Office of Inspector General

ANALYSIS OF THE CAUSES OF AMTRAK 

TRAIN DELAYS

Federal Railroad Administration

Report Number: CR-2012-148

Date Issued: July 10, 2012
U.S. Department of

Memorandum

Transportation

Office of the Secretary

of Transportation

Office of Inspector General

Subject: INFORMATION: Analysis of the Causes of 

Amtrak Train Delays

Federal Railroad Administration

Report No. CR-2012-148

Date: July 10, 2012

From: Mitchell Behm

Assistant Inspector General for Rail, Maritime, and 

Economic Analysis

Reply to 

Attn. of: JA-50

To: Federal Railroad Administrator

Amtrak train delays outside the Northeast Corridor (NEC) reduce the value of 

Amtrak service as an option for travelers and increase the railroad’s need for 

subsidies. Consequently, they have long been the subject of congressional concern 

and industry debate. Amtrak points to freight railroads’ dispatching practices as 

the cause with the greatest impact on Amtrak train delays, while the freight 

railroads contend that capacity limitations, or insufficient infrastructure1 for rail 

traffic levels, contribute more heavily. 

In 2008, we issued two audit reports on Amtrak delays in response to requests 

from the Surface Transportation Subcommittee of the Senate Committee on 

Commerce, Science, and Transportation, and the Senate Appropriations 

Subcommittee on Transportation, Housing and Urban Development, and Related 

Agencies. In the first report, we concluded that delays outside the NEC 

substantially reduced Amtrak’s net earnings.

2 In the second report, we identified 

causes of these delays but did not assess their relative importance.3

The impact of Amtrak delays took on a new significance with the passage of the 

Passenger Rail Investment and Improvement Act of 2008,

4 and the American 

Recovery and Reinvestment Act of 2009.

5

 1 Rail infrastructure includes track, sidings, signals, switches, and yards.

Together, these acts provided 

2 OIG Report Number CR-2008-047, “The Effects of Amtrak's Poor On-Time Performance,” March 28, 2008. OIG 

reports are available on our Web site: www.oig.dot.gov. 3 OIG Report Number CR-2008-076, “Root Causes of Amtrak Train Delays,” September 8, 2008. 4 P.L. No.110-432, Div. B. 5 P.L. No. 111-5.
2

$12 billion to create and sustain a High Speed Intercity Passenger Rail (HSIPR) 

program. A substantial portion of the HSIPR program is geared towards improving 

the speed and reliability of existing Amtrak services. The Administration’s current 

budget proposal would add $47 billion more to the HSIPR program over the next

6 years. Stakeholders responsible for determining the distribution of these funds 

need information on the causes of Amtrak service delays.

We conducted this analysis to determine significant causes of Amtrak delays and 

to quantify their relative impacts. In our analysis, we (1) identified statistically 

significant causes of delays—those whose impacts were greater than would be 

expected to occur by chance—outside the NEC and under the control of either 

Amtrak or a freight railroad; (2) assessed the degree of influence each cause had

on Amtrak delays system-wide6 and by individual route; and (3) examined delay 

determinants at locations of consistent, substantial delay. 

To conduct our work, we constructed statistical models of Amtrak delays using 

data from Amtrak, the Surface Transportation Board’s Carload Waybill Sample, 

and Oak Ridge National Laboratory’s national rail network model.7 Our sample 

covered fiscal years 2002 through 2007.8 Since the marked reductions in freight 

traffic caused by the economic recession that began in late 2008 resulted in 

different usage patterns, we did not include post-fiscal year 2007 data in our 

analysis. However, our findings remain relevant given freight rail traffic’s recent 

strong growth.9 We analyzed on-track delays, which are the delays incurred by a 

train between the time it leaves one station and arrives at another, and did not 

analyze delays occurring in stations.10 Furthermore, we analyzed delays with 

respect to pure runtime—the time it takes a train to travel between two stations in 

the absence of interruptions—rather than delays with respect to schedule. 

Schedule delays differ from delays with respect to pure runtime by the amount of 

schedule padding negotiated between Amtrak and the freight railroads, which can 

differ for reasons unrelated to underlying delay causes. Additional information on 

our scope and methodology is provided in Exhibits A and B.

 6 System-wide delays were calculated as the average delays across all non-NEC routes. 7 We also used data from the National Climatic Center to control for some causes beyond stakeholder control. 8 Amtrak’s fiscal year begins October 1. 9 The numerical estimates of different causes’ delay contributions are specific to the sample period, but the results 

concerning the relative importance of different categories of causes remain relevant. For example, the estimated 

seconds of delay attributable to each host railroad holds only for the sample period, while the findings concerning the 

relative importance of host railroad effects to delays remains relevant. 10 On-track delays accounted for 77 percent of total delays in our sample, and in-station delays for the rest.
3

BACKGROUND

The Passenger Rail Investment and Improvement Act of 2008 directed the 

Secretary to establish a HSIPR program, and authorized approximately $4 billion 

over a 5-year period for HSIPR investments. Four months after that act’s passage, 

the American Recovery and Reinvestment Act of 2009 appropriated another 

$8 billion for HSIPR to be allocated over a much shorter time span. Under its 

current budget proposal, the Administration would allocate $47 billion to HSIPR 

over the next 6 years. Responsibility for implementing the HSIPR program has 

been delegated to the Federal Railroad Administration (FRA). Under the program, 

State transportation departments, often in collaboration with freight railroads and 

Amtrak, apply to FRA for investment funds.

Outside the NEC, Amtrak trains operate almost exclusively on infrastructure 

owned and dispatched by freight railroads. Freight railroads’ dispatchers are 

required by law11 to give Amtrak’s trains preference when freight trains and 

passenger trains operate on the same routes. However, Amtrak and the freight 

railroads disagree over whether preference is actually always given to Amtrak.

Further, the relative importance of freight train interference in Amtrak delays is a 

particularly controversial issue for Amtrak and the freight railroads. In our 2008 

study, we found that freight train interference was one of the primary causes of 

Amtrak delays, but that study relied entirely on data constructed from Amtrak 

conductors’ observations. Amtrak’s conductors have limited abilities to separate 

freight train interference from other delay causes. Hypothetically, if a conductor 

observes a freight train blocking his Amtrak train but cannot determine the reason 

for the freight train’s failure to move, the conductor may record the cause of the 

train’s delay as freight train interference, even though something else may be 

impeding the freight train’s movement.

Amtrak services on routes outside the NEC generally exhibit worse on-time 

performance (OTP)12

 11 49 U.S.C. § 24308(c) 

than Amtrak services on the NEC—where Amtrak largely 

owns the tracks and dispatches rail traffic. OTP on non-NEC long-distance routes 

is particularly poor. During the study period for this analysis, OTP on the NEC 

ranged between 75 and 90 percent, while on the 15 long-distance routes outside 

the NEC, it was consistently below 55 percent. During the same period, OTP on 

12 An Amtrak train is considered on time if it arrives at its endpoint, or final destination, within a fixed number of 

minutes of its scheduled arrival time. The fixed number of minutes depends upon trip length as follows: arrival at an 

endpoint 10 minutes or less late of the scheduled arrival time is considered on time on trips of less than 250 miles; 15 

minutes or less late is on time on trips between 250 and 350 miles; 20 minutes or less late is on time on routes 

between 350 and 450 miles; 25 minutes or less late is on time on routes between 450 and 550 miles; 30 minutes or 

less late is on time on routes of more than 550 miles. Trip length is the total distance traveled by a train from its 

origin to its final destination. Acela is held to a stricter standard. Even though its trip length exceeds 450 miles, 

Amtrak considers Acela on time only if it arrives at its endpoint within 10 minutes of its scheduled arrival.
4

Amtrak’s 13 non-NEC short-distance routes varied from 65 to 76 percent. Figure 1 

depicts OTP on the NEC, and on non-NEC long- and short-distance services. 

Figure 1. Amtrak’s Average Annual OTP for Fiscal Year 2002-

Fiscal Year 2007 

Source: Amtrak data

Delays also differ across routes, which differ in host railroad, congestion, track 

configuration, and terrain. For example, the average OTP for two long-distance 

routes, the Sunset Limited and the Empire Builder, is 14 percent and 72 percent,

respectively.13 Two short-distance routes, the Carolinian and the Hiawatha, show a 

similar range of average OTP at 30 percent and 92 percent, respectively. However, 

long-distance routes tend to exhibit greater variation in OTP than short-distance 

routes.

In the current study, we analyzed delays at the station-pair level to capture the 

causes of delays at their origins. We defined a station-pair as two Amtrak stations 

on a route—the first station being the origin and the second station being the 

destination—with no Amtrak stations in between.14 On average, there were 

30 station-pairs on each long-distance route and 20 station-pairs on each shortdistance route. 

 13 For the locations of Amtrak routes, see Exhibit D. 14 In our analysis, we also differentiated between station-pairs by direction of travel and by Amtrak service.

20%

30%

40%

50%

60%

70%

80%

90%

2002 2003 2004 2005 2006 2007

Average On-Time Performance

NEC Non-NEC Short Distance Non-NEC Long Distance
5

RESULTS IN BRIEF

We identified six statistically significant causes of Amtrak delays: 

• slow orders, which are speed restrictions imposed by host railroads along track 

segments in poor condition or undergoing repairs or improvement;15

• capacity utilization, which is a measure of congestion; 

• host effects, or the effects of operating on track owned and dispatched by a 

particular host railroad or group of host railroads;16

• turn points, where Amtrak changes crews; 

• late arrivals, or Amtrak trains operating well outside their scheduled time slot; 

and

• Amtrak mechanical problems. 

Virtually the same causes were significant for both long-distance and shortdistance services.17

The chief causes of delays system-wide were host effects and slow orders. 

However, delays caused by host effects differed considerably by host. For

example, the difference between the largest and the smallest host railroad 

contributions to delays along a long-distance route, averaged across the Amtrak 

system, was 38 minutes and 30 seconds. Only slow orders may have produced 

sufficiently large delays to exceed the delays caused by the largest host effects.18 

Other factors, such as capacity utilization and activities at turn points, contributed 

significantly to delays system-wide, but considerably less than either host effects 

or slow orders. On most individual routes, as opposed to system-wide, slow orders 

caused the largest delays of any factors other than host effects.19 Delays caused by 

different hosts varied even more widely across individual routes than they did on a 

system-wide basis. 

At the locations with the highest average delays, slow orders, capacity utilization, 

and late arrivals were generally well above their system-wide averages. This 

suggests that all three causes contributed notably to delays at these locations.20

 15 Slow orders can also be imposed because of adverse weather conditions. However, we analyzed weather effects 

separately (see Exhibit B), and our results on slow orders relate only to infrastructure issues. 16 These effects are broken out by specific host railroad or host railroad group in the body of the report. 17 As defined by Amtrak, long-distance routes extend 550 miles or more and short-distance routes cover less than 550 

miles. 18 We could determine only minimum values for the delays caused by host effects system-wide. The delays caused by 

slow orders exceeded the minimum host effect values, but we could not determine how they would compare to the 

average values of host effects. 19 On individual routes, we were unable to compare the size of delays caused by host effects versus other factors. 20 We cannot test this hypothesis because our models, which were estimated on a system-wide basis, cannot generate 

meaningful delay impact estimates for individual station-pairs. 
6

Host effects may have also played a role, but we cannot measure them at the 

individual station-pair level. Further analysis found that locations with the highest 

levels of slow orders also had some of the highest average delays, while locations 

with the highest levels of either capacity utilization or late arrivals did not have 

particularly large delays. This suggests that slow orders may have contributed 

more to delays at these station-pairs than either capacity utilization or late arrivals. 

OUR MODELING PROCESS IDENTIFIED SIX STATISTICALLY 

SIGNIFICANT CAUSES OF DELAYS 

In our modeling process, we identified six statistically significant causes of 

Amtrak train delays under stakeholder control. Four of these causes were under 

the control of a specific stakeholder: host railroads’ effects, slow orders, Amtrak 

mechanical failures, and activities at Amtrak turn points. The other two—capacity 

utilization and late arrivals—were not. Capacity utilization is, in part, a function of 

capital investment, which can be carried out by the freight railroads alone or in 

partnership with the public sector. Late arrivals—or trains that arrive significantly 

late at their stations of departure—are out of their scheduled time slots and cause 

dispatching problems further down their routes. Whether or not an Amtrak train is 

out of its scheduled time slot arguably depends on the actions of both Amtrak and 

the host railroad. Virtually the same causes were significant on both long- and 

short-distance services. Table 1 details these causes.
7

Table 1. Statistically Significant Causes of Amtrak Train Delays

Cause a Definition

Capacity Utilization The amount of rail traffic divided by infrastructure capacity, which is the 

maximum amount of cars that can travel on a track segment based on 

the segment’s characteristics. Congestion increases as capacity 

utilization increases.

Slow Orders Speed restrictions imposed by the host railroad; we considered only 

those slow orders due to poor infrastructure conditions, and 

infrastructure repair and improvement work.

Amtrak Mechanical Mechanical failures of Amtrak's locomotives and rolling stock.b

Late Arrival The percentage of trains that arrived at least two hours late at the origin 

station.

Turn Point A location at which a train’s crew changes.

Host Effects Host effects capture the impacts of such host railroad factors as 

dispatching practices, management capability, and business model.

BNSF Host Effect The effect of operating on infrastructure owned and dispatched by 

Burlington Northern Santa Fe Railroad (BNSF). 

CSXT Host Effect The effect of operating on infrastructure owned and dispatched by CSX 

Transportation (CSXT).

NS Host Effect The effect of operating on infrastructure owned and dispatched by 

Norfolk Southern Railroad (NS).

UP Host Effect The effect of operating on infrastructure owned and dispatched by Union 

Pacific Railroad (UP).

 CN Host Effect The effect of operating on infrastructure owned and dispatched by 

Canadian National Railroad (CN).

Multiple Host Effect The effect of operating on infrastructure owned and dispatched by 

multiple railroads between two stations. a All causes were measured on a monthly basis at the station-pair level. 

b Rolling stock includes all rail cars other than locomotives. 

Unlike other causes in our model, we measured host effects relative to a baseline. 

Measurement of total host effects would require comparison with operations in the 

absence of any host effect, but this is impossible since trains always operate on 

some entity’s infrastructure. Consequently, we measured delay caused by a host 

effect relative to a baseline of delay associated with operation on infrastructure 

owned and dispatched primarily by short-line and regional railroads.21

 21 Generally, Class III carriers are referred to as short lines, and Class II carriers are referred to as regional railroads. 

The four host railroads whose effects we traced are Class I railroads, which have annual revenues in excess of $250 

million adjusted to 1991 dollars. 

Therefore, 

a host effect is the difference in delays attributed to operating on infrastructure 
8

owned and dispatched by the particular host and delays from operating on 

infrastructure primarily owned and dispatched by regional and short-line railroads. 

HOST EFFECTS AND SLOW ORDERS CAUSED THE LARGEST 

DELAYS SYSTEM-WIDE AND CONTRIBUTED SUBSTANTIALLY 

TO ROUTE LEVEL DELAYS

Host effects and slow orders were the chief causes of delays system-wide. 

However, delays caused by host effects varied considerably from one host railroad 

to another, for both long- and short-distance services system-wide. Only slow 

orders may have caused delays greater than those caused by the largest host 

effects. Capacity utilization and the activities at turn points also contributed 

notably to delays on both types of services, but capacity utilization had a relatively 

greater impact on short-distance services than on long-distance services. Amtrak 

mechanical problems contributed little to delays on either type of service. Delays 

caused by different hosts varied even more widely across individual routes than 

they did system-wide. Slow orders dominated other non-host effect causes in their 

contributions to individual route delays.

Delays Caused by Host Effects Differed Markedly Across Freight 

Railroads for Long-Distance Services System-wide

Delays differed substantially depending on which freight railroad owned and 

dispatched traffic over the infrastructure on which an Amtrak train operated. On 

average, the UP host effect caused 50 seconds more delay between each 

station-pair than the baseline railroad group.22 In contrast, long-distance services 

on BNSF’s rails experienced 27 seconds less delay between each station-pair on 

average than those operating on the baseline group’s infrastructure. Over an entire 

long-distance route, the UP host effect added an average 25 minutes of delay more 

than the baseline (50 seconds times 30 stations), while the BNSF host effect 

reduced delays 13 minutes 30 seconds below the baseline. Figure 2 depicts the 

contributions of individual host effects to station-pair delays on long-distance 

services.23 No bar appears for the CN host effect because it was statistically 

indistinguishable from the baseline host effect.

 22 We calculated each causal factor’s contributions to delays as the product of the average value of the factor and its

estimated effect. For example, to calculate slow orders’ contribution to system-wide long-distance delays, we 

multiplied the system-wide average level of slow orders on long-distance routes times slow orders’ average effect on 

delays between a station-pair on a long-distance service. 23 Exhibit C shows the estimates underlying Figures 2-5, along with their confidence intervals.
9

Figure 2: Host Effects’ Contributions to Station-Pair Delay on 

Long-Distance Services System-wide 

Source: OIG 

A minimum value for the baseline host effect can be determined for long-distance 

services system-wide. Host effect contributions to delays cannot be negative, since 

trains cannot travel faster than pure runtime. So the baseline host effect must be at 

least as large and positive as BNSF’s host effect in relation to it is negative. At a 

minimum,

24 the baseline host effect causes 27 seconds of delay at each stationpair. Consequently, UP’s host effect caused a minimum of 1 minute 17 seconds of 

delay (27 seconds minimum baseline value plus 50 seconds UP host delay relative 

to the baseline) between station-pairs. This translates into an average 38 minutes 

30 seconds of delay over an entire long-distance route from traveling on UP 

controlled infrastructure. Similarly, CSXT, NS, and multi-host effects generated 

delays equal to the value of their impacts relative to the baseline plus at least 

27 seconds, for total minimum contributions to station-pair delays of 65, 44, and 

38 seconds, respectively. 

Only Slow Orders May Have Caused Longer Delays Than the Largest 

Host Effects Caused on Long-Distance Services System-wide

Slow orders caused the greatest delays among factors other than host effects on the 

long-distance services system-wide—1 minute 55 seconds on average between 

each station-pair—which represents 23 percent of the average long-distance 

 24 This minimum assumes BNSF does not cause any increase in delays.

-40 -20 0 20 40 60

BNSF Host

CN Host

Multiple Host

NS Host

CSXT Host

UP Host

 

Baseline

Seconds
10

service station-pair delay of 8 minutes and 25 seconds. Over the entire length of a 

long-distance route, slow orders contributed 57 minutes 30 seconds of delay on

average. No other cause contributed delays greater than the minimum values of 

most host effects. Figure 3 shows the contributions of all causes other than host 

effects. After slow orders, late arrivals made the next largest contributions to 

delays. On average, they added 30 seconds of delay between each station-pair, 

while capacity utilization and turn points contributed 26 seconds and 20 seconds, 

respectively. Amtrak mechanical problems contributed little to delays.

Figure 3: Non-Host Effect Causes’ Contributions to Station-Pair 

Delay on Long-Distance Services System-wide

Source: OIG

0 40 80 120

Amtrak Mechanical

Late Arrival

Turn Point

Capacity Utilization

Slow Orders

 

Seconds
11

Two Host Effects and Slow Orders Caused the Largest Delays on 

Short-Distance Services System-wide

Host effects on short-distance services system-wide also varied widely. As 

Figure 4 illustrates, the multiple host effect generated the largest delay above the 

baseline—15 seconds. The UP host effect added 12 seconds, the second largest 

delay contribution. CN, CSXT, and NS host effects were statistically 

indistinguishable from the baseline host effect. As with long-distance services, 

short-distance services operating on BNSF’s rails experienced less delay than 

those operating on the baseline group’s infrastructure—24 seconds less on 

average. 

Figure 4: Host Effects’ Contributions to Station-Pair Delay on 

Short-Distance Services System-wide

Source: OIG

Again, the baseline delay must be at least as large and positive as the BNSF host 

effect is large and negative. At a minimum then, the multiple host effect between a 

station-pair added 39 seconds to delay (24 seconds minimum baseline value plus 

15 seconds multiple host delay relative to the baseline), and the UP host effect 

added 36 seconds. The minimum values of these two host effects exceeded the 

contributions of all non-host effect causes except slow orders.

-40 -20 0 20

BNSF Host

CN Host

NS Host

CSXT Host

UP Host

Multiple Host

Baseline

Seconds
12

Slow orders contributed 1 minute 3 seconds to the average total delay on 

short-distance services of 5 minutes 10 seconds, or approximately 20 percent. 

Figure 5 shows the contributions of non-host effect causes to delays on shortdistance services system-wide. Capacity utilization played a relatively more 

prominent role in delays on short-distance services than on long-distance services, 

contributing 24 seconds between each station-pair, or 8 percent of the average

total. Turn points increased delays between station-pairs by 16 seconds. Few trains 

arrived at their departure stations over two hours late on short-distance services, so 

late arrivals did not have a major impact on short-distance delays. As on longdistance services, Amtrak mechanical problems contributed little to delays on 

short-distances services.

Figure 5: Non-Host Effect Causes’ Contributions to Station-Pair 

Delay on Short-Distance Services System-wide

Source: OIG

0 20 40 60 80

Amtrak Mechanical

Late Arrival

Turn Point

Capacity Utilization

Slow Orders

 

Seconds
13

Host Effects Had Widely Varying Impacts and Slow Orders Caused 

Substantial Delays Across Individual Long-Distance Routes 

Host effects on individual long-distance routes varied greatly from one route to 

another, as Figure 6 shows.25 The impact of host effects on individual routes 

depends on the proportion of the infrastructure owned by the host on that route. 

For example, the Illinois/Missouri Service travels on UP infrastructure for the 

majority of its length, and the UP host effect contributes 3 minutes 11 seconds to 

station-pair delays on that route. In contrast, the UP host effect’s contribution to 

long-distance service delays system-wide captures UP’s impact only on UP’s 

portion of the entire long-distance system’s infrastructure. Because we generated 

route level results using the system-wide model, we cannot determine a minimum 

value for the baseline for individual routes.

Figure 6: Host Effects’ Contributions to Station-Pair Delay on 

Individual Long-Distance Routes 

Source: OIG

 25 Exhibit E shows the estimates underlying Figures 6-7.

1.30

2.29

2.81

2.27

0.20

2.79

1.93

-1.43

3.75

2.53 2.32

-1.76

3.25 3.07

2.26

-2

-1

0

1

2

3

4

5

Minutes of Delay

UP Host BNSF Host Multiple Host CSXT Host NS Host Net Effect
14

Among non-host effect causes, slow orders produced the largest delays on most 

individual long-distance routes. However, the amount of delay due to slow orders 

varied considerably across routes. For example, slow orders added 4 minutes 

34 seconds to station-pair delays on the Texas Eagle, but only 39 seconds on the 

Southwest Chief. Figure 7 depicts the causes’ relative contributions. Late arrivals 

increased delays on many of the long-distance routes, but most notably on the 

Sunset Limited, Coast Starlight, and California Zephyr.

Figure 7: Non-Host Effect Causes’ Contributions to Station-Pair 

Delay on Individual Long-Distance Routes

Source: OIG

0

1

2

3

4

5

6

7

Minutes of Delay

Capacity Utilization Slow Orders Amtrak Mechanical Late Arrival Turn Point
15

As depicted in Figure 8, the multiple host effect caused sizable delays on several

individual short-distance routes, while the UP host effect caused substantial delays 

on the Capitol Corridor and Missouri routes.26 Short-distance routes hosted at least 

in part by BNSF experienced reductions in delays relative to the baseline systemwide host effect. Among these routes, the Cascades, Heartland Flyer, and San 

Joaquin experienced particularly substantial reductions in relation to the baseline.

Figure 8: Host Effects’ Contributions to Station-Pair Delay on 

Individual Short-Distance Routes 

Source: OIG

 26 Exhibit F shows the estimates underlying Figures 8-9.

0.95

-1.06

-1.80

0.00

1.05

0.07

1.05

0.71

0.95

-0.10

0.06 0.00

-0.91

-2

-1

0

1

2

Minutes of Delay

UP Host BNSF Host Multiple Host Net Effect

Baseline
16

Slow orders caused large delays, but not always the largest of non-host effect 

causes on individual short-distance routes. For example, capacity utilization 

contributed more to delays on the Pacific Surfliner, Pennsylvanian and San 

Joaquin routes than slow orders, as Figure 9 indicates. Late arrivals had less 

impact on short-distance routes than on long-distance routes because far fewer 

trains ran so far outside of their scheduled time slots over the shorter distances.

Figure 9: Non-Host Effect Causes’ Contributions to Station-Pair 

Delay on Individual Short-Distance Routes 

Source: OIG

SEVERAL DELAY CAUSES HAD ELEVATED VALUES AT 

STATION-PAIRS WITH THE HIGHEST AVERAGE DELAYS, BUT 

SLOW ORDERS MAY HAVE BEEN THE MOST IMPORTANT

Slow orders, capacity utilization, and late arrivals at the locations with the highest 

average delays were all generally above their system-wide averages—suggesting

that all three causes contributed notably to these delays. We cannot test this 

hypothesis, however, because our system-wide models cannot generate delay 

impact estimates at individual station-pairs.27

 27 Use of a system-wide model at the station-pair level would involve errors beyond acceptable levels.

Furthermore, without using our 

models, we cannot measure host effects. However, further analysis of the raw data 

reveals that locations with the highest levels of slow orders also had some of the 

highest average delays, while locations with the highest levels of either capacity 

0

2

4

6

Minutes of Delay

Capacity Utilization Slow Orders Amtrak Mechanical Late Arrival Turn Point
17

utilization or late arrivals did not have particularly large delays. These data

suggest that slow orders may have primarily driven the largest station-pair delays. 

Slow orders, capacity utilization, and late arrivals were all generally above their

system-wide averages at the station-pairs with the highest average delays, as

illustrated in Table 2 for a selection of those station-pairs.28 This fact suggests that 

these causes all contributed notably to delays at these locations.

Table 2. Delay Causes’ Average Values at Selected Station-Pairs 

with High Average Delay 

Station-Pair Route Average

Delay 

(minutes)

 Slow 

Orders 

(minutes)

Capacity 

Utilization 

(%)

 Late 

Arrival 

(%)

Long-Distance Services

San Antonio – Houston Sunset Ltd. 48.4 15.3 52.5 42.6

Alpine-El Paso Sunset Ltd. 46.0 14.0 49.3 44.6

St. Louis-Poplar Bluff Texas Eagle 43.2 37.9 2.8 3.6

Winnemucca-Sparks California Zephyr 41.1 28.0 41.4 53.7

Salt Lake City-Elko California Zephyr 40.0 19.6 7.9 22.0

System-wide 8.4 2.1 15.2 17.5

Short-Distance Services

Vancouver-Bellingham Cascades 29.4 5.5 6.8 0.0

Springfield-Alton Illinois 25.6 6.0 6.4 1.7

Sedalia-Jefferson City Missouri 24.4 1.9 14.7 3.0

Lee’s SummitWarrensburgMissouri 21.1 1.5 12.7 1.1

Pittsburgh-Alliance Pennsylvanian 19.1 3.3 12.2 2.2

System-wide 5.2 0.9 10.1 1.5

Source: OIG 

 28 We chose to display a selection from among the station-pairs with the highest average delays to avoid primarily 

showing data for station-pairs that differed only in direction, and to provide a sampling from different routes. Similar 

concerns determined our selections of station-pairs for Tables 3-4. 
18

We examined average delays at the station-pairs with the highest levels of slow 

orders. Delays at these station-pairs matched the magnitudes of the delays at the 

station-pairs with the highest average delays. Table 3 illustrates this for selected 

station-pairs.

Table 3. Delay Causes’ Average Values at Selected Station-Pairs 

with High Average Slow Orders

Station-Pair Route Average 

Delay 

(minutes)

Slow 

Orders 

(minutes)

Capacity 

Utilization 

(%)

Late 

Arrival 

(%)

Long-Distance Services

St. Louis- Poplar Bluff Texas Eagle 43.2 37.9 2.8 3.6

Elko-Winnemucca California Zephyr 41.0 33.9 34.5 31.0

Salt Lake City-Elko California Zephyr 40.0 19.6 7.9 22.0

Chico-Sacramento Coast Starlight 27.9 19.4 6.5 44.3

Klamath Falls-Dunsmuir Coast Starlight 31.9 17.8 10.9 25.9

System-wide 8.4 2.1 15.2 17.5

Short-Distance Services

Hermann-Jefferson City Missouri 16.1 6.1 24.2 4.2

Springfield-Alton Illinois 25.6 6.0 6.4 1.7

Gainesville-Fort Worth Heartland Flyer 18.8 5.7 40.6 0.8

Vancouver-Bellingham Cascades 29.4 5.5 6.8 0.0

Rensselaer-Dyer Hoosier 10.4 5.1 2.9 3.7

System-wide 5.2 0.9 10.1 1.5

Source: OIG 
19

By comparison, we did not find an association between the highest values of 

capacity utilization and late arrival and the highest station-pair delays. Table 4 

illustrates this with the average delays at station-pairs selected from among those 

with the highest levels of capacity utilization. Taken together, these results suggest 

that slow orders alone could have driven the bulk of the delays at the station-pairs 

with the highest average delays

Table 4. Delay Causes’ Average Values at Selected Station-Pairs 

with High Average Capacity Utilization 

Station-Pair Route Average 

Delay 

(minutes)

Slow 

Orders 

(minutes)

Capacity 

Utilization 

(%)

Late 

Arrival 

(%)

Long-Distance Services

Pomona-Ontario Sunset Ltd. 4.7 0.2 95.0 5.5

Deming-Lordsburg Sunset Ltd. 12.0 0.6 87.7 51.1

Tucson-Maricopa Sunset Ltd. 18.1 0.3 81.0 49.5

Yuma-North Palm 

Springs

Sunset Ltd. 28.4 2.9 77.0 59.7

Glasgow-Wolf Point Empire Builder 2.2 0.6 62.7 8.4

System-wide 8.4 2.1 15.2 17.5

Short-Distance Services

Martinez-Antioch San Joaquin 1.3 1.2 68.5 0.2

Gainesville-Fort Worth Heartland Flyer 18.8 5.7 40.6 0.8

Wasco-Corcoran San Joaquin 3.5 0.5 33.7 0.2

Fresno-Madera San Joaquin 1.9 0.6 29.8 0.5

Seattle-Edmonds Cascades 1.2 0.4 28.3 0.3

System-wide 5.2 0.9 10.1 1.5

Source: OIG
20

CONCLUDING OBSERVATIONS

Train delays substantially increase Amtrak’s need for subsidies and reduce the 

value of intercity passenger rail as an option for travelers. Under the HSIPR 

program, substantial investment funds have been made available to improve 

passenger rail’s speed and reliability. Stakeholders making and overseeing 

investments in rail infrastructure involving public funds need to be aware of 

causes of the delays that they are intending to reduce as part of the decision

making process. Of key concern is the need to differentiate those factors in 

passenger train delays that may not require capital investment to address, such as 

host effects and slow orders, from those factors that require investment. Making 

this distinction is critical to ensure publically funded rail investments are targeted 

to locations where capacity constraints are the primary cause of delays, and not 

expended on locations where other delay causes predominate.

#

cc: Audit Liaison, OST, M-1

Audit Liaison, FRA, RAD-43

Amtrak Liaison
21

Exhibit A. Scope and Methodology

EXHIBIT A. SCOPE AND METHODOLOGY

We conducted this analysis to identify the primary causes of delays and determine 

their relative impacts. In our analysis, we: (1) identified statistically significant 

causes of delay—those whose impacts were greater than would be expected to 

occur by chance—outside the NEC and under the control of a stakeholder, either 

Amtrak or a freight railroad; (2) assessed the degree of influence each cause had

on Amtrak’s delays system-wide and by individual route; and (3) examined delay 

determinants at locations of consistent, substantial delay. 

We built our sample from Amtrak’s 28 non-NEC passenger routes, 15 of which

were long-distance routes—or longer than 550 miles—and 13 of which were

short-distance. The sample period included fiscal years 2002 through 2007. We 

compiled 1,117 unique, non-overlapping Amtrak origin and destination 

station-pairs. The station-pairs differed by direction and route because delay times 

can differ according to these parameters. The unit of observation in our data was a 

unique combination of origin station, destination station, route, and month. 

We designed and estimated delay models to explain the variations in delay of 

Amtrak’s trains. The dependent variable—the one we wished to explain—was the 

average minutes of on-track delay at the station-pair level. The independent 

variables—those used to explain the variations in delays—included precipitation;

temperature; capacity utilization; slow orders; mechanical delays; late arrivals;

turn points—locations at which crews changed; distance between stations; host 

effects; meetings between Amtrak and various freights to address delay issues; and 

fiscal year dummy variables. 

We divided our sample into long-distance and short-distance routes when 

estimating our delay models. The models for these two types of routes were the 

same except that we excluded the meeting variables from the short-distance model 

because these meetings were unrelated to short-distance routes. The decision to 

estimate separate models was based upon the different characteristics associated 

with these services as well as test results. For example, outside of the NEC, shortdistance routes are served by shorter, lighter trains that can travel at higher speeds 

than those on long-distance routes. 

We generated our measure of delay with respect to pure runtime (PRT). PRT is the 

time required to make a trip in the absence of any interference. Our delay variable 

was the difference between the time a train departed an origin station and the time 

it arrived at its destination station minus PRT. PRT delays differ from delays with 

respect to scheduled travel time, which incorporates schedule padding introduced 
22

Exhibit A. Scope and Methodology

to accommodate expected delays and as a result of negotiations between Amtrak 

and the host railroads. 

Capacity utilization is traffic volume divided by railroad infrastructure capacity. 

We used the maximum value of the calculated capacity utilization rate across all 

track segments between a station-pair. Oak Ridge National Lab (ORNL) 

developed the capacity values for each infrastructure segment based on four 

different factors: number of tracks, type of signal system, frequency of passing 

sidings, and terrain grade. 

Traffic volume is defined as the total number of rail cars—locomotives, Amtrak

rail cars, commuter rail cars, and freight rail cars—that pass over a segment of 

track in a given month. We obtained raw data on daily freight traffic from the 

Surface Transportation Board’s Carload Waybill Sample.1 ORNL used the 

Transportation Routing Analysis Geographical Information System (TRAGIS), a 

national railroad routing simulation program, to assign routes to all freight traffic 

identified in the Waybill Sample. We determined Amtrak train frequency using 

Amtrak timetables and the number of cars on each train from Amtrak’s consist 

books.2 We obtained information on commuter train frequency and number of cars 

per train from 10 commuter agencies.

We captured host effects using dummy variables for the host railroads that own 

and dispatch the associated infrastructure. The host railroads for each of our 

1,117 station-pairs remained constant in our sample.

3 Amtrak provided the data 

on freight railroad infrastructure ownership. We created seven host railroad 

categories for the station-pairs in our sample: one category for each of five Class I 

railroads—BSNF, UP, CSXT, NS, and CN; one category for multiple hosts 

between a single station-pair; and one category for other, largely short-line and 

regional, railroads. 

We chose the Hausman-Taylor estimator for our sample after a series of tests. The 

Hausman-Taylor estimator allowed us to address potential measurement error 

associated with the capacity utilization variable and to obtain coefficients for our 

time-invariant variables, such as host effects. The measurement error with capacity 

utilization arises because of the assumptions we had to make during the 

construction of the variable. These assumptions include no change in capacity 

along Amtrak routes during our study period and the use of the maximum capacity 

utilization value for each station-pair. 

 1 The Waybill Sample provided origin, destination, and, in some cases, intermediate points for each trip sampled. 2 Amtrak's consist books detail the numbers and types of rail cars making up each train on each Amtrak route. 3 Station-pairs which changed host railroad accounted for less than three percent of our total observations, and we 

excluded them from our analysis. 
23

Exhibit B. Detailed Scope and Methodology

EXHIBIT B. DETAILED SCOPE AND METHODOLOGY

This exhibit provides a detailed discussion of our scope and methodology broken 

into the following sections: data, model, estimation, and sensitivity analyses.

DATA

Our sample includes nearly all of Amtrak's non-NEC corridor passenger routes 

from Amtrak fiscal year 2002 through 2007.1 The data covers 28 Amtrak longdistance—defined as longer than 550 miles—and short-distance routes.2 We 

compiled 1,117 unique, non-overlapping Amtrak origin and destination stationpairs from these routes. The unit of observation was a unique combination of 

origin station, destination station, route, and month.3 Altogether, we had 64,684

unique observations.

Station-pairs serviced by different routes were treated as separate observations. 

Many instances in which multiple routes served the same station-pair involved 

both short- and long-distance routes. Our treating these instances separately 

allowed for the possibility that short-distance and long-distance trains were 

affected differently by factors such as host railroad dispatching practices. Ninetythree percent of the 1,117 station-pairs were served by a single route. 

Approximately six percent were shared by two routes. The remaining station-pairs 

were served by three to six routes.

The station-pairs in our model also differed by direction. We used directional 

markets because delay times can differ according to travel direction. For example, 

on the Texas Eagle, which runs between Chicago, Illinois and San Antonio, Texas, 

northbound trains consistently take 100 minutes longer, on average, than 

southbound trains to reach their final destination. 

 1 We excluded the following NEC routes from our sample: Acela Express, Metroliner, Regional, Empire Service, 

Keystone, Vermonter and Downeaster. We also excluded one non-NEC service, the Auto Train, which travels from 

Lorton, Virginia to Sanford, Florida with no intermediate station stops. 2 Long distance routes include the California Zephyr, City of New Orleans, Coast Starlight, Empire Builder, Cardinal, 

Lake Shore Limited, Silver Service, Southwest Chief, Sunset Limited, Texas Eagle, Capitol Limited, Crescent, Three 

Rivers and Illinois/Missouri. Short-distance routes include the Heartland Flyer, Hoosier State, Illinois, Pacific 

Surfliner, San Joaquin, Capitol Corridor, Carolinian, Cascades, Hiawatha, Michigan, Missouri, Pennsylvanian, 

Piedmont, and Kentucky Cardinal. 3 We have unbalanced panel data in that we did not have the full 72 months worth of data for all 1,117 station-pairs. 

The minimum number of observations for any station-pair included in the sample was 12 months. At least 68 months

of data was available for half of the station-pairs. 
24

Exhibit B. Detailed Scope and Methodology

Measure of Delay

 

We generated our measure of delay with respect to pure runtime (PRT).4 PRT is 

the time required to make a trip in the absence of any interference. Our delay 

measure is the difference between the time a train departs an origin station and the 

time it arrives at its destination station minus PRT. Note that PRT delays differ 

from delays with respect to scheduled travel time. Scheduled travel time 

incorporates schedule padding, which may be introduced to accommodate 

expected delays or as a consequence of negotiations with the host railroad. We 

initially analyzed delays with respect to scheduled travel time but found that 

padding hid important determinants of delay. In addition, current year padding 

appeared to be related to previous year delays, and consequently introduced 

endogeneity issues. The remainder of this report solely considers delays with 

respect to PRT.

Though we do not present the results here, we also examined delays occurring in 

stations. We found that on average, on-track delays accounted for 77 percent of 

total delay minutes, while station delays accounted for 23 percent. However, while 

there are scheduled terminal wait times, there is no analogue for PRT that would 

correspond to time spent in a terminal. Consequently, we could not construct a 

model for station delays analogous to our model of on-track delays with respect to 

PRT. All delays discussed in this report are on-track delays. 

Daily delays were aggregated into monthly averages by station-pair. To avoid 

unrepresentative monthly averages, we eliminated station-pairs that were missing 

more than two-thirds of their expected observations.5 In addition, we eliminated 

outliers that were obviously the result of data entry error. Some delays were long 

enough to have compelled the cancellation of the associated trains, but did not, 

and others were sufficiently negative so as to be physically impossible. Both types 

of extreme delays fell within the top and bottom one percent of daily delay data, 

which we removed from our sample. Figure B1 displays a histogram for average 

minutes of delay for the data used in the analysis. The distribution has a longer tail 

on the right, but the tail thins out dramatically as delays increase.6

 4 Amtrak provided PRT data and data on arrival and departure times for each station at a daily frequency at the 

individual train level. We removed data on temporary trains—those operating less than once a week. Nearly all routes 

are serviced at least once a day.

We calculated 

the skewness and kurtosis to compare the degree of deviation from a normal 

5 According to Amtrak, missing observations most likely occur as the result of station staff's failure to record trains' 

entry and exit times. Amtrak is currently automating the recording process for all stations. 6 The method used to measure PRT creates considerable opportunity for noise around zero minutes of delay. 

Specifically, PRT represents a time agreed upon by representatives of both Amtrak and the relevant host railroad as 

they ride a route and gauge the minimum possible travel time. In addition, PRT changes infrequently; short-term 

travel time adjustments are usually made only in scheduled recovery time and miscellaneous adjustments rather than 

in PRT itself.
25

Exhibit B. Detailed Scope and Methodology

distribution. The skewness was 2 and kurtosis 10, as compared with 0 and 3 for 

the normal distribution.

Figure B1: Minutes of Delay Histogram

Note: Figure B1 captures 99 percent of observations; the remainder have delays in excess of 40 minutes.

Source: OIG

Host Effects 

We captured host railroad effects—host effects—using dummy variables for the 

individual host railroads who own and dispatch the associated infrastructure. 

Amtrak provided the data on railroad infrastructure ownership. We identified 

seven host railroad categories associated with the station-pairs in our sample: one 

category for each of five Class I railroads—Burlington Northern and Santa Fe 

(BSNF), Union Pacific (UP), CSX Transportation (CSXT), Norfolk Southern (NS) 

and the Canadian National Railway Company (CN); one category for multiple 

hosts between a single station-pair; and one category for other, primarily short-line

and regional, railroads. 

The host railroads for our 1,117 station-pairs remained constant throughout the 

sample period. Three host railroads divided ownership of roughly three-fourths of 

the track miles fairly evenly among them. Track miles owned by multiple hosts 

0

5 10 15 20 25

Percent

-10 0 10 20 30 40

Average Minutes of Delay
26

Exhibit B. Detailed Scope and Methodology

accounted for 2 percent of total track miles, and those owned by the "other" 

railroad group accounted for 4 percent. We eliminated the station-pairs between 

which Amtrak hosts the tracks because there were so few of them.

Capacity Utilization Rate 

Our capacity utilization measure has four components: the capacity of the railroad 

infrastructure, freight traffic, Amtrak traffic, and commuter rail traffic. Traffic is 

defined as the total number of cars—locomotives, passenger cars, and freight 

cars—that pass over a segment of track in a given month. The Transportation 

Routing Analysis Geographical Information System (TRAGIS), a national railroad 

system model and routing simulation program developed by Oak Ridge National 

Laboratories (ORNL), provides information on track characteristics (number of 

tracks, type of signal system, frequency of passing sidings) for each segment of 

intermediate track between each station-pair. ORNL used this information in 

combination with data on terrain grade to develop capacity values for each 

segment based on a method designed by Clark (1995). ORNL determined it was 

reasonable to assume that capacity did not change along Amtrak routes over our 

sample period. 

We obtained raw data on daily freight traffic from the Surface Transportation 

Board's Carload Waybill Sample.7 ORNL used TRAGIS to assign routes to all 

freight traffic identified in the Waybill Sample, and translated daily traffic levels 

into monthly averages for each track segment. We determined Amtrak train 

frequency using Amtrak timetables, and the number of cars on each train from 

Amtrak consist books. We obtained information on commuter train frequency and 

number of cars per train from the 10 relevant commuter agencies. 

Monthly freight, Amtrak, and commuter traffic estimates were divided by the 

number of days in each month to get average daily traffic to match daily capacity. 

We calculated the capacity utilization rate for each segment of track by totaling 

the daily averages of freight, Amtrak, and commuter rail traffic and dividing the 

sum by segment capacity. The maximum value of the calculated capacity 

utilization rate across all track segments between a station-pair was used to 

represent segment capacity in the analysis. 

MODEL 

We constructed the following model to measure delays on long-distance routes. 

The dependent variable is ON-TRACK DELAYijt. It is the average on-track 

 7 The Waybill Sample provided origin, destination, and, in some cases, some intermediate points for each trip sampled.
27

Exhibit B. Detailed Scope and Methodology

minutes of delay with respect to PRT for all trains that operate at station-pair i, 

route j, in month t. The rest of the model is as follows: 

ON-TRACK DELAYijt = α0 + β1 PRECIPijt + β2 TEMPijt + γ1 CAP UTILijt

+ γ 2 SLOW ORDERijt + γ 3 MECHijt + γ 4 LATE ARRijt 

+ δ1 DISTANCEi + δ 2 TURNi + η1 MEET UPjt 

+ η2 MEET NSjt + θ1 BNSFi + θ2CNi + θ3 CSXTi 

+ θ4 NSi + θ5 UPi + θ6 MULTI HOSTi 

+ ∑=

7

l 2

λl FISCAL YEARlt

where i indexes the station-pair, j the route, and t the month. The Greek letters 

represent the coefficients which quantify the effect of each variable on Amtrak 

delays. β’s control for weather related delays. γ’s represent non-weather related 

delay determinants with both cross-sectional and time series variation. δ’s are 

associated with time invariant variables. η’s represent the effects of meetings 

between Amtrak and host railroads to address delays, θ’s are dummy variables for 

individual host railroads, and λ’s capture fiscal year-specific effects. 

This model was also used for short-distance routes with MEET UPjt and 

MEET NSjt excluded, because the meetings were unrelated to short-distance 

routes. MEET UPjt equals one if route j is California Zephyr and if t is later than 

April 2007, the date UP and Amtrak met to discuss addressing delays on that 

route. MEET NSjt equals one if route j is either Capitol Limited or Lake Shore

Limited and t is later than June of 2007, marking a meeting between NS and 

Amtrak to address delays on those routes. 

We generated seven host railroad dummy variables to capture the effects of 

individual railroads and groups of railroads: UPi, BNSFi, CSXTi, NSi, CNi, 

MULTI HOSTi, and Other RRi. UPi takes the value 1 if Union Pacific is the host 

railroad for the O&D station-pair i and 0 otherwise. The rest of the individual host 

dummies are defined in the same fashion. The definitions for MULTI HOSTi and 

Other RRi are slightly different. MULTI HOSTi takes the value 1 if there is more 

than a single host railroad for a given station-pair. Other RRi represents the group 

of other, primarily short line and regional, freight railroads. 

Other RRi is the reference group and its dummy is deleted from the estimation. 

The coefficients on the remaining host dummies measure the difference in delays 

relative to Other RRi. These dummy variables capture the time invariant 

characteristics specific to each host railroad. They can include, but are not limited 

to, host business model, managerial ability, and dispatching practices. 
28

Exhibit B. Detailed Scope and Methodology

CAP UTILijt is the maximum monthly level of the capacity utilization rate 

between station-pair i, on route j, in month t. It is the maximum ratio of traffic 

volume to capacity, both measured in carloads, observed between the station-pair. 

An increase in the level of capacity utilization is expected to result in an increase 

in train delays. Therefore, we expect a positive coefficient on CAP UTILijt.

To capture variation in minutes of delay due to weather, we include precipitation 

(PRECIPijt) and temperature (TEMPijt).8 PRECIPijt is the percent of days during 

which at least one inch of rain fell at station-pair i in month t. TEMPijt is the 

percentage of days with temperatures exceeding 90 degrees for station-pair i in 

month t. The weather variable for each station-pair was measured at the weather 

station closest to the mid-point between the station-pair. Trains operate at reduced 

speed during heavy rains. Also, most freight railroads have heat order policies that 

reduce train speeds when the temperature exceeds certain thresholds.9 

SLOW ORDERijt is the average minutes of slow order delays from all the trains in 

our sample operating at station-pair i, on route j, in month t. A slow order is a local 

speed restriction issued by a host railroad that requires trains to travel at less than a 

track segment's normal speed limit. Several triggers can result in slow orders: poor 

infrastructure conditions, infrastructure maintenance or improvement, and 

weather-related issues.10 Our data includes all except weather-related slow orders, 

and came from Amtrak's conductor delay reports. 

MECHijt is the average minutes of delays resulting from mechanical problems 

with all the Amtrak trains in our sample operating between station-pair i, on route 

j, in month t. The data for this measure was also taken from Amtrak conductor 

delay reports. 

LATE ARRijt was used to capture delays induced by trains running well outside 

their scheduled time slot. It is defined as the percent of trains that arrive more than 

two hours late at the origin station of station-pair i, on route j, in month t. 

LATE ARRijt could be endogenous because factors not captured in our model 

could be expected to cause late arrivals. We use the percentage of trains that arrive 

late rather than the actual minutes of delay to minimize this potentiality.

 8 We obtained our weather data from the National Climatic Data Center. 9 Amtrak provided data on the freights' heat-order policies. Different freights have different thresholds for imposing 

heat-related speed restrictions. We use 90 degrees since it is the minimum threshold across the various freight 

railroads' heat-order policies. 10 Rail infrastructure includes, but is not limited to, track, ballast, signal systems, and sidings.
29

Exhibit B. Detailed Scope and Methodology

DISTANCEi is the weighted average distance between station-pair i, with the 

weights being the percentages of train and date combinations.11 An increase in 

distance increases the probability of a delay event occurring, including as a result 

of factors not otherwise captured. Among these factors are delay-inducing track 

configurations, such as crossovers.

TURNi takes the value 1 if the origin station of the station-pair i on route j is an 

Amtrak crew base. A crew base is a location at which Amtrak changes crews. 

Finally, we include fiscal year dummy variables to control for delay changes due 

to time trends that affect the whole system. These could include changes in 

Amtrak or host railroad practices, Amtrak ridership, and overall network 

congestion. FISCAL YEAR2t takes the value 1 if month t belongs to fiscal year

2002 and 0 otherwise. FISCAL YEAR3t through FISCAL YEAR7t are generated in 

a similar fashion. 

ESTIMATION

We chose the Hausman-Taylor estimator for our panel data after a series of tests.12

The tests showed fixed effects estimators to be preferable and the variable 

CAP UTILijt to be endogenous. The Hausman-Taylor estimator allowed us to 

address the endogeneity issue and to obtain coefficients for our time-invariant 

variables, such as host effects. We also tested the equality of coefficients across 

the two samples for long- and short-distance routes. The difference in coefficients 

confirmed our expectation that we need to estimate separate models for the two 

groups. Table B1 summarizes the estimation results. The reported bootstrapped 

standard errors are cluster-robust.13

There are several reasons to expect that CAP UTILijt has some degree of 

measurement error. It was constructed using data from multiple sources and 

several assumptions. Most notably, translating the Carload Waybill data into 

freight traffic by station-pair required the use of a routing algorithm, in our case 

the one underlying TRAGIS. Use of the Waybill data also required assumptions 

about the percentage of actual freight traffic the Waybill sample represented. It 

 11 We obtained the distance information from Amtrak. It is weighted because of the slight changes in distance over 

time in approximately 40 percent of the station-pairs. We suspect that this difference in reported mileage occurs

because of small changes in the methodology used in recording it. 12 We performed an F-test on the significance of station-pair effects. The rejection of the null hypothesis of no stationpair effects indicated that the Fixed Effects (FE) estimator is preferable to the OLS estimator. To compare Random 

Effects (RE) and OLS estimators, we ran the Breusch-Pagan LM test and rejected the null hypothesis of no stationpair effects. This led to the RE estimator being preferred to the OLS estimator. We ran a bootstrapping Hausman test 

on each time-varying variable to compare FE and RE estimators. The results suggest that the variable CAP UTILijt is 

endogenous in our long-distance model. 13 We compared OLS and cluster-robust standard errors. The significant differences between them on most of the 

variables in our model indicate the existence of clusters in the independent variables.
30

Exhibit B. Detailed Scope and Methodology

was further assumed that capacity did not change during the sample period along 

Amtrak routes. Lastly, we summarized the information on capacity utilization 

levels for all the track segments between each station-pair using a single measure, 

the maximum. All these considerations could be expected to have greater impact 

on the long-distance model as long-distance routes have many more track

segments because of having almost twice as much distance between station-pairs 

on average as the short-distance routes. Consequently, we were not surprised that 

tests showed CAP UTILijt to be endogenous.

SENSITIVITY ANALYSES

We performed a series of sensitivity analyses to check the robustness of our 

findings. To test for the possibility of a nonlinear conditional expected value for 

the dependent variable resulting from the truncation of delays near zero, we reestimated our model using a Tobit procedure.14 The results deviated very little, if 

at all, from those reported above. To investigate the possibility of a nonlinear 

relationship between minutes of delay and the level of capacity utilization, we 

tested the significance of various thresholds for capacity utilization--the 50th, 

60th, 70th, 80th, and 90th percentiles-in our models. 15 The results did not suggest 

a nonlinear relationship in our data. 

We also experimented with augmenting capacity utilization by adding a traffic

heterogeneity variable. An increase in traffic heterogeneity, or traffic mix, has 

been shown to increase train delays (Dingler et al, 2009). This occurs because 

different types of traffic travel at different speeds. The more diverse the speeds of 

the trains on a track segment, the more complicated the task facing a dispatcher 

trying to coordinate their movements.

 

We generated two variables to capture traffic heterogeneity: HET1ijt and HET2ijt. 

HET1ijt is similar to the Herfindahl-Hirschman index. It is the sum of squares of 

service frequencies of different types of traffic, measured by the number of cars, 

for all the traffic at station-pair i, on route j, in month t.

16 HET2ijt is defined in the 

same fashion except that we weighted the components of the sum by the average 

speed of each type of traffic. We chose the minimum value of the index across all 

track segments between a station-pair for HET1ijt and HET2ijt, because the 

segment with the minimum heterogeneity index has the most diverse traffic.17

 14 Only 0.26 percent of delays were recorded as zero.

In

both the long- and short-distance models, neither HET1ijt nor HET2ijt was 

15 Krueger (1999) found that the relationship between train delay and traffic volume can be expressed as an exponential 

equation. Since traffic volume is the numerator for our congestion variable, this led us to consider the possibility of a 

nonlinear relationship between the level of congestion and the average on-track delays. 16 The types of traffic considered were passenger, commuter, and four types of freight trains. 17 We also experimented with defining HET1ijt and HET2ijt as the average value of all the links in any given stationpair. The results did not change. 
31

Exhibit B. Detailed Scope and Methodology

statistically significant, nor did they notably change the estimated coefficients on 

CAP UTILijt.

We tried redefining LATE ARRijt to indicate that a train arrived either three or 

four hours later than scheduled. On long-distance routes, the estimated coefficient 

on LATE ARRijt increased by 9 percent using the three-hour definition and by 

12 percent using the four-hour definition. On short-distance routes, the estimated 

coefficient on LATE ARRijt indicating a three-hour late arrival was not significant 

and a four-hour late arrival was marginally significant at the 10 percent level. The 

remaining coefficients change very little with the various definitions of 

LATE ARRijt in both the long- and short-distance route models. We expected the 

impact of late arrivals on delays to increase with the degree of lateness, and such 

results were observed in long-distance routes. However, trains rarely arrived more 

than two hours late in our short-distance route sample.

Alternative thresholds were also tested for the weather variables. PRECIPijt is 

defined to be the percentage of days having greater than or equal to an 

accumulation of one inch of precipitation, which represents the 97.5th percentile 

in our sample. Cut-offs at the 85th, 90th, 95th percentiles, and average 

precipitation, were tested. The estimated coefficients in the long-distance model 

were 26 percent to 56 percent smaller than those on the precipitation variable as 

first defined, but were still significant at the one percent level. Similarly, in the 

short-distance model the estimated coefficients on the alternative measures were 

48 percent to 64 percent smaller than with the initial definition.

For TEMPijt, which is currently defined as the percentage of days on which 

temperatures greater than or equal to 90 degrees are registered, we tested 

thresholds of 95, 100, and 110 degrees. The results showed that the higher the 

threshold, the greater the effect on delays on long-distance routes. However, the 

effect was insignificant using a 110 degree threshold, as temperatures above 110 

degrees rarely occur. On short-distance routes, the coefficient on the temperature 

variable increases by 28 percent using a 95-degree threshold, and is significant at 

the 5 percent level. It becomes insignificant with a 100-degree definition. The 

coefficient becomes negative with an increase in absolute size of 116 percent 

when using a threshold of 110 degrees, and is significant at the one percent level.

REFERENCES

Clarke, D. B. (1995): "An Examination of Railroad Capacity and its Implications 

for Rail-Highway Intermodal Transportation", Ph.D. Dissertation. The University

of Tennessee, Knoxville.
32

Exhibit B. Detailed Scope and Methodology

Dingler, M. H., Y-C. (R). Lai, C. P. L. Barkan (2009): "Impact of Train Type 

Heterogeneity on Single-Track Railway Capacity", Transportation Research 

Record: Journal of the Transportation Research Board, 2117, 41-49.

Krueger, H. (1999): "Parametric Modeling in Rail Capacity Planning", 

Proceedings of the 1999 Winter Simulation Conference, 2, 1194-1200.
33

Exhibit B. Detailed Scope and Methodology

Table B1: Hausman-Taylor Estimators for Long-Distance and 

Short-Distance Samples

Variable Long-Distance Routes Short-Distance Routes

Precipitation 2.836*** 3.108***

(0.614) (0.774)

Temperature 0.804*** 0.457***

(0.161) (0.159)

Capacity Utilization 1.869*** 2.544**

(0.692) (1.041)

Slow Order 0.896*** 1.128***

(0.0322) (0.0438)

Amtrak Mechanical Failure 0.156*** 0.200***

(0.0570) (0.0694)

Late Arrival 2.843*** 3.648**

(0.304) (1.557)

Meet UP 0.682

(0.649)

Meet NS 0.158

(2.185)

Distance 0.781*** 1.895***

(0.0575) (0.131)

Turn Point 1.447*** 1.457**

(0.451) (0.586)

UP 3.669*** 0.945*

(0.583) (0.527)

BNSF -1.902*** -1.805***

(0.583) (0.598)

CSXT 2.575*** -0.572

(0.507) (0.924)

NS 1.812*** -0.485

(0.555) (0.602)

CN 0.707 1.010

(0.548) (0.708)

Multiple Hosts 7.078*** 5.233***

(1.466) (1.236)

FY 2002 -0.0956 -0.804***

(0.203) (0.173)

FY 2003 -0.294 -0.681***

(0.187) (0.176)

FY 2004 -0.136 -0.600***

(0.162) (0.150)

FY 2005 -0.0365 -0.655***

(0.137) (0.124)

FY 2006 0.568*** -0.228*

(0.110) (0.121)

Observations 48649 16035

Number of Station-Pairs 810 307

 Notes: *Significant at the 10 percent level. **Significant at the 5 percent level. 

 ***Significant at the 1 percent level.
34

Exhibit C. Confidence Intervals on System-wide Delay Estimates, in 

Seconds, by Cause

EXHIBIT C. CONFIDENCE INTERVALS ON SYSTEM-WIDE DELAY 

ESTIMATES, IN SECONDS, BY CAUSE

Long-Distance Services 

Variable Estimate 95% CI Lower Bound 95% CI Upper Bound

Slow Orders 115 107 123

UP Host Effect 50 34 66

CSXT Host Effect 38 23 53

Late Arrival 30 24 36

Capacity Utilization 26 7 45

Turn Point 20 8 33

NS Host Effect 17 7 28

Multiple Host Effect 11 7 16

Amtrak Mechanical 4 1 7

BNSF Host Effect -27 -44 -11

Short-Distance Services 

Variable Estimate 95% CI Lower Bound 95% CI Upper Bound

Slow Orders 63 58 67

Capacity Utilization 24 5 44

Turn Point 16 3 29

Multiple Host Effect 15 8 22

UP Host Effect 12 -1 25

Late Arrival 3 1 6

Amtrak Mechanical 2 1 4

BNSF Host Effect -24 -39 -8

Note: The confidence interval for each estimate provides a range that the true value of the 

variable would be expected to fall within 95 percent of the time in the sample period. 
35

Exhibit D. Amtrak Routes

EXHIBIT D. AMTRAK ROUTES

ROUTE ENDPOINTS

California Zephyr Chicago, IL Emeryville, CA 

Capitol Corridor Auburn, CA San Jose, CA 

Capitol Ltd Washington, DC Chicago, IL 

Cardinal Chicago, IL New York, NY 

Carolinian New York, NY Charlotte, NC 

Cascades Eugene, OR Vancouver, BC 

City of New Orleans Chicago, IL New Orleans, LA

Coast Starlight Seattle, WA Los Angeles, CA 

Crescent New York, NY New Orleans, LA 

Empire Builder Chicago, IL Seattle, WA 

Heartland Flyer Oklahoma City, OK Fort Worth, TX 

Hiawatha Chicago, IL Milwaukee, WI 

Hoosier Chicago, IL Indianapolis, IN

Illinois (a) Chicago, IL Carbondale, IL 

 (b) Chicago, IL Quincy, IL 

 (c) Chicago, IL St. Louis, MO 

Illinois/MO Chicago, IL Kansas City, MO 

Kentucky Cardinal Chicago, IL Indianapolis, IN 

Lake Shore Ltd Chicago, IL New York, NY 

Michigan Chicago, IL Pontiac, MI 

Missouri Kansas City, MO St. Louis, MO 

Pacific Surfliner San Luis Obispo, CA San Diego, CA 

Pennsylvanian New York, NY Pittsburgh, PA 

Piedmont Charlotte, NC Raleigh, NC 

San Joaquin Sacramento, CA Bakersfield, CA 

Silver Service (a) New York, NY Savannah, GA 

 (b) New York, NY Miami, FL 

Southwest Chief Chicago, IL Los Angeles, CA 

Sunset Ltd (a)a Orlando, FL Los Angeles, CA 

 (b)b New Orleans, LA Los Angeles, CA 

Texas Eagle Chicago, IL Los Angeles, CA 

Three Rivers Chicago, IL New York, NY a October 2001 to October 2005 b October 2005 to Present
36

Exhibit E. Contributions to Average Station-Pair Delays, in Minutes, on Individual Long-Distance Routes 

by Cause

EXHIBIT E. CONTRIBUTIONS TO AVERAGE STATION-PAIR DELAYS, IN MINUTES, ON 

INDIVIDUAL LONG-DISTANCE ROUTES BY CAUSE 

Services Capacity 

Utilization

Slow 

Orders

Amtrak 

Mechanical

Late 

Arrival

Turn 

Point

UP 

Host

BNSF 

Host

Multiple 

Host

CSXT 

Host

NS 

Host

Net 

Effect

California Zephyr 0.48 3.43 0.07 0.88 0.38 2.02 -0.83 0.11 0 0 6.55

Capitol Ltd 0.36 1.29 0.11 0.51 0.31 0 0 0.25 0.98 1.06 4.87

Cardinal 0.17 1.26 0.07 0.46 0.26 0 0 0.48 2.16 0.17 5.03

Carolinian 0.20 0.97 0.02 0.23 0.39 0 0 0.23 0.96 1.07 4.09

City of New Orleans 0.33 1.73 0.05 0.16 0.40 0 0 0.20 0 0 2.86

Coast Starlight 0.21 4.67 0.06 0.91 0.37 3.07 -0.28 0 0 0 9.01

Crescent 0.37 0.84 0.04 0.31 0.32 0 0 0.13 0.05 1.74 3.81

Empire Builder 0.83 0.89 0.07 0.20 0.38 0 -1.43 0 0 0 0.93

Illinois/MO 0.28 1.53 0.02 0.13 0.15 3.17 0 0.58 0 0 5.86

Lake Shore Ltd 0.33 0.90 0.07 0.54 0.41 0 0 0.50 1.16 0.87 4.78

Silver Service 0.20 1.48 0.04 0.55 0.35 0 0 0.08 2.21 0.03 4.93

Southwest Chief 0.62 0.65 0.13 0.26 0.36 0 -1.87 0.11 0 0 0.27

Sunset Ltd 0.94 3.08 0.12 1.23 0.33 2.25 0 0 0.99 0 8.94

Texas Eagle 0.37 4.57 0.05 0.57 0.22 2.78 -0.27 0.56 0 0 8.85

Three Rivers 0.56 0.83 0.12 0.25 0.23 0 0 0.28 0.80 1.17 4.24
37

Exhibit F. Contributions to Average Station-Pair Delays, in Minutes, on Individual Short-Distance Routes 

by Cause

EXHIBIT F. CONTRIBUTIONS TO AVERAGE STATION-PAIR DELAYS, IN MINUTES,

ON INDIVIDUAL SHORT-DISTANCE ROUTES BY CAUSE

Services Capacity 

Utilization

Slow 

Orders

Amtrak 

Mechanical

Late 

Arrival

Turn 

Point

UP 

Host

BNSF 

Host

Multiple 

Host

Net 

Effect

Capitol Corridor 0.27 0.95 0.02 0.01 0.31 0.95 0 0 2.50

Cascades 0.60 1.78 0.05 0.03 0.56 0.26 -1.32 0 1.96

Heartland Flyer 0.85 3.11 0.01 0.04 0.24 0 -1.80 0 2.45

Hiawatha 0.08 0.29 0.03 0 0.42 0 0 0 0.82

Hoosier 0.30 2.28 0.12 0.13 0.29 0 0 1.05 4.17

Illinois 0.37 0.87 0.04 0.03 0.20 0.26 -0.49 0.30 1.58

Kentucky Cardinal 0.26 1.51 0.08 0.33 0.29 0 0 1.05 3.52

Michigan 0.27 1.04 0.03 0.09 0.31 0 0 0.71 2.45

Missouri 0.57 2.36 0.01 0.22 0.17 0.95 0 0 4.29

Pacific Surfliner 0.24 0.17 0.03 0.01 0.24 0.10 -0.21 0 0.58

Pennsylvanian 0.65 0.42 0.07 0.13 0.21 0 0 0.06 1.55

Piedmont 0.27 0.63 0.01 0.01 0.19 0 0 0 1.10

San Joaquin 0.75 0.58 0.04 0.04 0.15 0.25 -1.28 0.13 0.65
38

Exhibit G. Major Contributors to This Report

EXHIBIT G. MAJOR CONTRIBUTORS TO THIS REPORT 

Name Title

Betty Krier Program Director/ 

Supervisory Economist

Chia-Mei Liu Senior Economist

Brian McNamara Senior Economist

Jerrod Sharpe Senior Economist

Kang Hua Cao Economist

Susan Neill Writer/Editor