review 

Cambridge Systematics, Inc. 31 

Table A4 Rail Passenger Forecasts Based On Application of 

HSR_MC13_LVTransParty_Alt1.xls - EMU 

100 minutes; $50; 20 minute headway 100 minutes; $50; 20 minute headway 

 With recommended reductions 

 

Diverted 

from Air 

Diverted 

from Auto 

Diverted 

from Bus Total Rail 

Diverted 

from Air 

Diverted 

from Auto 

Diverted 

from Bus Total Rail 

Average Friday Trips 

2012 2,078 9,057 688 11,823 1,974 7,970 630 10,574 

2013 2,882 12,558 954 16,395 2,738 11,051 873 14,662 

2014 3,747 16,326 1,240 21,313 3,559 14,367 1,135 19,061 

2015 3,897 16,979 1,290 22,165 3,702 14,941 1,180 19,824 

2016 4,052 17,658 1,342 23,052 3,850 15,539 1,228 20,616 

2017 4,215 18,364 1,395 23,974 4,004 16,161 1,277 21,441 

2018 4,383 19,099 1,451 24,933 4,164 16,807 1,328 22,299 

2019 4,558 19,863 1,509 25,930 4,331 17,479 1,381 23,191 

2020 4,663 20,320 1,544 26,527 4,430 17,881 1,413 23,724 

2021 4,771 20,787 1,579 27,137 4,532 18,293 1,445 24,270 

2022 4,880 21,265 1,616 27,761 4,636 18,713 1,478 24,828 

2023 4,992 21,754 1,653 28,400 4,743 19,144 1,512 25,399 

2024 5,107 22,255 1,691 29,053 4,852 19,584 1,547 25,983 

2025 5,225 22,767 1,730 29,721 4,964 20,035 1,583 26,581 

2026 5,345 23,290 1,770 30,405 5,078 20,495 1,619 27,192 

2027 5,468 23,826 1,810 31,104 5,195 20,967 1,656 27,818 

2028 5,594 24,374 1,852 31,819 5,314 21,449 1,694 28,457 

2029 5,722 24,934 1,894 32,551 5,436 21,942 1,733 29,112 

2030 5,791 25,234 1,917 32,942 5,501 22,206 1,754 29,461 

2031 5,860 25,536 1,940 33,337 5,567 22,472 1,775 29,815 

2032 5,931 25,843 1,964 33,737 5,634 22,742 1,797 30,173 

2033 6,002 26,153 1,987 34,142 5,702 23,015 1,818 30,535 

2034 6,074 26,467 2,011 34,552 5,770 23,291 1,840 30,901 

2035 6,147 26,784 2,035 34,966 5,840 23,570 1,862 31,272 

Annualization - Friday to Annual Trips 

Air 168 days 

Auto 252 days 

Bus 210 days 

F-D-31
Desert Xpress Ridership Forecast Review 

32 Cambridge Systematics, Inc. 

Table A5 Rail Passenger Forecasts Based On Application of 

HSR_MC13_LVTransParty_Alt1.xls – DEMU Annual Trips 

116 minutes; $55; 30 minute headway 116 minutes; $55; 30 minute headway 

 With recommended reductions 

 

Diverted 

from Air 

Diverted 

from Auto 

Diverted 

from Bus Total Rail 

Diverted 

from Air 

Diverted 

from Auto 

Diverted 

from Bus Total Rail 

Annual Trips 

2012 276,944 1,794,169 115,314 2,186,427 263,097 1,578,869 105,512 1,947,478 

2013 384,029 2,487,915 159,902 3,031,846 364,827 2,189,365 146,310 2,700,503 

2014 499,237 3,234,289 207,873 3,941,399 474,275 2,846,175 190,204 3,510,654 

2015 519,207 3,363,661 216,188 4,099,055 493,246 2,960,022 197,812 3,651,080 

2016 539,975 3,498,207 224,835 4,263,018 512,976 3,078,423 205,724 3,797,123 

2017 561,574 3,638,136 233,829 4,433,538 533,495 3,201,559 213,953 3,949,008 

2018 584,037 3,783,661 243,182 4,610,880 554,835 3,329,622 222,511 4,106,968 

2019 607,398 3,935,008 252,909 4,795,315 577,029 3,462,807 231,412 4,271,247 

2020 621,369 4,025,513 258,726 4,905,607 590,300 3,542,451 236,734 4,369,486 

2021 635,660 4,118,100 264,677 5,018,436 603,877 3,623,928 242,179 4,469,984 

2022 650,280 4,212,816 270,764 5,133,860 617,766 3,707,278 247,749 4,572,793 

2023 665,237 4,309,711 276,992 5,251,939 631,975 3,792,545 253,447 4,677,968 

2024 680,537 4,408,834 283,363 5,372,734 646,510 3,879,774 259,277 4,785,561 

2025 696,190 4,510,237 289,880 5,496,307 661,380 3,969,009 265,240 4,895,629 

2026 712,202 4,613,973 296,547 5,622,722 676,592 4,060,296 271,341 5,008,228 

2027 728,583 4,720,094 303,368 5,752,044 692,153 4,153,683 277,581 5,123,418 

2028 745,340 4,828,656 310,345 5,884,341 708,073 4,249,217 283,966 5,241,256 

2029 762,483 4,939,715 317,483 6,019,681 724,359 4,346,949 290,497 5,361,805 

2030 771,633 4,998,992 321,293 6,091,917 733,051 4,399,113 293,983 5,426,147 

2031 780,892 5,058,980 325,148 6,165,020 741,848 4,451,902 297,511 5,491,260 

2032 790,263 5,119,687 329,050 6,239,000 750,750 4,505,325 301,081 5,557,156 

2033 799,746 5,181,124 332,999 6,313,868 759,759 4,559,389 304,694 5,623,841 

2034 809,343 5,243,297 336,995 6,389,635 768,876 4,614,102 308,350 5,691,328 

2035 819,055 5,306,217 341,039 6,466,311 778,102 4,669,471 312,050 5,759,623 

 

Notes: Assumes a "ramp-up" period in 2012 and 2013 (60 percent of forecast ridership in 2012; 80 percent of 

forecast ridership in 2013). Annual growth rates for ridership based on URS projections. 

F-D-32
Desert Xpress Ridership Forecast Review 

Cambridge Systematics, Inc. 33 

Table A6 Rail Passenger Forecasts Based On Application of 

HSR_MC13_LVTransParty_Alt1.xls – EMU Annual Trips 

100 minutes; $50; 20 minute headway 100 minutes; $50; 20 minute headway 

 With recommended reductions 

 

Diverted 

from Air 

Diverted 

from Auto 

Diverted 

from Bus Total Rail 

Diverted 

from Air 

Diverted 

from Auto 

Diverted 

from Bus Total Rail 

Annual Trips 

2012 349,174 2,282,239 144,502 2,775,915 331,716 2,008,370 132,219 2,472,305 

2013 484,189 3,164,705 200,376 3,849,269 459,979 2,784,940 183,344 3,428,263 

2014 629,445 4,114,116 260,489 5,004,050 597,973 3,620,422 238,348 4,456,742 

2015 654,623 4,278,681 270,909 5,204,212 621,892 3,765,239 247,881 4,635,012 

2016 680,808 4,449,828 281,745 5,412,381 646,767 3,915,849 257,797 4,820,413 

2017 708,040 4,627,821 293,015 5,628,876 672,638 4,072,482 268,109 5,013,229 

2018 736,362 4,812,934 304,735 5,854,031 699,544 4,235,382 278,833 5,213,758 

2019 765,816 5,005,451 316,925 6,088,192 727,525 4,404,797 289,986 5,422,309 

2020 783,430 5,120,577 324,214 6,228,221 744,258 4,506,107 296,656 5,547,022 

2021 801,449 5,238,350 331,671 6,371,470 761,376 4,609,748 303,479 5,674,603 

2022 819,882 5,358,832 339,299 6,518,014 778,888 4,715,772 310,459 5,805,119 

2023 838,739 5,482,085 347,103 6,667,928 796,802 4,824,235 317,600 5,938,637 

2024 858,030 5,608,173 355,087 6,821,290 815,129 4,935,192 324,904 6,075,226 

2025 877,765 5,737,161 363,254 6,978,180 833,877 5,048,702 332,377 6,214,956 

2026 897,954 5,869,116 371,609 7,138,678 853,056 5,164,822 340,022 6,357,900 

2027 918,607 6,004,105 380,156 7,302,868 872,676 5,283,613 347,842 6,504,131 

2028 939,735 6,142,200 388,899 7,470,834 892,748 5,405,136 355,843 6,653,726 

2029 961,349 6,283,470 397,844 7,642,663 913,281 5,529,454 364,027 6,806,762 

2030 972,885 6,358,872 402,618 7,734,375 924,240 5,595,807 368,395 6,888,443 

2031 984,559 6,435,178 407,449 7,827,187 935,331 5,662,957 372,816 6,971,105 

2032 996,374 6,512,401 412,339 7,921,113 946,555 5,730,913 377,290 7,054,758 

2033 1,008,331 6,590,549 417,287 8,016,167 957,914 5,799,683 381,817 7,139,415 

2034 1,020,430 6,669,636 422,294 8,112,361 969,409 5,869,280 386,399 7,225,088 

2035 1,032,676 6,749,672 427,362 8,209,709 981,042 5,939,711 391,036 7,311,789 

 

Notes: Assumes a "ramp-up" period in 2012 and 2013 (60 percent of forecast ridership in 2012; 80 percent of 

forecast ridership in 2013). Annual growth rates for ridership based on URS projections. 

F-D-33
Transmax, LLC 

Desert Xpress Updated Ridership and Revenue Study 

Draft Final Report 

December 22, 2005 

One Penn Plaza, Suite 610 

New York, NY 10119-0698 

F-D-34
Table of Contents 

A. Summary of Ridership Forecasts ....................................................................................... 1

1. Baseline Forecast .......................................................................................................... 1

2. Alternative Stations ....................................................................................................... 2

3. Sensitivity Analysis for Fare ........................................................................................... 4

4. Shuttle Service Between Primm and Las Vegas ............................................................... 5

B. Model Application............................................................................................................. 7

1. Results from Testing With 2004 Trips............................................................................. 7

2. HSR Attraction of Day Trips ........................................................................................... 7

3. Future Las Vegas Visitation and Assumptions.................................................................. 8

C. Sensitivity Analysis for Travel Time, Headway, and I-15 Delay ............................................ 9

1. Introduction.................................................................................................................. 9

2. Running Time Sensitivity ............................................................................................... 9

3. Headway Sensitivity .................................................................................................... 10

4. Impact of I-15 Capacity and Congestion on Ridership (with and without widening) ......... 11

D. Ridership and Revenue Forecasts.................................................................................... 14

F. Model Development and Documentation.......................................................................... 17

1. Southern California to Las Vegas Travel Market............................................................. 17

2. Airport and Highway Measures of Congestion and Delay................................................ 19

3. Southern California Demographic Analysis .................................................................... 20

4. Web Survey Used to Assess HSR Market....................................................................... 21

5. Model Sensitivity Analysis Statistics ............................................................................. 23

Page 1

F-D-35
List of Tables 

 No. Title Page 

1. 2012 Ridership, Rio HSR Station (Monorail Spur to Monorail Mainline) .......................... 1

2. 2012 Ridership, Rio HSR Station (Bus Shuttle to Monorail Station)................................ 2

3. 2012 Ridership, Mandalay Bay HSR Station (Monorail Spur to Monorail Mainline) .......... 2

4. 2012 Ridership, Mandalay Bay HSR Station (Bus Shuttle to Monorail Station)................ 3

5. 2012 Ridership, Rio and Bonneville HSR Stations (Monorail Spur to Mainline)................ 3

6. 2012 Ridership, Mandalay Bay and Bonneville HSR Station (Monorail Spur)................... 3

7. Fare Sensitivity Analysis............................................................................................. 5

8. Daily Primm Shuttle Ridership 2012............................................................................ 6

9. 2004 HSR Trips to Las Vegas by Location of Origin...................................................... 7

10. HSR Ridership vs. Change in Running Time............................................................... 10

11. HSR Ridership Vs Change in Headway ...................................................................... 11

12. Ridership Depending on Congestion ......................................................................... 12

13. Sensitivity of Ridership to Delay ............................................................................... 13

14. Projected Las Vegas Lodging Rooms(1,000’s) ........................................................... 14

15. HSR Ridership and Revenue..................................................................................... 16

16. I-15 Survey Expanded to Annual Auto Visitor Control................................................. 17

17. I-15 Survey Information Converted to Annual ........................................................... 18

18. Air Travel Control for 2004....................................................................................... 19

19. Airport Delay Measures............................................................................................ 20

20. Mode Choice Parameters, Las Vegas HSR Stated Preference Survey ........................... 21

21. Binary Choice Model between Auto Travel and Las Vegas HSR................................... 22

22. Binary Choice Model between Air Travel and Las Vegas HSR...................................... 23

23. Sensitivity Analysis Statistics .................................................................................... 23

Page 2

F-D-36
Desert Xpress High Speed Rail Ridership, Interim Report 

A. Summary of Ridership Forecasts 

1. Baseline Forecast 

Ridership forecasts were developed for a proposed high-speed rail service between Victorville 

California to Las Vegas. The estimated diversion of trip makers from driving and flying to Las 

Vegas was based on a Web based stated preference survey of selected Southern California 

residents carried out during July 2005. The probability of a trip maker switching to High Speed 

Rail (HSR) was derived from how these respondents answered questions concerning the 

attractiveness of HSR service. The model was developed using current information (2004) to 

describe variables such as security delays at airports, air travel to Las Vegas, and travel costs. In 

addition, a survey was taken of I-15 vehicles to establish the number of California resident 

coming to Las Vegas by auto. The ridership shown below is for the year 2012. Future trip levels 

were based on expected Las Vegas visitation in 2012, and the growth of Southern California 

population. The baseline forecast assumes a $55 one-way fare. 

The best performing single station option was for a High Speed Rail (HSR) station near the Rio, 

with a monorail spur connection the HSR station to the monorail system. The 2012 ridership 

forecast for the Rio Station with monorail service is 4.16 million annual round trips on HSR 

between Victorville and Las Vegas. The projected 2012 total travel market from Southern 

California to Las Vegas was for 18.2 million trips. Of this market, the HSR service captures 

22.8%. The diversion varies by mode. The diversion from auto is 3.4 million trips, 23.6% of the 

total market. This share includes a 3.3% share of the market for new day trips made possible by 

the high speed rail service. Diversion share from air travel is 22.2% of the market. The air market 

is smaller than the auto market so this diversion contributes only 461,000 annual trips. There is 

also a diversion of 260,000 annual trips from charter bus. Table 1 below shows these results in 

more detail. 

The survey of I-15 users indicated that 11.2% of the vehicles going to Las Vegas come from 

Barstow, Bakersfield, and other communities north of Bakersfield. A Barstow station would tap 

this market, so a second station was tested in Southern California. With both the Victorville 

station and the Barstow station, ridership increased from 4.16 million annual trips to 4.27 million 

trips. With this station added, the ridership from Victorville decreased slightly, because the 

additional stop increases travel time from Victorville. However, there was a net gain in ridership 

of 2.6%. Potential ridership at the Barstow station is not included in the following forecasts. 

Table 1 

2012 Ridership, Rio HSR Station (Monorail Spur to Monorail Mainline)

116 Min Ave Run Time Ave Friday 

HSR Trips 

Ratio, Annual 

To Peak Day 

Annual Trips 

 By Mode 

 Annual 

HSR Trips 

 Market 

Share 

From Auto 12,077 252 14,994,213 3,044,727 20.3% 

Induced Day Trips 1,566 252 394,748 2.2% 

From Air 2,747 168 2,074,520 460,750 22.2% 

From Bus 1,240 210 1,180,227 260,187 22.0% 

Totals 17,630 18,248,960 4,160,412 22.8% 

* Based on an average annual Friday. **This ratio is used to expand the Friday ridership to annual ridership. 

Page 1

F-D-37
2. Alternative Stations 

Three HSR station locations in Las Vegas were tested. Two of these were the Rio HSR Station 

located adjacent to Rio Suites on W. Flamingo Rd. and the Mandalay Bay Station located 

adjacent to Mandalay Bay. The third was a station at Bonneville adjacent to a proposed monorail 

station for the Downtown extension. Both strip area stations were tested assuming that there will 

be a monorail spur between the HSR station and a monorail station. The spur for the Rio Station 

is to the Harrah’s monorail station. Monorail service to the airport is also assumed to maintain a 

fair comparison between HSR and air travel. As a variation, both stations were tested assuming a 

dedicated shuttle bus for connecting service to the HSR station rather than a direct monorail 

connection. For all alternatives and variations tested, it is assumed that taxi egress is used for 

destinations with no monorail service. The Barstow station is not included in the following 

ridership forecasts. These Tables show HSR trips from Southern California to Las Vegas (trip 

totals also can be considered as the number of round trips). 

The Rio Station was also run with a connection to the Bally’s monorail station using a dedicated 

shuttle bus. With the absence of the more attractive monorail access, ridership decreases to 4.0 

million annual trips. Ridership by mode for this variation is shown below. Note that diversion 

from air is more impacted by this service change than diversion from auto. 

Table 2 

2012 Ridership, Rio HSR Station (Bus Shuttle to Monorail Station)

116 Min Ave Run Time Ave Friday 

HSR Trips 

Ratio, Annual 

To Peak Day 

Annual Trips 

 By Mode 

 Annual 

HSR Trips 

 Market

Share 

From Auto 11,743 252 14,994,213 2,960,285 19.7%

Induced Day Trips 1,522 252 383,648 2.1%

From Air 2,509 168 2,074,520 420,719 20.3%

From Bus 1,205 210 1,180,227 252,911 21.4%

Totals 16,979 18,248,960 4,017,563 22.0%

The Mandalay Bay HSR Station was also tested assuming a spur connection to the monorail, in 

this case connecting at the MGM Grand station. Ridership for this station is slightly lower than 

the Rio Station with an annual ridership of 4.12 million trips. This station is less competitive in 

diverting airport trips than the Rio location, but about the same for diversion from auto. 

Ridership for this Alternative is shown below. 

Table 3 

2012 Ridership, Mandalay Bay HSR Station (Monorail Spur to Monorail Mainline)

114.5 Min Ave Run 

Time

Ave Friday 

HSR Trips 

Ratio, Annual 

To Peak Day 

Annual Trips 

 By Mode 

 Annual 

HSR Trips 

 Market 

Share 

From Auto 12,103 252 14,994,213 3,051,276 20.3%

Induced Day Trips 1,569 252 395,441 2.2%

From Air 2,468 168 2,074,520 413,883 20.0%

From Bus 1,242 210 1,180,227 260,751 22.1%

Totals 17,383 18,248,960 4,121,351 22.6%

This Alternative was tested assuming that a dedicated shuttle bus would connect the HSR station 

to the monorail. Ridership declined 2.3% from ridership assuming a monorail spur. 

Page 2

F-D-38
Table 4 

2012 Ridership, Mandalay Bay HSR Station (Bus Shuttle to Monorail Station)

114.5 Min Ave Run Time Ave Friday 

HSR Trips 

Ratio, Annual 

To Peak Day 

Annual Trips 

 By Mode 

 Annual 

HSR Trips 

 Market 

Share 

From Auto 11,817 252 14,994,213 2,979,113 19.9%

Induced Day Trips 1,532 252 386,089 2.1%

From Air 2,413 168 2,074,520 404,722 19.5%

From Bus 1,213 210 1,180,227 254,533 21.6%

Totals 16,975 18,248,960 4,024,457 22.1%

Two Alternatives were run to evaluate having two stations for the high speed rail service. The 

following Table shows ridership with the Rio Station at Flamingo Rd and an HSR Station at 

Bonneville, near Downtown. While the addition of the Downtown Station adds some ridership, 

the increase is only a 1 percent gain. This Alternative was evaluated further since intuitively a 

larger increase was expected. This analysis showed that the share of trips using HSR did increase 

in the Downtown area by 8%. However the magnitude of the Downtown market is such that this 

increase is only a small share of total HSR ridership. 

 Table 5 

2012 Ridership, Rio and Bonneville HSR Stations (Monorail Spur to Mainline)

116 Min Ave Run Time Ave Friday 

HSR Trips 

Ratio, Annual 

To Peak Day 

Annual Trips 

 By Mode 

 Annual 

HSR Trips 

 Market

Share 

From Auto 12,201 252 14,994,213 3,075,958 20.5%

Induced Day Trips 1,582 252 398,639 2.2%

From Air 2,773 168 2,074,520 465,024 22.4%

From Bus 1,252 210 1,180,227 262,879 22.3%

Totals 17,809 18,248,960 4,202,500 23.0%

A run was also made to evaluate two stations with one at Mandalay Bay and the other station at 

Bonneville. Contrary to expectations, the growth with the additional station is even less than for 

the Rio Station Alternatives. 

Table 6 

2012 Ridership, Mandalay Bay and Bonneville HSR Station (Monorail Spur)

114.5 Min Ave Run Time Ave Friday 

HSR Trips 

Ratio, Annual 

To Peak Day 

Annual Trips 

 By Mode 

 Annual 

HSR Trips 

 Market 

Share 

From Auto 12,286 252 14,994,213 3,097,385 20.7%

Induced Day Trips 1,593 252 401,416 2.2%

From Air 2,565 168 2,074,520 430,129 20.7%

From Bus 1,261 210 1,180,227 264,726 22.4%

Totals 17,705 18,248,960 4,193,657 23.0%

In general, variations in station locations in Las Vegas as tested by the above Alternatives do not 

significantly impact ridership. There are two reasons for this. One is that the Las Vegas egress is 

only a small part of the total trip between Southern California and Las Vegas. The second reason 

is that regardless of the location, in this model, destinations in the Strip and Downtown can be 

reached by taxi. Station location and monorail connections would have a greater impact if the 

available taxi service were not in the model. However this would mean that some locations could 

only be reached by local bus connections. This is somewhat unrealistic given the nature of the 

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market. Available taxi service is a necessary assumption for a major transportation facility. 

(Where taxi was used, taxi fares were included in the model.) 

3. Sensitivity Analysis for Fare 

There is greater variation in ridership with changes in fare, running time and other variables as 

explained below. However by far, the most important variable in these ridership models is fare. 

In the estimation of the model, fare was always the most significant variable regardless of the 

other variables that were tested with it. 

In order to evaluate how the fare variables affected ridership, a series of model runs were made 

where everything was held constant except the dependent variable being evaluated. The 

alternative used for this analysis was the Rio HSR station with a monorail connection. All runs 

used the 2012 trip table. Fare is particularly strong in the auto model where the elasticities are 

greater than 1, ranging from –1.2 to –2.1. Fare is important but not quite so strong for the air 

model, with elasticities starting at -0.8 and ranging to –1.3 at the highest level. It should be noted 

that auto is 83% of the total Southern California market so the auto mode always dominates 

overall ridership results. Because of this high elasticity, ridership decreases rapidly as fare 

increases. At a $40 fare, ridership is 6.2 million trips, while with the $70 fare, ridership drops to 

2.6 million per year. 

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Table 7 

Fare Sensitivity Analysis 

HSR Fare 

Level 

HSR Running Time 

116 Min 

Ave Friday 

HSR Trips 

Ratio, Annual 

To Peak Day 

Annual Trips 

 By Mode 

 Annual 

HSR Trips 

 Market 

Share 

$40 From Auto 18,286 252 14,994,213 4,609,979 30.7% 

Day Trips 2,371 252 597,683 3.3% 

From Air 3,613 168 2,074,520 605,944 29.2% 

From Bus 1,877 210 1,180,227 393,945 33.4% 

Totals 26,147 18,248,960 6,207,552 34.0% 

$50 From Auto 13,966 252 14,994,213 3,520,805 23.5% 

Day Trips 1,811 252 456,472 2.5% 

From Air 3,020 168 2,074,520 506,394 24.4% 

From Bus 1,433 210 1,180,227 300,870 25.5% 

Totals 20,230 18,248,960 4,784,542 26.2% 

$55 From Auto 12,077 252 14,994,213 3,044,727 20.3% 

Day Trips 1,566 252 394,592 2.2% 

From Air 2,747 168 2,074,520 460,750 22.2% 

From Bus 1,240 210 1,180,227 260,187 22.0% 

Totals 17,630 18,248,960 4,160,255 22.8% 

$60 From Auto 10,380 252 14,994,213 2,616,791 17.5% 

Day Trips 1,346 252 339,267 1.9% 

From Air 2,492 168 2,074,520 417,987 20.1% 

From Bus 1,065 210 1,180,227 223,618 18.9% 

Totals 15,284 18,248,960 3,597,663 19.7% 

$70 From Auto 7,545 252 14,994,213 1,902,205 12.7% 

 Day Trips 978 252 246,621 1.4% 

 From Air 2,035 168 2,074,520 341,205 16.4% 

 From Bus 774 210 1,180,227 162,553 13.8% 

 Totals 11,333 18,248,960 2,652,584 14.5% 

4. Shuttle Service Between Primm and Las Vegas 

A dedicated hourly shuttle between Las Vegas was evaluated in addition to the line haul service. 

This operation serves a number of relatively small markets. The first market is trips made to 

Primm from Las Vegas by Las Vegas visitors and residents. This market was estimated to be 

11,430 daily round trips in 2005. A second market is for the visitors who stay in Primm but wish 

to travel to the Strip or other Las Vegas attractions (3,070 daily round trips). The third market is 

the employees who work in Primm but live in Las Vegas. It was estimated that the employees 

make 4,460 daily round trips. 

Another market is the Las Vegas visitors who will ride the HSR as a form of entertainment. 

There is a distinct sub market of Las Vegas visitors who go to various attractions during the 

course of their visit. This market is estimated to make 54,240 trips per day in 2005.This is a 

sizeable market, so even if a small share is attracted to the HSR shuttle, it could account for a 

meaningful number of trips. Ridership will depend upon how the service is presented. If the HSR 

is seen as just another train, then ridership will be limited to “railfan” types who will ride the 

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train just because it is there. If the train is provided as an experience, and marketed that way, the 

service might attract a share of the attraction-going market. 

The following table shows the shuttle ridership forecast for 2012. This assumes that the shuttle 

service is marketed as an “experience”. Otherwise, ridership by attraction goers would be 

significantly smaller. 

Table 8 

Daily Primm Shuttle Ridership 2012 

Fare 

Assumptions: Mode Visitors from Las 

Vegas 

Trips from Primm

to Las Vegas 

Employee 

Trips 

Attraction 

Trips 

Total HSR 

Shuttle Trips 

Auto Trips 11,287 2,898 3,686 

Bus Trips 179 0 200 

HSR Trips 1,621 617 1,218 1,963 5,419 

Share 12.39% 17.54% 23.86% 

Visitor Round Trp 

$12.00 

Employee 

Round Trip 

$10.00 Total Trips 

Control 13,086 3,515 5,104 

Auto Trips 11,501 2,979 3,686 

Bus Trips 179 0 200 

HSR Trips 1,407 536 1,218 1,181 4,341 

Share of Total 10.75% 15.25% 23.86% 

Visitor Round Trp 

$16.00 

Employee 

Round Trip 

$10.00 Total Trips 

Control 13,086 3,515 5,104 

Note that ridership, especially attraction ridership, is fairly sensitive to fare. When fares are 

raised from a $12 round trip fare to a $16 fare, ridership decreases from 5,420 daily trips to 4,340 

daily trips. This ridership is for daily travel. However, the attraction trips are calculated as 

annual, and then presented as daily. This is done because annual attraction trips are discounted to 

take into account length of stay, and the likelihood that people who may make multiple visits to 

Las Vegas each year will not repeatedly ride the shuttle. With attraction trips discounted this 

way, the ridership forecasts can be directly expanded to annual ridership. 

There are other variables that could impact this forecast. The first is the nature of future 

development at Primm. Additional casinos could easily be built in Primm, since it is surrounded 

by vacant land. For this forecast it was assumed that existing facilities would be expanded, but 

no new casinos would be built. If there is major development in Primm, ridership would be 

higher than the above forecast. The second variable has to do with the attraction trips. If the 

shuttle service does not have a unique entertainment aspect, interest in the service could subside 

quickly. The prudent assumption is that attraction ridership will diminish over time. Finally, this 

shuttle analysis does not include service to the Ivanpaw site, and does not take into account 

future development there. 

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B. Model Application 

1. Results from Testing With 2004 Trips 

Research Systems Group (RSG) developed two-mode choice models (described in detail in 

Section D below) from survey data. URS programmed these models so that they could be applied 

to each ZIP in the Southern California service area. Input data was prepared for each ZIP, and 

the models were run to produce the probability of HSR use at the ZIP level. The estimated trip 

table was multiplied by this percentage to yield a ridership number. 

In order to evaluate the suitability of the resulting models, the models were applied using 2004 

trip end data. (Note that this analysis is conceptual since it will take some time to implement the 

high speed rail service.) This application showed that 20.3% of the auto trips would switch to 

HSR and 22.0% of the air passengers would also switch. The total HSR mode share including 

Charter Bus is 20.6%. Table 8 below has study service area totals sub-divided into 

approximations of the Los Angeles airport airsheds. This sub-division by airshed shows that 

there are geographic differences in mode share. In general, the auto mode shares vary with the 

distance from Victorville. The Burbank airshed has the lowest share HSR (19.7%) while the 

Ontario airshed has the highest. The one variation in this relationship is for the cities north of 

Cajon Pass. Victorville and Apple Valley have a lower share even though they are close to 

Victorville. Most likely this happens because the shorter distance to Las Vegas from these cities 

begins to have an influence. 

The air passenger model varies differently and more widely. As with the auto model, the Ontario 

airshed has the highest share in the air passenger model. However in the case of the air passenger 

model, the HSR share is much higher here than for the other airsheds. This may be due to the 

shorter distance between the Ontario airport and Victorville. For some trips, particularly in San 

Bernardino County, the drive to Victorville is not much longer than the drive to the airport. Note 

the large spread between the highest and lowest share, 31.5% for the Ontario airshed versus 

15.3% for the Orange County airshed. 

Table 9 

2004 HSR Trips to Las Vegas by Location of Origin

116 Min Ave. Run Time 

Area Name Airshed Ave. Friday 

Total Auto 

HSR from 

Auto HSR Share Total Air Psgr 

HSR from 

Air HSR Share HSR from Bus 

Burbank Trib Area BUR 12,314 2,426 19.7% 1,943 325 16.7% 221 

Orange Co Trib Area SNA 8,078 1,726 21.4% 1,272 194 15.3% - 

Central LA Trib Area LAX 12,896 2,503 19.4% 4,704 1,086 23.1% 503 

Ontario Trib Area ONT 8,110 1,968 24.3% 1,439 453 31.5% 217 

S Bernardino Mts All Drive 5,291 840 15.9% - - 0.0% - 

Total Southern Calif. 46,689 9,463 20.3% 9,359 2,058 22.0% 940 

2. HSR Attraction of Day Trips 

During the I-15 interview survey, many respondents were enthusiastic about using the HSR 

service to make day trips to Las Vegas. Given these responses, it was determined that the 

ridership estimation model should be modified to include additional day trips that would be 

generated by the HSR service. To do this, the travel characteristics of survey respondents who 

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reported making day trips were evaluated. This revealed that the market for day trips did not 

include the entire study area. Essentially, it was limited to the communities in Los Angeles, 

Orange, and San Bernardino Counties that are closer to Victorville. Ventura and Riverside 

Counties were not included. The distribution of day trips was 51.0% Los Angeles, 20.5% Orange 

County, and San Bernardino 28.5%. As explained, not all of Los Angeles was included. 

However, the share is high because many of the communities included have large populations. 

3. Future Las Vegas Visitation and Assumptions 

To develop 2012 forecasts, a 2012 trip table was projected. Travel was assumed to grow 

proportionately to the increase in Las Vegas annual visitation. A 2012 projection of visitation 

was made based on the Las Vegas Hotel/Casino Development Construction report. Planned 

projects are listed by date up through 2009. Proposed development expected to start construction 

after 2009 are listed as tentative proposed projects with undetermined completion dates. Based 

on that report, total rooms in the Strip and Downtown were estimated for each year between July 

2005 and 2014. It was assumed that all the projects listed would be built by 2014. It was further 

assumed that visitation growth would be proportional to the increase in rooms over time. The 

intermediate year of 2012 was thus used to project travel. 

There have been a number of proposed changes to the Las Vegas Monorail system. These 

proposals include extending the Monorail to the airport to the south and to Downtown to the 

north. A West Side line has also been proposed although it has not been clearly defined at this 

time. The future transit network used in this study assumes that the segment to the airport and to 

Downtown will be complete by 2012. Since the West Side line may be less likely in this time 

frame, the accessibility is modeled assuming a spur to the Rio station from the Harrah’s monorail 

station. When Mandalay Bay is tested, it is assumed that a spur will go from the MGM Grand 

monorail station to Mandalay Bay. The Rio HSR Station alternative was also tested assuming no 

additions to the existing monorail system. For this case, a dedicated shuttle bus was assumed to 

move travelers from the HSR station to the Bally’s Monorail Station. 

For all the Alternatives and variations tested, egress from the Las Vegas HSR station to Strip and 

Downtown destinations is by two modes. If a destination can be reached using the monorail 

system then transit is used for this destination. If a destination cannot be reached via the 

monorail system, then the egress mode is taxi. The taxi time and cost is similar or less than 

transit because the fare is assumed to be shared by the number of individuals in an average party. 

The competitiveness of taxi tends to dilute differences between station location and transit 

services. 

The high-speed rail is coded to have a running time from Victorville to the Mandalay Bay 

Station of 114.5 minutes. From Victorville to the Rio Station the time is 117.5 minutes. When 

two stations are tested (Rio and Bonneville or Mandalay Bay and Bonneville) the time between 

Mandalay Bay and Downtown is 5 minutes with a 3 minutes dwell at Mandalay Bay. The time 

between Rio and Downtown is 4 minutes with a 3 minutes dwell at the Rio Station. 

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C. Sensitivity Analysis for Travel Time, Headway, and I-15 Delay 

1. Introduction 

In order to evaluate how these three variables affect ridership, a series of model runs were made 

where everything was held constant except the dependent variable being evaluated. The 

alternative used for this analysis was the Rio HSR station with a monorail connection, and taxi 

service to locations not served by monorail. All runs used the 2012 trip table. 

2. Running Time Sensitivity 

A range of High Speed Rail in-vehicle running times between Victorville and the first station in 

Las Vegas were tested. This was done to illustrate the relationship between HSR running times 

and ridership. Runs were made with times 10 minutes faster than the 116 min base, and with 

times 5 and 10 minutes slower than the base. Table 9 below shows the ridership by prior mode. 

Based on the elasticities, this is a fairly important variable. For the auto diversion model, the 

elasticities are between -0.47 and –0.53. However, the travel time is twice as important to air 

travelers. The elasticity in the air model is almost double what it is for auto, between –1.002 and 

–1.156. 

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Table 10 

HSR Ridership vs. Change in Running Time

HSR 

Runtimes 

Ave Friday 

HSR Trips 

Ratio, Annual 

To Peak Day 

Annual 

Trips 

 By Mode

 Annual 

HSR Trips 

 Market 

Share 

Ave Friday 

HSR Trips 

106 Min From Auto 12,596 252 14,994,213 3,175,542 21.2%

 Day Trips 1,633 252 411,545 2.3%

 From Air 3,007 168 2,074,520 504,305 24.3%

 From Bus 1,293 210 1,180,227 271,366 23.0%

 Totals 18,529 18,248,960 4,362,757 23.9%

116 Min From Auto 12,077 252 14,994,213 3,044,727 20.3%

 Day Trips 1,566 252 394,592 2.2%

 From Air 2,747 168 2,074,520 460,750 22.2%

 From Bus 1,240 210 1,180,227 260,187 22.0%

 Totals 17,630 18,248,960 4,160,255 22.8%

121 Min From Auto 11,824 252 14,994,213 2,980,790 19.9%

 Day Trips 1,533 252 386,306 2.1%

 From Air 2,624 168 2,074,520 439,964 21.2%

 From Bus 1,214 210 1,180,227 254,723 21.6%

 Totals 17,194 18,248,960 4,061,783 22.3%

126 Min From Auto 11,574 252 14,994,213 2,917,834 19.5%

 Day Trips 1,501 252 378,147 2.1%

 From Air 2,504 168 2,074,520 419,847 20.2%

 From Bus 1,188 210 1,180,227 249,343 21.1%

 Totals 16,766 18,248,960 3,965,171 21.7%

3. Headway Sensitivity 

Headway is not a strong variable in terms of elasticities. At a 60-minute headway, auto elasticity 

is only -0.1. However, because the differences are large, 30 to 60 and 60 to 120, it is an 

important ridership variable. Ridership goes from 4.16 million to 3.86 million when service 

changes from twice an hour to once an hour. When the headway further decreases to once every 

2 hours the ridership drops more rapidly, with ridership at 3.18 million. Frequency of service did 

not show up as a significant variable for air passengers. Perhaps because they are already basing 

their travel on an airline schedule, the difference between twice an hour and once an hour is not 

important to them. It is possible that very infrequent service might have an impact on air 

travelers, but headways greater than 2 hours were not included in the model. The differences in 

ridership between once an hour and twice an hour do indicate that when the headways vary 

throughout the day, this should be taken into account when forecasting ridership. However the 

impact is minimized because more people do travel in the peak when twice an hour service is 

offered. 

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Table 11 

 HSR Ridership Vs Change in Headway 

HSR 

Headway HSR Fare $55 Ave. Friday 

HSR Trips 

Ratio, 

Annual to 

Pk Day 

Annual Trips 

By Mode Annual Trips HSR Market Share 

30 Min. From Auto 12,077 252 14,994,213 3,044,727 20.3%

Day Trips 1,566 252 394,592 2.2%

From Air 2,747 168 2,074,520 460,750 22.2%

From Bus 1,240 210 1,180,227 260,187 22.0%

Totals 17,630 18,248,960 4,160,255 22.8%

60 Min. From Auto 11,055 252 14,994,213 2,786,952 18.6%

Day Trips 1,433 252 361,185 2.0%

From Air 2,747 168 2,074,520 460,750 22.2%

From Bus 1,184 210 1,180,227 248,516 21.1%

Totals 16,420 18,248,960 3,857,403 21.1%

120 Min. From Auto 8,942 252 14,994,213 2,254,395 15.0%

 Day Trips 1,061 252 267,431 1.5%

 From Air 2,747 168 2,074,520 460,750 22.2%

 From Bus 958 210 1,180,227 201,027 17.0%

 Totals 13,709 18,248,960 3,183,603 17.4%

4. Impact of I-15 Capacity and Congestion on Ridership (with and without widening) 

Runs were made to evaluate the impacts of I-15 congestion on ridership. The base for this 

analysis was the Rio Station Alternative assuming the monorail spur, which is projected to 

generate 4.1 million round trips in 2012. It is important to note that this base run already has 

some auto delay as input to the model run. The delay used is equivalent to delay encountered in a 

peak category of travel day in 2005. Note that this is average daily delay, not highest hour delay. 

This corresponds to the delay experienced by survey respondents who indicated their sensitivity 

to delay. 

While highway delays occur throughout the Southern California freeway system, delays on I-15 

happen on a recurring basis. The worst case is Sunday evening between Barstow and I-615. 

However delay also occurs Friday and Saturday on I-15 northbound entering Nevada. Some

improvements to I-15 are already under construction or complete. These improvements are in the 

southbound direction between Barstow and Victorville. Widening and intersection improvements 

in the northbound direction are also under construction. Additional improvements are planed 

between Barstow and the Nevada State line. These are currently scheduled to be complete by 

2009. Widening all sections of I-15 between Barstow and the Nevada State line is not scheduled 

at this time but is under consideration. 

Clearly, the completion schedule and extent of these improvements will impact highway 

congestion. As traffic to Las Vegas grows between now and 2012, congestion will get worse. 

However, some of the scheduled improvements will mitigate some of the expected congestion. 

The delay was tested under two conditions, to reflect varying rates of completion. In this 

analysis, it was assumed that the most optimistic 2012 future is that all the announced 

improvements will be completed by 2012. These improvements should accommodate growth 

between 2004 and 2012, but possibly may not reduce congestion from current levels. 

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Improvement levels that would result in lower congestion levels in 2012 were tested, and as 

expected resulted in a decrease in HSR use. It is further assumed that highway improvement 

projects not scheduled now, will not be complete by 2012. A large improvement project 

completed by 2012 (say, continuous widening of I-15 from Victorville to I-215 in Las Vegas) 

would result in a reduction in congestion, and a decrease in HSR ridership from the base level of 

4.1 million 2-way trips. 

Two levels of delay were tested. One assumes that improvements between Victorville and 

Barstow are complete, but all the improvements on I-15 between Barstow and Nevada are not 

compete. The additional delay for this scenario is 35 minutes. Keep in mind that this is in 

addition to current delay, and is an average between Northbound and Southbound traffic. 

(Sunday traffic to California is more congested than Friday northbound travel.) The worst-case 

scenario hypothesizes that all the planned improvements between Barstow and Victorville will 

not be completed by 2012, and meaningful improvements will not be completed on I-15 between 

Barstow and Nevada. The additional delay for this scenario is 55 minutes. 

Another factor is involved in evaluating the impact of congestion and delay on ridership. 

Currently, the worst congestion only occurs Friday and Saturday northbound and on Sunday 

southbound. Based on traffic counts at the Nevada border, it would appear that congestion does 

not extend throughout the day, even on Friday and Saturday. Currently all travelers to Las Vegas 

do not experience delay. Sixty-seven percent of the trips occur at a time during the week when 

there is no delay. Another 20% experience delay, but only 13% of the trips experience the worst 

case congestion. This measure of peaking was used to develop ridership estimates based on 

congestion. For instance, in the worst case congestion, 67% of the trips would not experience 

additional congestion so ridership for that group should be based on mode share reflecting no 

additional congestion. However, 20% of the trips would experience moderate congestion 

represented by the 35 Minute delay. Finally, 13% of the trips would experience severe 

congestion in line with the run made with the 50 minute delay. The composite of these trips is a 

forecast of 3.6 million trips diverted from auto. This ridership is higher than the 3.4 million trips 

forecast with no delay, but lower than the 4.1 million diversions that would be expected if heavy 

congestion were to occur every day all year round. These estimates should be used as ranges due 

to the uncertainty associated with the extent of future delay. 

Table 12 

 Ridership Depending on Congestion 

Modeled HSR from drive Normal Flow Moderate Congestion Heavy Congestion 

Conditions 100% Effect 67% 20% 13% 

Ridership Increases 

Only During Peak

Base Delay 3,439,319 2,302,216 697,710 439,393 3,439,319 

35 min 3,745,995 2,302,216 697,710 478,573 3,478,498 

50 min 4,058,569 2,302,216 759,923 518,506 3,580,645 

Estimated 2012 Ridership with Congestion Only During Peaks 

Assumed Status of I-15 HSR from Auto HSR from Bus HSR from Air Total HSR Trips 

All I-15 Improvements complete 3,439,319 260,187 460,750 4,160,255 

Barstow to Victorville Complete 3,478,498 263,151 460,750 4,202,399 

Planned Improvements Not Completed. 3,580,645 270,878 460,750 4,312,273 

Ridership variation with delay is shown below. The delay elasticities increase at a higher rate as 

delay gets longer. When 35 minute delay is added, the auto model elasticity is a relatively low 

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.094. However as the added delay nears an hour, the elasticity significantly increases to 0.168. If 

highway congestion would result in a 60 or 70 minute delay on a regular ongoing basis, the 

impact on ridership would be positive. As noted previously, as some improvements are already 

being carried out on I-15, this worst-case scenario does not seem likely at this point in time. Of 

course diversion from air is not impacted by highway congestion. 

Table 13 

Sensitivity of Ridership to Delay 

I-15 Delay HSR Fare $55 Ave. Friday 

HSR Trips 

Ratio, 

Annual to 

Pk Day 

Annual Trips 

By Mode Annual Trips HSR Market Share 

Reduction in From Auto 11,824 252 14,994,213 2,980,739 19.9%

Delay Induced Day Trips 1,533 252 386,299 2.1%

 From Air 2,747 168 2,074,520 460,750 22.2%

 From Bus 1,214 210 1,180,227 254,719 21.6%

 Totals 17,318 18,248,960 4,082,507 22.4%

No New Delay From Auto 12,077 252 14,994,213 3,044,727 20.3%

Induced Day Trips 1,566 252 394,592 2.2%

 From Air 2,747 168 2,074,520 460,750 22.2%

 From Bus 1,240 210 1,180,227 260,187 22.0%

 Totals 17,630 18,248,960 4,160,255 22.8%

From Auto 13,154 252 14,994,213 3,316,219 22.1%

Induced Day Trips 1,705 252 429,777 2.4%

Additional 35 

Min Delay 

From Air 2,747 168 2,074,520 460,750 22.2%

 From Bus 1,350 210 1,180,227 283,387 24.0%

 Totals 18,957 18,248,960 4,490,132 24.6%

From Auto 14,252 252 14,994,213 3,592,930 24.0%

Day Trips 1,848 252 465,638 2.6%

Additional 50 

Min Delay 

From Air 2,747 168 2,074,520 460,750 22.2%

 From Bus 1,463 210 1,180,227 307,034 26.0%

 Totals 20,310 18,248,960 4,826,352 26.4%

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D. Ridership and Revenue Forecasts 

Ridership and revenue forecasts are based upon a primary indicator of Las Vegas area economic 

activity—hotel, motel, and timeshare/condo availability and usage—and the base fare of $55.00 

per one-way trip presented previously. 

It is to be emphasized that the projections presented below are based upon specific sets of 

circumstances and conditions, as noted below. 

• A continuation of generally good economic conditions, both nationally and with 

respect to the Las Vegas urban areas; 

• The maintenance by Las Vegas of its current competitive position within the 

gaming/tourism field and the continued achievement of reasonable hotel/motel room 

occupancy rates; 

• The promotion and operation of the high-speed rail project with a dedicated 

management team in an efficient, customer-oriented manner, and the provision of a 

suitable promotion budget; 

• No government actions, at any level, which will adversely affect ridership and/or fare 

box revenues of the high-speed rail system. 

Information outlining recent and near-term future levels of Las Vegas lodging rooms is presented 

in the accompanying table. 

Table 14 

Projected Las Vegas Lodging Rooms(1,000’s) 

2004(1) 2005(2) 2006(2) 2007(2) 2008(2) 2009(2) Projected 

Remaining(2)

 

2003(3)

% 

Chg Amt. % Chg Amt. % Chg Amt. % Chg Amt. % Chg Amt. % Chg Amt. % Chg Amt.

Hotels/Motels 127.7 3 131.5 1 132.8 3 136.8 7.2 146.7 3.3 151.6 10.1 166.9

Timeshare/Condo/ Condo 

“Hotels”/ Rentals 

 4.7 14.9 5.4 44.4 7.8 55.1 12.1 59.5 19.3 8.3 20.9 108.1 43.5

Totals 127.4 3.9 132.4 3.4 136.9 2.7 140.6 5.9 148.9 11.5 166 3.9 172.5 22 210.4

(1) Actual rooms as of December 31, 2004.

(2) Projected rooms from December 1, 2005 Construction Bulletin.

(3) From URS Downtown Monorail Report.

The average growth rate for hotel/motel rooms between 2004 and 2009 is 3.5 percent annually. 

If the balance of the current projection, (based on the Las Vegas Construction Bulletin), takes 

roughly three years to occur, then this growth level is consistent with that future build-out of 

currently planned development. However, it is likely and prudent to consider future constraints 

on growth over the long term due to such factors as: development site availability; infrastructure 

needs and costs (including water); and the effects of competitive leisure time locations. It is thus 

reasonable to use, for long-term HSR forecasting purposes, a growth in hotel/motel rooms of 

somewhat under 3 percent per year for the 2010-2020 decade, 2 percent per year for 2020-2030, 

and 1 percent a year for 2030-2040. 

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The information summarized in the above table indicates that timeshares, condominiums, condo 

“hotels” and rental apartments will be constructed and utilized at a much faster rate than 

hotel/motel rooms over the ensuing decades. This is a clear market change from previous 

decades, but current activity strongly supports this shift in the composition of rooming stock in 

Las Vegas. Based upon a very low base level, the average 2004-2009 growth in 

timeshares/condos averages 36 percent annually. The “build-out” information indicates a 

slightly lower, but still very substantial, annual rate of development. Since the same constraints 

on overall Las Vegas area development over the succeeding decades that are noted above apply 

to timeshares/condos, it appears reasonable to assume a roughly 13 percent annual growth rate 

for the decade starting in 2010, 4 percent for 2020-2030, and 3 percent annually for 2030-2040. 

However, high-speed rail ridership generated by occupants of timeshares/condos needs to reflect 

the facts that a higher duration of stay in such units is most likely, as is a higher vacancy rate. 

Thus before combining the timeshare/condo and hotel/motel rates, the former growth factors 

should be reduced. In the weighting process of the hotel/motel and timeshare/condo rates, it is 

also useful to note that in 2004 timeshare/condo rooms amounted to approximately 4 percent of 

total rooms in the Las Vegas area, whereas in 2010 and the years immediately following, 

available information indicates that timeshares/condos will constitute approximately 20 percent 

of the Las Vegas room stock. 

Based upon the foregoing considerations, the basic growth in high-speed rail ridership is 

estimated at 4.0 percent annually for the decade starting in 2010, 2.3 percent annually for the 

2020-2030 decade, and 1.2 percent annually for the 2030-2040 period. Once the high-speed rail 

project is open to the public, there will be a period of time during which ridership is building up 

to forecast levels due to a lack of familiarity with the new service and an adjustment of travel and 

Las Vegas visitation habits. This is typically termed “ramp-up” and deductions from early year 

ridership levels are estimated based upon comparable facilities and experience elsewhere. In the 

case of the subject project, there are no “comparables” and it is thus prudent to use a ramp-up 

deduction of 40 percent during the first year of operation, 20 percent during the second year and 

nothing in the third and subsequent years. 

The following table presents annual ridership and revenue levels for the 2010-2040 period, based 

upon the considerations and factors outlined above. The revenue projections assume continued 

growth in the consumer price index (CPI) of 2 percent annually on the average, which is 

reflected in a fare increase of 6 percent every three years. Since the periodic increases are 

consistent with general price levels, elasticity deductions are not appropriate. The ridership 

assumes the base alternative where the Rio site is the HSR station in Las Vegas, and Victorville 

is the only station in California. Consequently, ridership for a possible Barstow station is not 

included. 

Page 15

F-D-51
Table 15 

HSR Ridership and Revenue 

 Round Trips by Previous Mode in Millions Fare Revenue 

Year Growth From Auto Day Trips From Air From Bus Total Ridership Round Trip 

CPI Adj. 

Values in 

Millions 

2010 4.00% 1.374 0.178 0.208 0.117 1.877* $ 110.00 $ 206.5

2011 4.00% 2.363 0.306 0.358 0.202 3.229* $ 110.00 $ 355.1

2012 4.00% 3.045 0.395 0.461 0.260 4.160 $ 110.00 $ 457.6

2013 4.00% 3.167 0.411 0.479 0.271 4.327 $ 116.73 $ 505.1

2014 4.00% 3.293 0.427 0.498 0.281 4.500 $ 116.73 $ 525.3

2015 4.00% 3.425 0.444 0.518 0.293 4.680 $ 116.73 $ 546.3

2016 4.00% 3.562 0.462 0.539 0.304 4.867 $ 123.88 $ 602.9

2017 4.00% 3.704 0.480 0.561 0.317 5.062 $ 123.88 $ 627.0

2018 4.00% 3.853 0.499 0.583 0.329 5.264 $ 123.88 $ 652.1

2019 4.00% 4.007 0.519 0.606 0.342 5.475 $ 131.46 $ 719.7

2020 2.30% 4.099 0.531 0.620 0.350 5.601 $ 131.46 $ 736.3

2021 2.30% 4.193 0.544 0.635 0.358 5.730 $ 131.46 $ 753.2

2022 2.30% 4.290 0.556 0.649 0.367 5.861 $ 139.51 $ 817.7

2023 2.30% 4.388 0.569 0.664 0.375 5.996 $ 139.51 $ 836.5

2024 2.30% 4.489 0.582 0.679 0.384 6.134 $ 139.51 $ 855.7

2025 2.30% 4.592 0.595 0.695 0.392 6.275 $ 148.05 $ 929.0

2026 2.30% 4.698 0.609 0.711 0.401 6.419 $ 148.05 $ 950.4

2027 2.30% 4.806 0.623 0.727 0.411 6.567 $ 148.05 $ 972.2

2028 2.30% 4.917 0.637 0.744 0.420 6.718 $ 157.11 $ 1,055.5

2029 2.30% 5.030 0.652 0.761 0.430 6.873 $ 157.11 $ 1,079.7

2030 1.20% 5.090 0.660 0.770 0.435 6.955 $ 157.11 $ 1,092.7

2031 1.20% 5.151 0.668 0.779 0.440 7.039 $ 166.72 $ 1,173.5

2032 1.20% 5.213 0.676 0.789 0.445 7.123 $ 166.72 $ 1,187.6

2033 1.20% 5.275 0.684 0.798 0.451 7.209 $ 166.72 $ 1,201.8

2034 1.20% 5.339 0.692 0.808 0.456 7.295 $ 176.93 $ 1,290.7

2035 1.20% 5.403 0.700 0.818 0.462 7.383 $ 176.93 $ 1,306.2

2036 1.20% 5.468 0.709 0.827 0.467 7.471 $ 176.93 $ 1,321.9

2037 1.20% 5.533 0.717 0.837 0.473 7.561 $ 187.76 $ 1,419.6

2038 1.20% 5.600 0.726 0.847 0.479 7.652 $ 187.76 $ 1,436.6

2039 1.20% 5.667 0.735 0.858 0.484 7.743 $ 187.76 $ 1,453.9

2040 1.20% 5.735 0.744 0.868 0.490 7.836 $ 199.25 $ 1,561.4

 Total Revenue 30 Year Period, Current Dollars $ 28,629.9 

* Includes ramp-up deduction 

Page 16

F-D-52
F. Model Development and Documentation 

1. Southern California to Las Vegas Travel Market 

The first step in estimating market share for a proposed project is to establish the size of the total 

market. When the market estimate for the previous study of high speed rail between Victorville 

and Las Vegas was being carried out, recreation travel had not yet stabilized after the impacts of 

9-11. Now, Las Vegas visitation is finally beginning to grow again, and the impacts of airport 

security measure implemented after 9-11 have stabilized. To the extent possible, the assumptions 

for this study have been updated to reflect current conditions. In most cases the revised data is 

from 2004. 

There was some difficulty in establishing the highway control for travel between Southern 

California and Las Vegas. The main source of ambiguity had to do with auto trips. The difficulty 

was in rationalizing the traffic counts of vehicles entering Nevada from California with the Las 

Vegas Convention and Visitor Authority’s estimate of Las Vegas visitors from Southern 

California. When traffic counts were used to measure auto travel, total trips exceeded the 

LVCVA visitor estimate. 

Since the ambiguity revolved around the question of what share of the I-15 vehicles had Las 

Vegas destinations, it w4as decided to do a survey of I-15 users. The survey was carried out at 

Baker, California a/o November 10, 11, and 12, 2005. Three types of data were collected; 

Mainline Classification Counts, Ramp License Plate Counts, and Interviews at the parking lots of 

service of stations and restaurants. The table below shows an estimate of the share of I-15 

vehicles that have destinations in Las Vegas. The table combines information from the various 

surveys mentioned above. Counts were not taken for the complete 24 hour period, so the values 

were expanded using 24 hour I-15 count data. The number of vehicles was converted to annual 

trips by using a ratio between summer average Friday traffic and annual travel. 

Table 16 

I-15 Survey Expanded to Annual Auto Visitor Control 

Survey Expanded 

To 24 Hr 

Total Daily 

Vehicles 

Privately Owned 

Vehicle 

Share POV California Plates Less Through 

Traffic 

LV Visitor 

Share of I-15 

Mon-Thur 19,199 16,343 85.1% 13,565 12,670 77.5% 

Fri 33,782 30,943 91.6% 27,601 25,779 83.3% 

Sat 19,497 17,468 89.6% 14,655 13,688 78.4% 

Sun 39,408 37,416 94.9% 34,311 32,046 85.6% 

Total Weekly 169,483 151,201 122,193 

Ave Day of Week 24,212 21,600 89.2% 17,456 80.8% 

2004 Count, California 

Nevada Border Tot Vehicles Annual I-15  Auto 

Annual to Las 

Vegas 

Share to Las 

Vegas 

Annual 1-way Vehicles 7,475,291 6,668,934 5,389,500 72.1% 

The mainline counts established that autos made up 89.2% of the I-15 traffic. Based on the ramp 

survey, it was determined that 80.8% of the autos and other privately owned vehicles had 

California plates. Using origin destination data collected in the interview survey, the share of 

vehicles with destinations other than Las Vegas was established. These trips were removed, as 

shown in the table above. Based on these reductions, it was determined that 72.1% of the 

Page 17

F-D-53
vehicles on I-15 carried passengers with destinations in Las Vegas. The average occupancy of 

the vehicles surveyed was 2.46 persons per vehicle; and the average number of daily one-way 

persons traveling from California to Las Vegas was 36,324. 

The survey showed that not all the California trips on I-15 come from Los Angeles, Orange 

County or Southern San Bernardino County. Seven percent of the vehicles came from San 

Diego. Another 8.4% came from north of Los Angeles. Surprisingly, more than half of these 

northern trips come from Sacramento, Oakland and places even further north. A somewhat 

smaller share comes from Bakersfield, Fresno, and other closer in areas. The Barstow area 

generates 2.9% of the I-15 trips. The San Diego trips can use the Victorville station. However, it 

is not reasonable to expect that the trips from Barstow and north would use the Victorville 

station. They could use a Barstow Station and so the Barstow station was evaluated. 

Table 17 

I-15 Survey Information Converted to Annual 

2004 S. California to Las Vegas 

Travel Estimate 

Survey Based 

 Estimate 

Origin Share of I15 Trips 

I-15 AADT at Border (1-way) 7,475,291 

Visitor Share of I-15 Count 72.1% 

Annual Auto Visitor Vehicles 5,389,500 

Persons per Auto 2.46 

Annual Auto Visitor Persons 13,258,169 

Auto LA Metro 10,435,211 78.7% 

Victorville Area 408,842 3.1% 

Barstow / North 379,639 2.9% 

Bakersfield 262,827 2.0% 

Bishop 116,812 0.9% 

Fresno 87,609 0.7% 

Sacramento/Folsom 311,499 2.3% 

Oakland/San Francisco 214,155 1.6% 

Red Bluff/Anderson 116,812 0.9% 

Auto San Diego 924,762 7.0% 

Annual Auto Visitor Persons 13,258,169 100% 

Less Day Trips -1,687,403 

Stay Other Than Hotel/Motel -2,892,691 

Auto Visitors Consistent with LVCVA Visitor Estimates 8,678,074 

Air (LA Metro Only) 1,573,315 

Bus (LA Metro Only) 897,035 

Total Visitors from Southern California 11,148,424 

2004 Las Vegas Visitors 37,388,781 

S. California Share 29.8% 

An important part of the interview survey was to determine how much of the travel to Las Vegas 

would be day trips, and what share of the other visitors do not stay at hotels or motels. The 

survey showed that 12.7% of the trip makers were day trips, and another 21.8% of the trip 

makers did not use public overnight accommodations. This explains why auto based trip makers 

can not be reconciled with the Las Vegas Convention and Visitor Authority's (LVCVA) visitor 

estimate. LVCVA assumes that only 1% of their visitors are day-trippers. Clearly, that 1% does 

not apply to California visitors. In addition, LVCVA does not count visitors who stay in 

timeshares, condominiums or at friend’s houses. 

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F-D-54
It can be see from the I-15 survey that these uncounted visitors are significant. It is possible that 

trip makers who do not stay at public accommodations are less likely to utilize Las Vegas 

entertainment facilities. This possibility may contribute to the observation that there are more 

auto trips to Las Vegas than the LVCVA annual estimate for California visitors. When 

comparing the study estimate to the LVCVA estimate of visitors from Southern California, these 

auto trip types should not be included since they would likely not be included in the LVCVA 

Visitor Profile estimates. The table above shows that with these trips removed, the study estimate 

of the Las Vegas market is 29%. 

New visitor controls were established based on the survey, and a revised California to Las Vegas 

trip table was developed. These can be considered as one-way trips or round trips. The total 

average Friday market for California to Las Vegas travel was 56,700 daily trips, or 14.24 million 

annual trips. Of these, 11.77 million were by auto, 1.57 million by air, and 0.9 million by bus. 

The bus volume was based on the classification counts taken at Baker and is higher than was 

used in previous visitor controls. The air travel estimate is described below. These numbers do 

include the San Diego area, but do not include autos bound for Barstow and points north. There 

were 1.49 million trips from Barstow, Bakersfield, Sacramento, Oakland and so on. These trips 

can only be served by Desert Xpress service if there is a Barstow station. The air and bus 

markets were limited to the tributary areas of the five Los Angeles area airports. 

Air travel to Las Vegas from California airports was updated using the FAA 2004 airport-toairport travel survey and 2004 airport activity reported by individual California airports. The 

passenger activity at McCarran airport (LAS) in Las Vegas was also used in the analysis. The 

FAA travel survey is developed from a 10% sample of airline passengers. However, an analysis 

of the data showed that not all airports had a ten percent sample. Because of this, the FAA origin 

destination data was used to establish the percentage of trips headed to LAS from each airport. 

The percentage was then multiplied by the annual air traffic activity reported by each airport for 

2004. The Table below shows the estimated trips to Las Vegas by each of the study area airports 

Table 18 

Air Travel Control for 2004 

Traffic to Las Vegas (LAS) Annual Pk Month Pk Week Weekend 

Day 

LAX Los Angeles 745,569 65,610 14,815 4,445 

LGB Long Beach 82,605 7,269 1,641 492 

BUR Burbank 326,145 28,701 6,481 1,944 

ONT Ontario/San Bernardino 205,426 18,077 4,082 1,225 

SNA Santa Ana 213,571 18,794 4,244 1,273 

LA Metro Area Subtotal 1,573,315 138,452 31,263 9,379 

2. Airport and Highway Measures of Congestion and Delay 

The previous study did not explicitly take into account security checkpoint wait times and other 

expected delays, which can occur at individual airports. Data describing these delays were found 

from a number sources. Probably the most reliable data are the Security Checkpoint Wait times 

provided by the Transportation Security Administration. Another important source of delay data 

is the Delta customer advisory information provided at their web site. In addition to these 

sources, surveys carried out by Travelocity since 9-11 were also used. Data was usually available 

Page 19

F-D-55
for peak and off peak travel times. The difference between these two measures was used to 

quantify airport delay. 

Table 19 

Airport Delay Measures 

Peak Off Peak 

 

Airport Delay Used 

In Model Delta Study TSA 

Times Delta Study TSA Times 

Airport 

Off Pk 

Airport 

Time 

Peak 

Airport 

Time

Airport 

Delay

Total 

Curbside 

Counter 

Security 

Total 

Curbside 

Counter 

Security 

Burbank (BUR) 13.0 21.5 8.5 21.5 5 15 9 13 5 10 3 

Los Angeles (LAX) 27.5 59.5 32.0 59.5 55 60 20 27.5 25 30 9 

Ontario (ONT) 13.5 35.0 21.5 35 10 15 20 13.5 5 5 6 

Orange County (SNA) 13.0 39.0 26.0 39 20 30 9 13 5 10 3 

Las Vegas (LAS) 10.0 30.0 20.0 30 15 30 20 10 5 10 6 

In this study, highway travel times are calculated using a highway network provided by the 

Orange County Transportation Authority (OCTA). For this study update, an effort was made to 

update the congested speeds on the network. In order to quantify Los Angles area congestion 

(including parts of Ventura, Orange, and San Bernardino Counties) data was collected from a 

number of sources. The first step was to take Southern California locations published in a ranking 

of the 25 worst congestion locations throughout the United States. There were seven study area 

locations in this reports sponsored by American Highway Users Alliance. The worst intersection 

in the U.S. was reported to be US 101 (Ventura Freeway) at the I-405 interchange. 

After these recurring congestion locations were identified, an effort was made to identify 

segments of freeways where delays tend to occur. The best discussion of congestion was a list of 

segments “Freeways to Avoid” found on the Total Escape Web Site. When clarification was 

needed for some locations, real time traffic reports of speed and delay provided by Sigalert were 

used. Highway travel time was then calculated from every study area ZIP to nearby commercial 

airports and to Victorville. Travel times were calculated twice, once using the typical speeds 

found in the OCTA model and then using the speeds from the updated congestion network. The 

difference between the two sets of travel times was used as the measure of traffic delay. 

3. Southern California Demographic Analysis 

Las Vegas trip ends are allocated to Southern California ZIP codes based on population. The 

population in the ZIP level database was updated to 2004. This update was based on a report 

developed by the State of California, Dept of Finance, Demographic Research Unit. In some 

places population changed significantly. Much of this growth occurred in the County of San 

Bernardino. Victorville, Apple Valley and Hesperia were among the fast growing cities. 

Population projections were also developed for 2012. Population projections by County were 

also available from the Dept of Finance. In order to apply the county forecasts to cities within the 

counties, the assumption was made that cities showing higher growth between 2000 and 2004 

would continue to grow through 2012. While this effort was made to capture any change in trip 

origin distribution due to development, there was no noticeable change in distribution between 

Page 20

F-D-56
2004 and 2012. This may be because the trips coming from heavily developed areas probably 

“overpower” the faster growth occurring around the edges. 

There were three levels of analysis used in updating trips to Las Vegas from Southern California. 

First, total trip controls were established for auto and air travel. Then, these trips were allocated 

to districts call Super Zips. This allocation process used trip generation and distribution values 

established in a number of surveys. Charter bus estimates were added at this point. Finally the 

trips were allocated by mode to each ZIP in the study area using the population information 

described above. 

4. Web Survey Used to Assess HSR Market 

A web based panel survey was carried out to determine how strongly people would value the 

reliability, fun, comfort, convenience, and perceived safety of HSR as compared to the auto and 

air modes. The survey incorporated a stated preference element and analysis using statistical 

modeling methods. The survey sample of 400 was drawn from Resource Systems Group Survey 

Café Panel, a nationally-representative participation panel with industry-leading response rates 

that has been used for numerous high-profile survey projects. The proposed high speed rail 

service was described and respondents were asked how likely they would be to make trips to Las 

Vegas using this service. 

The stated preference survey was then used to determine the conditions under which travelers 

would use the proposed HSR service. This information was then analyzed using statistical 

analysis techniques. This resulted in two-mode choice models estimated from the survey results. 

The first was a model for binary choice between Auto Travel and the Las Vegas HSR. The 

second was model for binary choice between Air Travel and Las Vegas HSR. The following 

three tables provide a documentation of these models. 

Table 20 

Mode Choice Parameters, Las Vegas HSR Stated Preference Survey 

Model Application Inputs

HSR 

Travel time (Victorville to Las Vegas) 105 minutes used in SP survey 

Fare (per person, one way) $40-$70 range tested in SP survey 

Frequency of service Pick from list of levels tested in SP survey 

Access cost per mile (by auto) $0.25-$0.29 range tested in SP survey 

Average minutes delay for HSR trip This is for whole trip 

Egress time in Las Vegas (average in minutes) 10-20 minutes tested in SP survey 

Egress cost in Las Vegas (average in dollars) $5-$10 tested in SP survey 

Auto 

Average party size for current auto travelers 2.70 average from sample used for model estimation 

Median household income for current auto travelers $70K is midpoint of median income of sample used 

Travel time Victorville to Las Vegas (minutes) 165 minutes 

Distance Victorville to Las Vegas (miles) 188 miles 

Cost per mile $0.25-$0.29 range tested in SP survey for auto access

Average minutes delay for auto trip This is for whole trip 

Page 21

F-D-57
Air 

Average party size for current air travelers 2.31 average from sample used for model estimation 

Median household income for current air travelers $70K is midpoint of median income of sample used 

Flight time (Origin Airport to Las Vegas) 64 minutes average from sample used for model 

Average air fare (per person, one way) $73.25 average from sample used for model estimation

Average access cost (per person, one way) $6.88 average from sample used for model estimation 

Average minutes delay for air trip This is for whole trip 

Egress time in Las Vegas (average in minutes) 30-45 minutes tested in SP survey 

Egress cost in Las Vegas (average in dollars) $5-$10 tested in SP survey 

Table 21 

Binary Choice Model between Auto Travel and Las Vegas HSR 

Variable Label Units Estimate Standard 

Error T-Ratio Value Of Time 

Travel Time Time Minutes -0.006 0.001 -6.4 $ 9.00 

Cost of Travel Cost $* -0.410 0.023 -18.2 

Reliability (Average Minutes Late) RelAvMins Minutes -0.004 0.005 -0.9 

Distance to Victorville AcDist Miles -0.008 0.003 -1.0 

Frequency – 2 per Hour Freq2pH (0,1) 0.722 0.138 5.2 

Frequency – 1 per Hour Freq1pH (0,1) 0.606 0.136 4.4 

Frequency – 1 per 2 Hours Freq1p2H (0,1) 0.348 0.139 2.5 

Frequency – 1 per 4 Hours Freq1p4H (0,1) 0.000 0.000 0.0 

Curent Mode Constant cmcon (0,1) 0.188 0.277 0.7 

Number of Observations 2400

Number of Respondents 300

Log Likelihood -1304.18

*Cost of travel is total cost of travel divided by party size 

Cost is also divided by natural log of income, where income is household income in $ 

** VoT is evaluated at $70,000 per annum household income 

Notes: 

Reliability is specified for just the auto alternative and is in terms of average minutes late. It has the correct sign 

and a reasonable magnitude (i.e. it is similar to the other time coefficient in the model), but it is not statistically 

significant 

Sensitivity to in-vehicle time was found to be statistically similar to access and egress time

Page 22

F-D-58
Table 22 

Binary Choice Model between Air Travel and Las Vegas HSR 

Variable Label Units Estimate Standard 

Error 

T-Ratio Value 

of Time** 

In-Vehicle Time IVTime Minutes -0.012 0.005 -2.4 $ 30.44

Access, Wait and Egress Time AcEgTime Minutes -0.025 0.004 -5.6 $ 60.69

Cost of Travel Cost $* -0.104 0.015 -7 

Reliability (Average Minutes Late) RelAvMinutess Minutes -0.018 0.020 -0.9 

Curent Mode Constant cmcon (0,1) -0.243 0.317 -0.8 

Number of Observations 408

Number of Respondents 51

Log Likelihood -204.21

*Cost of travel is total cost of travel divided by party size and divided by log of household income in thousands 

The model was significantly improved by transforming cost by 1/log (income), indicating that cost sensitivity is 

inversely proportional to the log of income 

Notes: 

Reliability (represented here as average minutes late) has the correct sign and a reasonable magnitude (i.e. it is 

similar to the other time coefficient in the model), but it is not statistically significant 

A specification has not been found that shows frequency to be statistically significant 

5. Model Sensitivity Analysis Statistics 

Research Systems Group carried out a sensitivity analysis on the air model and the version 3 auto 

model (without the income effect), the results for which are shown in the following Table. 

Random numbers were drawn independently for each iteration for each coefficient from a 

univariate normal distribution. There were 1000 iterations run. 

The results show that the market shares estimated using the models have a 95% confidence 

interval around them of +/- 8.5% for the auto model and +/- 8.4% for the air model. 

Table 23 

Sensitivity Analysis Statistics 

 Auto Share HSR Share vs. 

Auto Air Share HSR Share vs. Air 

95% Confidence Interval: 1.96*St Dev 8.5% 8.5% 8.4% 8.4% 

Lower Bound of 95% Confidence Interval 62.3% 20.6% 51.6% 31.6% 

Upper Bound of 95% Confidence Interval 79.4% 37.7% 68.4% 48.4% 

Standard Deviation of iteration results 4.4% 4.4% 4.3% 4.3% 

Minimum of iteration results 56.2% 16.9% 46.4% 27.8% 

Maximum of iteration results 83.1% 43.8% 72.2% 53.6% 

Number outside 95% Confidence Interval 46 46 46 46 

% of iterations outside Confidence 

Interval 4.6% 4.6% 4.6% 4.6% 

Page 23

F-D-59
DESERTXPRESS RIDERSHIP &

REVENUE AUDIT

Technical Memorandum: FRA Summary

September 2007

Prepared for: Prepared by:

Federal Railroad Administration

On behalf of: Iridium Concesiones de

Infraestructuras / (Grupo ACS)

Avda. de Tenerife 4 y 6

San Sebastian de los Reyes

28700 Madrid

SPAIN

Steer Davies Gleave

28-32 Upper Ground

London

SE1 9PD

+44 (0)20 7919 8500

www.steerdaviesgleave.com

F-D-60
Technical Memorandum: FRA Summary

P:\Projects\7500s\7502\Outputs\Reports\207502 Executive Summary FRA v2.0.doc

Contents

Contents Page

SUMMARY 1

Introduction 1

The Project 2

The URS Forecasts 2

The Total Travel Market 3

Traffic Capture 4

The SDG Analysis 4

Steer Davies Gleave Forecasts 6

Key Risks 7

Parking Arrangements in Victorville 8

Review of Systems Operations 8

F-D-61
Technical Memorandum: FRA Summary

P:\Projects\7500s\7502\Outputs\Reports\207502 Executive Summary FRA v2.0.doc

1

SUMMARY

Introduction

1. ACS Infrastructure North America (ACS), a division of Iridium Concesiones de

Infraestructuras S.A., one of the world’s largest transportation concession and

construction companies, is considering participation in the DesertXpress project. As

part of their analysis, they commissioned Steer Davies Gleave (SDG) to carry out a

review/audit of the traffic and revenue forecasts prepared by URS on behalf

DesertXpress Enterprises Inc. (DXE), in support of the Victorville California to Las

Vegas High Speed Train referred to as the DesertXpress.

2. DXE has informed ACS and SDG that the Federal Railroad Administration (FRA) is

conducting an independent peer review of the draft URS study to “analyze the

soundness of the technical approach and data collection effort, the likelihood of the

assumptions on which the forecast is based, and the reasonableness of the results”.

3. In this context, DXE has asked ACS and SDG if they might prepare a technical

memorandum summarising the review/ audit which can be passed to the FRA to assist

them, and has stated that they have authorized ACS to release such memorandum

directly to FRA so the FRA can provide this memorandum to their own independent

consultants, Cambridge Systematics.

4. In response, SDG have prepared this document. It should be noted that the SDG report

to ACS/Iridium is considered by both ACS/Iridium and DXE to be proprietary and

confidential. This memorandum consists of non-proprietary excerpts from the

Executive Summary of the report that are germane to the scope of the FRA’s

independent peer review in support of the EIS.

5. The scope of work of the SDG assignment for ACS/Iridium included several tasks

which appear to be relevant to the scope of the FRA’s independent peer review for the

Environmental Impact Statement (EIS), including the following:

• Review the URS ridership and revenue report and the data collected by URS for

its study, including observed economic and traffic data (car, airplane and bus) and

Stated Preference surveys undertaken;

• Review the methodology used by URS to evaluate ridership capture rates,

competing modes and forecast ridership and growth factors;

• Identify key risks and produce a report with tasks carried out and conclusions.

This memorandum summarizes the above SDG tasks.

6. In addition to the above audit of the forecasts, Iridium also requested advice on:

• Operations (train plan, number of trains needed);

• Other potential sources of revenues (advertising, stations retail, etc); and

• The car park size and access junctions’ capacity at Victorville station.

This memorandum summarizes this advice, and includes DXE’s comments to the

SDG advice in these areas of investigation.

F-D-62
Technical Memorandum: FRA Summary

P:\Projects\7500s\7502\Outputs\Reports\207502 Executive Summary FRA v2.0.doc

2

The Project

7. The DesertXpress High Speed Train is to run from Victorville, CA to Las Vegas, NV.

Victorville is 80 miles northeast of downtown Los Angeles and located on the

existing I-15 highway running between LA and Las Vegas. All drivers travelling from

Southern California to Las Vegas must pass Victorville.

8. The train service will run at about 125 mph and allow an end to end Las Vegas to

Victorville journey time of less than two hours. Trains will be operated by 10-car

train sets and have initially been assumed to depart every day of the year from 6 am to

10 pm, running every 20 minutes from Friday to Sunday and every hour during the

rest of the week.

9. Various facilities and services would be provided on board the train and at Victorville

station, where users will also be able to park their car at no additional cost. These

services could include hotel check-in facilities, baggage services, valet parking and

entertainment.

The URS Forecasts

10. Passenger forecasts were developed by URS for the proposed high-speed rail service.

11. These forecasts were produced using a spreadsheet-based forecasting model which

looked at the mode capture of different flows to Las Vegas for car, air and rail travel.

12. In order to estimate the capture rate of the various modes, a suite of binary logit choice

models was used. These models relied on coefficients derived from an internet-based

stated preference (SP) survey of selected Southern California residents, carried out by

Resource Systems Group Inc. (RSG).

13. Since the URS report was prepared before the probable decision to operate the Regina

EMU train sets, which are able to achieve a run time of close to 100 minutes, the

assumptions underlying the URS Base Case were a $55 one-way fare (2012 prices)

and a rail journey time of 116 minutes from Victorville to the future Rio station (or

other possible locations in Las Vegas, where a projected monorail branch may also

provide a link to the main monorail line which presently runs parallel to the Las Vegas

Strip).

14. In this base case, it was estimated that the total 2012 market between Southern

California and Las Vegas was 18.2 m round trips. It was then projected that the

DesertXpress would capture 22.8% of the total market.

15. Additionally, it was estimated that the new HSR service would generate a further 0.4

m (day) trips, giving a total traffic on the HSR of 4.16 m round trips per year. These

forecasts are summarised in Table 1 and Table 2 overleaf.

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TABLE 1 URS 2012 BASE CASE FORECAST OF DX RIDERSHIP

2012 Trips By

Mode no DX

2012 Market

Share no DX

2012 Trips by

Mode with DX

2012 Market

Share with DX

Car 14,994,213 82% 11,949,486 64%

Air 2,074,520 11% 1,613,770 9%

Bus 1,180,227 6% 920,040 5%

DesertXpress -- -- 4,160,412 22%

TOTAL 18,248,960 100% 18,643,708 100%

Source: URS Ridership and Revenue Study, Table 1, SDG Analysis

TABLE 2 URS 2012 BASE CASE FORECAST OF DX RIDERSHIP

Diversion to DX Annual DX Trips Capture DX Demand

From Car 3,044,727 20.3% 73%

From Air 460,750 22.2% 11%

From Bus 260,187 22.0% 6%

Induced Day Trips 394,748 9%

TOTAL 4,160,412 22.8% 100%

Source: URS Ridership and Revenue Study, Table 1, SDG Analysis

The Total Travel Market

16. In order to estimate the size of the total market of travellers between Southern

California and Las Vegas, for the I-15 highway mode, URS relied on NDOT traffic

count data to determine the total average daily traffic on the I-15 but found some

differences compared to travel information published by the Las Vegas Convention

and Visitors Authority. In an effort to understand those differences and develop

reliable highway mode travel data, a roadside survey was carried out at the access/

egress from the service station on the I-15 at Baker to determine the following

information about the car market:

• The percentage of trucks;

• The visitor share of traffic;

• Average vehicle occupancy; and

• O-D patterns, including the percentage of motorists whose final destinations were

within the Las Vegas area.

17. These results are reported by URS in Table 17 of their report. We note that URS then

ensured that this data reflected LVCVA visitor estimates.

18. Trips by air from Southern California to Las Vegas were estimated using 2004 airport

travel surveys.

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19. We agree that the estimates prepared by URS for the total volumes in the base year

and for the growth rates for subsequent years appear reasonable. We do note,

however, that in our analysis we have applied a reduction to base demand levels to

reflect the number of trips which we believe are undertaken during those hours when,

under the presently proposed operating plan, the service is not operated.

(Subsequently, DXE has indicated that it would of course be possible to develop a

different operating timetable which would include the operation of trains later into the

evening – and thus capturing further passengers.)

Traffic Capture

20. In order to estimate the rate of capture of traffic from each of the different competing

modes, a suite of binary logit choice models was developed. These models were

estimated using analysis carried out by RSG, including an internet-based stated

preference (SP) survey carried out among residents of Southern California.

21. We believe that these surveys do give useful insights into the potential of capture of

the new rail service; however, we do have some doubts about the robustness of the SP

analysis. The number of choice attributes included was high, which made the SP

exercise very complex. However, the selection of attributes was conventional –

focussing on time and cost – with a complicated representation of trip length

reliability. Overall, this has resulted in 46% of respondents non-trading (not varying

their choice between rail and current mode) which we feel is high.

22. It is not clear that the attribute set incorporated in the analysis in reality encompassed

the true factors defining the user’s choice (i.e. the softer factors of comfort/ the onboard entertainment etc.). Perhaps as a result, the only attributes to which respondents

reacted strongly were variations in the cost of the service.

23. Finally, the scaling parameter derived from the SP analysis and incorporated directly

into the mode choice model was very small. This ensures that the curve describing the

change in capture with change in relative generalised cost is very flat and makes the

forecasting model irresponsive to small changes in generalised time. The results

obtained from the model were very sensitive to the choice of the scaling factor itself.

The SDG Analysis

24. In order to gain more understanding – Who travels and how? – we arranged for a

specialist company to carry out a second internet based survey. This small survey was

again targeted at adult residents of counties in Southern California who had travelled

to Las Vegas within the past year. The key objectives were to gain further

understanding of how people perceived the current travel arrangements to/from Las

Vegas and to understand what factors might influence their choice to use the proposed

DesertXpress rail service.

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How do people get to Las Vegas now and what do they think of it?

25. At the moment, those travelling to Las Vegas by car do so because of cost and

convenience. People travelling by plane did so to obtain a faster journey (and also

because they found that mode convenient). They agree that the drive can be stressful,

but satisfaction levels for getting to Las Vegas are very high both for car and plane.

Few respondents in our survey claim to have experienced significant congestion on the

road or at the airport. In the URS study, it is estimated that 20% of travellers

experienced some congestion on I-15 and 13 % significant congestion.

26. The majority of car drivers do at present use their cars to travel around Las Vegas

during their stay, although only a small proportion say that this is necessary – and that

this alone would stop them from changing mode and using the train.

27. However, within the sample there is a group of people amongst both car users and

flyers who would not consider travelling to Las Vegas in any other way than they do

at present.

28. The responses in the survey tend to suggest that travellers are broadly happy with their

present choices. To attract traffic, DesertXpress will have to rely to a large extent on

the “pull” factors the service would offer. This has already been understood by DXE

from their own focus groups - and is consistent with their plans as they have reported

them to us.

What do people think of the DesertXpress service?

29. Across the board there was broad support for the DesertXpress proposal; most

respondents rated it a good or very good idea and were willing to consider using it.

However, as mentioned above, around a fifth of our sample was not interested in the

service.

30. To capture passengers the “pull factors” need to be maximized, i.e., it is essential that

the service includes all the possible added services. The inconvenience of having to

change mode in Victorville must be reduced to a minimum; free and secure parking at

Victorville should be provided and as many elements as possible which add to the

seamlessness of the trip (free hotel shuttle services and available taxis at the arrival

station in Las Vegas) must be integrated in the DesertXpress concept.

31. Key facilities such as the ability to book tickets in advance or as part of a hotel

package, provision on board of food, entertainment, the possibility to book a Las

Vegas show, internet access and the possibility of having your luggage checked in at

the station and delivered directly to your hotel room were important factors

influencing people’s decision to use the service. As we noted above, although these

factors do not play a significant role in the forecasting models, DXE have stated to us

that they also had identified these requirements and were committed to meet them as

far as possible.

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Steer Davies Gleave Forecasts

32. In our conversations with URS and RSG, and in our review of the reports with which

we have been provided, we have been able to confirm that the methodology developed

by URS for the analysis of the potential traffics for the DesertXpress is in line with

that commonly adopted. As such, it is seen to be appropriate for the first phase of the

project development process, which includes initial financial planning and

environmental impact analysis. For the purposes of more detailed financial planning

and the preparation of the final plan of finance, additional detailed work will be

needed to prepare what are commonly referred to as “Investment Grade” forecasts.

33. In our brief in our assignment for Iridium, however, we have been asked to move

forward from the initial forecasts and provide to our client a further analysis of the

forecasts and the risks around them. These estimates were prepared for the purpose of

proprietary financial planning/sensitivity analysis, and as such they are not themselves

included in the document.

34. In the development of these forecasts, we have retained in great part the

assumptions/parameters developed by URS. In particular:

• We have retained the overall estimates of the total market for travel between

South California and Las Vegas for the base and future years.

• However, in the light of the presently proposed operating schedule, we have

reduced ‘In Scope Demand’ to reflect the proportion of trips likely to be met

outside the planned operating hours of the DesertXpress

• We have assumed a train time of 100 minutes and a fare of $55 each way in 2012

prices ($50 in 2007 prices).

• We have retained the capture model structure developed by URS, but have

adjusted some of the inputs in the light of our own analysis for the purpose of

assisting ACS with their preparation of alternative proprietary project financing

scenarios.

• We have taken a more conservative view on the amount of traffic that might be

captured from bus.

• We have adjusted downwards the estimated level of induced number of day trips.

35. Our forecasts assume a full range of services including dining, entertainment, hotel

and luggage check-in, internet and retail facilities. In addition, we assume an as

seamless and stressless trip as possible, which can be ensured by offering free and

secure parking with valet, frequent trains and abundant connecting possibilities from

the arrival station to the final destination, such as courtesy shuttle buses.

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Key Risks

36. In addition to the envelope of risk around these forecasts (inherent in the nature of the

forecasting process and the development of traffic forecasting and capture models), we

have identified a number of exogenous factors which might impact on the market

position of the DesertXpress service.

• Upgrade of the competing road infrastructure

 As discussed, there are some plans for the upgrade of the I-15. However,

those upgrades scheduled for the years up to 2012 have already been

included in the Base Case. These improvements allow the present levels of

service to be maintained despite traffic growth. In this context, it is not

regarded as likely that further investment could be made to further increase

the modelled performance of the road.

• DXE states that they are unaware of any funded projects in the pipeline in either

state that would add any significant mileage of general traffic lanes – only truck

climbing lanes in certain locations.

 Competing (Amtrak) rail service

• Although discussed in the past, the project to run Talgo-type train sets over

refurbished tracks was shelved in the early 2000s It is DXE’s understanding that

there is no funding and no proposal on the table for such service

 Even if such a service were to be built were built, it would be difficult

(impossible on the original plans) for Amtrak to put in place a service which

could compete with DXE in terms of speed and quality.

• Competing (Maglev) rail service

 When this project was initially discussed, only some $45 million out of the

$1.3 billion required for the first section could be raised. It is very unlikely

that such a scheme could ever be financed.

Possible Upsides

37. Steer Davies Gleave have identified the following key upsides to the DesertXpress

project:

• Fuel price increase

 Likelihood: high

 Impact: medium, if the train fare remains unchanged, DesertXpress will

become more competitive than air or car

• More scope for day trips

 Likelihood: high, DesertXpress would allow for day trips to be made

 Impact: low, these additional trips would only represent a small proportion

of total trips

• Implementation of High Speed Rail in California

 Likelihood: medium, plans are underway to implement High Speed Rail in

the long term

 Impact: medium, this would increase mobility within the region

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• Extension of DesertXpress

 Likelihood: there are proposals on the table for some further rail extensions

across the north of the Los Angeles region. However, given the uncertainties

associated with such proposals – and the substantial delay inherent in the

implementation of such major schemes – these proposals have not been

included in the present analysis.

 Impact: high

Parking Arrangements in Victorville

38. The roads in the vicinity of the location of the Victorville station currently carry

relatively light traffic flows and there would appear to be plenty of space available to

enhance the junctions if necessary, although more work would be needed to verify

this.

39. Given the scale of the car park, its design, both physical and operational, will be

critical to achieving a short transfer time between car and train, by allowing

appropriate management arrangements such as allowing for a sufficient number of

parking spaces and by ensuring a short transfer time from cars to trains.

Review of Systems Operations

40. In general, Bombardier’s ‘Systems Operations Plan’, provides a reasonable overview

of the system and how it should be operated. It does make certain assumptions that do

potentially have financial implications and therefore associated risk. These are

summarised as follows.

Service Run Times Assumptions

41. The run times assumptions do appear ‘tight’ and could potentially erode the amount of

dwell time available to disembark/board passengers, luggage and undertake the

minimum amount of time required for cleaning and re-stocking of supplies. In

addition, this would impact the amount of recovery margin available during perturbed

conditions. Increases to the run time will have a direct impact on the number of

services required and associated investment costs. It should be noted here that DXE

has stated that Bombardier’s run times are not “assumptions”. Rather, that they are

calculated by Bombardier’s comprehensive train simulation model that represents the

alignment and the train performance in detail, and further, that their operating plan

then adds significant added time to determine the operating schedule, fleet size, train

miles and hours, energy consumption, etc. Further, they state that DXE has had a

highly qualified independent technology consultant under contract, Interfleet Ltd.

based in the UK, who has reviewed and will continue to review, Bombardier’s work.

42. It is unclear whether the Regina service will operate on the limited 4.5% maximum

grade or the unrelieved 5.8% grades. The latter condition will increase the end-to-end

run times. DXE has stated that the run times calculated by Bombardier’s simulation

model were based upon the 4.5 % grades, specifically the vertical and horizontal

alignment that they have identified as the “Applicant’s Proposed Action Alternative”

alignment shown on the 1”=1000’ plan and profile drawings.

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43. In both cases it is unknown if the train is capable of overcoming the grades with a

single motor failure and whether a shunting train would be required to assist. This

would have a direct impact on the fleet size requirements, as well as a disruptive

impact on the timetable. DXE states that Bombardier has modelled loss of propulsion

on a 10-car Regina EMU train with a range of powered cars being assumed (from six

to as many as all 10 of the cars on the train being powered) to ensure that the train will

overcome the grades under such a failure scenario – and that it does so.

Fleet Size

44. The Plan indicates that the 2012 timetable will require 12 in-service trains, 2 hot

spares and 2 maintenance trains. The SDG analysis indicates that the service will

require one less maintenance train resulting in a potential saving. This means the

service could function with 15 rather than 16 trains.

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Control Sheet

CONTROL SHEET

Project/Proposal Name: DESERTXPRESS RIDERSHIP & REVENUE

AUDIT

Document Title: Technical Memorandum: FRA Summary

Client Contract/Project Number:

SDG Project/Proposal Number: 207502

ISSUE HISTORY

REVIEW

Originator: Charles Russell

Other Contributors:

Review By: Print: Maarten Kroes

Sign: Via email - 4th October 2007

DISTRIBUTION

Clients: Miguel Seisedos, Thomas Stone

Steer Davies Gleave: Charles Russell, Maarten Kroes, Kevin

Feldman

Issue No. Date Details

1 19-09-2007 Submission of Draft Memorandum to client

2 04-10-2007 Submission of Final Memorandum to client

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