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IMBSIR 79-1724

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«OV5 '** Selecting Rail Properties for

Improvement: A Plan for Analysis

R. E. Schofer

J. F. Giisinn

W. G. Hall

C. R. Johnson

J. M. McLynn (DTM, Inc.) R. H. Watkins (Consultant)

'rrt

Operations Research Division

Center for Applied Mathematics

National Bureau of Standards

U S. Department of Commerce

Washington, DC 20234

September 1978

Issued October 1 980

IK 7 ? y

Technical Report to Federal Rail Administration

Interagency Agreement AR 75245

NBSIR 79-1724

SELECTING RAIL PROPERTIES FOR

IMPROVEMENT: A PLAN FOR ANALYSIS

R. E. Schofer

J. F. Gilsinn

W. G. Hall

C. R. Johnson

J. M. McLynn (DTM, Inc.) R. H. Watkins (Consultant)

Operations Research Division

Center for Applied Mathematics

National Bureau of Standards

U.S. Department of Commerce

Washington, DC 20234

September 1978

Issued October 1 980

Technical Report to Federal Rail Administration

Interagency Agreement AR 75245

U.S. DEPARTMENT OF COMMERCE, Philip M. Klutznick, Secretary

Luther H. Hodges, Jr., Deputy Secretary

Jordan J. Baruch, Assistant Secretary for Productivity. Technology, and Innovation

NATIONAL BUREAU OF STANDARDS, Ernest Ambler. Director

ABSTRACT

Section 901-(6) of the Railroad Revitalization and Regulatory Reform

Act of 1976 (PL 9^-210) calls for a listing and prioritization of rail

properties to he improved in order to permit high-speed operations . This

report identifies key factors entering the choice of links for such upgrading, and formulates an analytical methodology (and implementation plan)

to assist the decision process.

Keywords: Combinatorial optimization; cost/benefit analysis; high-speed

rail; mathematical models; modal split; network analysis.
TABLE OF CONTENTS

Abstract

Page

i

I. Introduction 1

II. Scope of Work 1*

Section A. Background Information k

Section B. The Problem k

Section C. Required Outputs of 901(6) 4

Section D. Required Output of Project Phase Reported Here 5 Section E. Approach 5 Section F. Constraints of State-of-the-Art 5 Section G. Definitions 5 1. Rail Properties 5 2. High-Speed 6

III. Elements of the Problem and Analysis Approach 7

Section A:

Section B:

Section C:

Section D: Section E:

Section F:

General Considerations 7

A.l Philosophy 7

A. 2 Data Considerations 9

A. 3 Model Approach 10

Costs and Benefits 11

B. l Overview 11

B.2 Classification Yards, Conditions of Roadways,

Current Attainable Speeds, and Sources of Information 13

B.2. a Classification Yard 13

B.2.b Condition of Roadways 15

B.2.c Current Attainable Speeds 16

B.3 Speed-Related Costs 17

B.3.a Capital Costs 17

B.3.b Maintenance Costs 19

B. 3.c Energy Efficiency 20

Diversion of Freight to Rail: Benefits and

Disutilities 22

C. l Fuel Consumption 22

C.2 Air Pollution 22

C.3 Noise 23

C.4 Safety 23

C.5 Effects on Users 25

C . 6 Employment 26

C.7 Highway Maintenance

Model Concept

Initial Analysis and Processing

E.l Quantification of Costs and Benefits

E. 2 Initial Network Screening

Central Optimization Model

F. l Step 1

ii
F, 2 Step 2 44

F.2.a Minimizing Upgrading Costs for a Specified

Level of Upgrading 46

F.2.b Maximizing Service for a Given Budget 51

F.3 Link Improvement Rankings 57

Section G: Rail Passenger Service 60

IV. A Simplified Approach 65

V. Work Plan 67

VI. Bibliography 72

VII. References 82

LIST OF FIGURES

1-1 Average Equipment Trip Cycle 3 III-D.l Flowchart of Basic Methodology 31

III-F.l Elasticity of Rail Market Share With Respect to

Door-to-Door Rail Time: Manufactured Goods 39

III-F.2 Increase in Rail Revenues With Decrease in Rail Times 42

V-l Work Schedule 70

LIST OF TABLES

III-D.l Models and Data Required for Analyzing High-Spped Rail

Improvements 32

III-F.l Rail Shares After Door-to-Door Time Reductions: Manufactured

Goods 40

III-F.2 Percent Increase in Rail Revenue Due to Decrease in Rail

Door-to-Door Time.... 41

III-G.l Rail Shares After Door-to-Door Time Reductions:

Passenger Service. 63

III-G.2 Percent Increase in Rail Passenger Revenue Due to Decrease

in Rail Door-to-Door Travel Time 64

in

i
* i -
I . INTRODUCTION

Section 901 of the Railroad Revitalization and Regulatory Reform Act

of 1976 (Public Law 94-210; Feb. 5, 1976) calls for selected studies directed

toward the general goals of the Act, which are "to rehabilitate and maintain

the physical facilities, improve the operations and structure, and restore

the financial stability of the railway system of the United States, and

to promote the revitalization of the railway system." Among these mandated

studies is one which is to develop "a listing, in order of descending

priority, of the rail properties which should be improved to the extent

necessary to permit high-speed passenger or freight service over such

properties, in terms of the costs and benefits of such improvements and

the reasons therefor." The purpose of the present document is to propose a

plan to implement that mandated study.

Throughout this study, primary emphasis has been given to analysis

and discussion of freight service, with high-speed passenger service

only considered insofar as it interacts with the proposed high-speed

freight service. This approach has been chosen as the one best harmonizing

with the stated overall goals of Public Law 94-210 with their stress on

restoring the financial stability of the rail system. Because the freight

service supplies the financial backbone of the rail system, it should be

the controlling consideration in this study. In addition, a number of

recent studies of high-speed passenger service have given the role of

freight secondary consideration at best. The freight-orientation adopted

here for the mandated study may in part balance these prior studies' emphasis

on planning for high-speed rail service.

1
In designing the study, we have not restricted attention merely

to those physical changes necessary to provide higher speeds for loaded

and empty rail freight cars —although we do intend to provide a detailed

analysis of such changes as will in fact promote higher speeds along the

track. We have in general interpreted "high-speed service" to denote

that service which, by whatever means, significantly reduces the current

average (and, secondarily, the variability) of shipping times. Figure 1

illustrates the need for placing any meaningful study of expedited freight

service in a context of changes in pick-up and distribution systems, and

especially of improvements in terminal and intermediate-yard procedures.

The ideal methodology to be employed in the study would be cost/benefit analysis, which might in theory provide adequate criteria for a project-by-project ranking of selected properties down to an established cutoff point at some level of economic viability for the rail system. But, a

detailed line-by-line analysis of the costs of rehabilitation and of the

monetary and nonmonetary returns will not be possible within the time con- straints of the mandated study. Therefore, we base our approach on a

cost/revenue analysis supplemented with quantification-as-possible of

supplemental costs and benefits, ranging from the relatively easy-toevaluate effects of unpriced time savings to the more qualitative consideration required for certain environmental impacts.

2
Figure 1-1

AVERAGE EQUIPMENT TRIP CYCLE

Average Cycle Time 25.6 Days

SOURCE:

Federal Railway Administration

Report: FRA-OE-73-1

3
II. SCOPE OF WORK

Section A. Background Information

On February 5, 1976, the Railroad Revitalization and Regulatory

Reform (RRRR.) Act was signed into law. Section 901 of this "Rail Act" directs the Secretary of Transportation to conduct? a comprehensive study of the

national railroad system. Section 901(6) calls for a study to produce a listing,

in order of descending priority, of the rail properties which should be improved

"to permit high-speed rail freight or passenger service over such properties,

in terms of the costs and benefits of such improvements and the reasons therefor."

Section B. The Problem

The problem to be addressed is that of determining which rail

properties should be improved in order to permit high-speed railroad freight

or passenger service under various operating levels and assumptions. Key

issues to be considered include the following:

1. The factors affecting high-speed freight and passenger

operations, and their relationships to qualities of

service.

2. The costs and benefits of facilitating high-speed

freight and passenger . operations

.

3. The specific rail properties to be improved to permit

high-speed service under varying assumptions.

Section C. Required Outputs of 901(6) .

For each of various possible operating levels and assumptions, a

listing (in descending priority by cost/benefit criteria) of railroad

properties to be improved to permit high-speed railroad freight and passenger

service.

k
Section D. Required Output of Project Phase Reported Here

A report (the present document) identifying critical elements of the

problem, and developing a study plan and methodology which can be implemented

to produce the set of listings described in item C above.

Section E. Approach

The study plan must be designed and developed to take time and

resource constraints into account, so that major emphasis is laid upon utilizing

existing engineering and operational data and readily—available analytic tools.

Section F. Constraints of State-of-the-Art

State-of-the-Art constraints are binding at two points relating to

the mandated 901(6) study: ( l) with respect to existing data and software; and

(ii) with respect to the operational technologies of existing railway rollingstock. The justification for these constraints is the immediacy of the problem.

U.S. railroads are in a crisis NOW. This crisis must be alleviated NOW. A

means for identifying those properties on which cost and benefit considerations

suggest priority for raising operating speeds is required as soon as possible

so that needed action can be formulated promptly. The study is to be constrained

to existing railroad technologies because of the large investments in the

existing massive physical plant. Such a plant is capable of operation at a

much higher level of service, one that will provide very satisfactory service

to a great majority of users.

Section G. Definitions

1. Rail Properties. The usual connotation of "rail properties' is

all the real property and rolling stock belonging to a railroad. For purposes

of this report, however, rail properties are defined as trackage links

(trackage between intersections with other trackage) and collections of such

trackage. Other classes of properties are being addressed in other Section 901

studies

5
2. High Speed. The appropriate definition of high speed is

determined by the constraints imposed by use of existing railroad operating

technologies. Extensive consultation with acknowledged rail experts suggested

the operational definition of 60-80 miles per hour for freight service and

90-110 miles per hour for passenger s ervie e . This would permit continued

use of most existing rolling stock. Higher freight speeds would cause

excessive fuel consumption. Higher passenger speeds would preclude commingling of freight and passenger trains on the service trackage and would require development and acquisition of new rolling stock.

6
III. ELEMENTS OF THE PROBLEM AND ANALYSIS APPROACH

For this study of the key elements of high-speed rail service, a

great deal of background information was obtained from a close review of

the literature. A bibliography appears in Section VI. Also, many of

the ideas mentioned result from discussions with the following people:

J. Schofer, Northwestern University; C. Fisher, Federal Rail Administration; B. George, FRA Office of Safety; S. Ditmeyer, World Bank; H.

Jones, Evaluation Technologies, Inc.; C. Hoppe, Booz, Allen & Hamilton;

E. Dwyer, Union Switch and Signal; A. S. Lang and J. McLellan, Association of American Railroads; and T. Dyer and G. Hale, T. K. Dyer, Inc.

Of course, neither these persons nor the firms, institutions, or agencies they represent, are responsible for our representation or interpretation of information gained from them.

Section A. General Considerations

Section A.l Philosophy

The methodology proposed here directs itself to the selection of rail

properties for upgrading for high-speed service; but it must be kept in

mind that 901.(6) is only one section of the "Rail Act." The approach

chosen, therefore, must accommodate the results of studies of other

issues specified by the Act. Several of those studies (especially those

of terminals and yards and of deferred maintenance) relate closely to the

high-speed issue. In the present study, our emphasis will be on linehaul high speed, which we define as 60-80 miles-per-hour for freight and

90-110 miles-per-hour for passenger service. Other railroad operations

that touch upon the high-speed issue will be treated in an aggregated or

parametric way, to permit use of the results of other studies as they

appear.

7
The rail properties treated here as candidates for upgrading are

the line-haul links (or natural clusters of such links); thus appro- •

priately detailed inputs are required for links now in the system.

Specific links and aggregates of links will be ranked according to

their feasibility for upgrading. Otherwise, the proposed models represent a macro approach to the upgrading problem. Costs incurred and benefits accrued will be attributed only to aggregated groups and/or geographic areas.

Since 1985 is the target date for the upgraded network, only incremental changes will be considered. This time limit precludes serious

consideration of major long-term developments , such as plant relocations, changes in land use, alterations in regional development policies, technological changes, and major new induced demands. We must

assume that tariffs and regulations will remain more or less constant,

or that they will change rationally and coherently as a result of transportation policy. We do not consider the possibility that transportation policy might suddenly become subordinate to some other national

concern, such as defense.

Since we expect the origin-destination markets for high-speed

freight service to be different from those for high-speed passenger

service, the goals of high speed for each may prove antagonistic. Should

this antagonism manifest itself, we take it as our function in this study to

treat the claims for high speed freight service as superior . This is con- sistent with the Rail Act's clear promotion of a rail system that is economi8
cally viable—or as nearly so as is commensurate with national needs. Economic health of the rail system must be based on freight revenues; the potential for profit in passenger operations is limited to a handful of corridors. However, certain benefits to passenger service should follow from

the upgrading of trackage for faster freight service.

Section A. 2 Data Considerations

This problem presents formidable data requirements. The number of

links to be considered is very large, about 8800; and, for costing purposes, the required data for each link are numerous if comprehensiveness

is to be attained. But such a situation is part and parcel of any

costing process, and it is made no worse than usual by the approach we

propose. Ideally, we would have at least enough information to calculate

the cost to upgrade a chosen link to support several speeds faster than

the current one. However, these very detailed engineering data do not

exist and cannot be gathered within our time and resource limits. We must

estimate such costs from whatever information is available on the current

condition of the system and from what might be called "generic engineering

data". The use of estimated costs carries some risk, but it is risk that

cannot be avoided. The most we can do is hedge our bets whenever possible.

Some of the procedures for doing this will be identified and described in

Section B.2.

The data available for estimating benefits are more nearly adequate

and appropriate to our purposes. The estimating procedure for these has

strong traditions in the study of highway projects [1,2,3]; and the benefits in question tend to be aggregated.

9
Section A. 3 Model Approach

In any competent modelling and analysis effort, some sensitivity

analyses must be made even if both the structure and content of the data

are well established. For the present problem, the usual sorts of

sensitivity analyses are adequate for benefits, and usually so for

operating costs. However the sensitivity analysis of capital costs

requires special "meta-parametric" treatment. By this we mean that we

not only suspect the numbers, but we are unsure how the costing should

be structured. It might be necessary to consider a number of different

structures in order to establish the "reasonable range" of capital cost

estimates. Alternative capital costing procedures should be applied to

candidate links. The exercise of the model might prove that benefits

and costs are relatively insensitive to distinctly different capital

cost structures, at least on a system basis. However, if the capital

costs developed from alternative procedures are in disagreement, and the

model proves sensitive to them, the disagreements must be reconciled. One

of them must be accepted, or some way must be found to combine them.

Although we are preoccupied with line-haul links, the submodel

structure we propose would also accept other types of components likely

to be generated by concurrent studies. In anticipation of important

results from the yard study, especially, more sophisticated treatment of

yard and terminal activity will be possible and practical within the

framework of the model structure. With some changes in the definition

of the network, the model could be adapted to analyze the implications

of upgrading only the yard or terminal facilities.

10
Section B. Costs and Benefits

Section B.l Overview

It is very hard to define satisfactory measures of costs and Benefits for a realistic evaluation of transportation policy alternatives.

In a study with tight time limits, we cannot expect to make major advances in method or concept—advances needed for the satisfactory quantification of the totality of social costs and benefits. Therefore,

throughout Section B we shall indicate where we can establish a cautious, common-sense reliance on established bodies of information.

Selecting properties for upgrading can be thought of as the reverse

of selecting properties for abandonment. Therefore, we consulted the

extensive methodology of the comprehensive "Phase II" rail plans developed within states for guidance in choosing useful cost-benefit

categories . Plans from Pennsylvania [ b ] , Michigan [ 5 ] , and Iowa [ 6 ]

were closely examined, and a cursory review was made of rail plans for

other states. This survey, though illuminating, brought no direct solution to the problem. Quite naturally, given their purpose, these

studies count heavily the direct effects of abandonment on shipper

employment, railroad employment,- and local tax bases, as well as indirect (multiplier) effects. Such effects, in the context of the whole

economy, are mainly transfer payments—unsuitable for direct entry into

a cost-benefit analysis at the macro level we envision.

11
It is clear that certain other effects must assume importance in a

national study; these are strongly related to volume, transit time, reliability of transit time, revenues, and other matters associated with

diversion between transportation modes. (The extent of such diversion,

is critical in the areas of noise and air pollution, safety, and congestion. ) The usefulness of the state studies is diminished in these

regards because of the great differences in traffic densities between the

segments considered for abandonment in those studies and those to be considered for upgrading in a national study.

A revenue output, calculated from the National Network's traffic

approximating model, will be a direct output of our procedure and should

serve as a basic measure of system viability. (However, if cost-revenue

considerations alone justified improved service, the railroads quite possibly could already have attracted the capital to provide such service

without government involvement.) Another benefit that can be computed

fairly directly from this approach is the value of time and cost savings,

insofar as that value is not reflected in revenues.

A considerable literature (mainly passenger studies [ 7,8,9,10])

is devoted to the money value of transportation time savings. Variants

of procedures presented there can be applied to the valuation of freight time

savings, although careful distinctions among categories of commodities

to be shipped are necessary to make the results realistic. In many

cases, the transportation pipeline is viewed as an extension of the

warehousing facilities of both shippers and receivers. Predictability

of departure and arrival times can be more important than a decrease

12
of mean transit time. Therefore, an important benefit of upgrading for

speed might be, not increased speeds as such, but the contribution of

increased speeds to the reduction of the currently high variability of

transit times.

The benefits of more predictable and less variable transit times are

obvious, and are direct for shippers and receivers: predictable demurrage,

more options as to order lead-time and inventory strategies , and direct

cost savings. For the carriers themselves, the benefits are less certain and only indirect. Theoretically, predictability would allow

innovations that would make rail freight service more attractive, especially for high-value, high-tariff commodities. Perhaps predictability

would allow more operational flexibility in general, but the demand for

predictability would in turn place further stresses upon the system.

Presumably, these stresses would differ as much as railroads differ in

their operational resources and adaptibility.

Section B.2 Classification Yards, Conditions of Roadways, Current

Attainable Speeds, and Sources of Information

Section B.2.

a

Classification Yards

The time a freight car spends in a classification yard is virtually

irretrievable. It is never made up by route speed. Therefore, the

distribution of throughput times among yards, the possibility of bypassing yards, and the number of yards through which a car is likely to

pass are all important in any study of the value of high-speed links.

13
Even on long hauls, a car is usually classified only a very few times

(ranging from 1 to 3 or 1* in the Conrail system). However, Figure 1

shows that times spent in yards can he very long, averaging from 8-12

hours in ’’modern" yards [ 11,12,13,14,15] to more than twice that time

in older yards. Utilization studies show that loaded cars spend nearly twothirds of their time in yards and a correspondingly small portion of

their time moving, often 10$ and usually no more than 20$. (This effect

is much greater for eastern roads. ) Yards might he the most significant

object of consideration in an effort to reduce transit time. Therefore,

the greatest net contribution of upgrading for higher link speed might

result from upgrading to support some speed less than the nominal high

speed if current conditions are remarkably poor and speeds excessively

slow. By treating yards as probabilistically parametrized impedances, it

will be possible to combine shortest-path and Monte Carlo techniques to

investigate what decreases in yard times are necessary if increased link

speeds are to have a noticeable effect on system performance.

As to the data for such an analysis, railroads generally record

throughput times in their own yards, but comprehensive national figures

are unavailable. There are studies of single yards, and additional

detailed case studies will be produced in the course of work required by

Section 901.(2) being done by C. Hoppe at Booz, Allen & Hamilton. Also,

there is methodology for simulation of yard operations (Stanford Research Institute [l6], etc.), and substantial throughput data for major

yards will be gathered for the 901.(2) study. Therefore estimates of

current yard impedances and ranges of possible improvements should be

available, either as outputs of 901.(2) or by study of better times at

better yards— or both.

14
Section B.2.t> Condition of Roadways

Some segment- specific information on the current conditions of track

and roadbed should be part of the process of estimating costs for upgrading. Ideally there should be an engineering study of each candidate

link. Such information, however, is not available, and its acquisition

would require time and money well beyond this study's limits.

Railroads know, or can ascertain, the condition of their own track

and the FRA Office of Safety speed-limit classification of their own

links; but this information does not now exist in a central data base.

Given these circumstances, and our time and funding limits, we are

left to choose between modelling in a manner insensitive to current

roadway conditions or determining and employing surrogates for those

conditions. Two possible surrogates are (i). deferred maintenance and

(ii) currently attainable speeds (discussed below in B.2.c).

It has been pointed out [IT ] that for the purposes of comprehensive

policy analysis four existing studies of deferred maintenance have major

deficiencies. The difficulty is simply that a consistent body of

link-specific data cannot be derived from these studies. However, the

preliminary report on deferred maintenance, pursuant to Section 504 of

the Rail Act, is about to appear. From this report broad conclusions

about roadway conditions might be drawn, using suitable assumptions

about the distribution of deferred maintenance between main lines and

branch lines. (Historical trends from the data bank of Thomas Dyer, Inc.,

might be helpful.) It may be supposed that the more viable railroads

15
adequately maintain their main lines and that deferred maintenance is

more common on branch lines. If this supposition is correct, a third

surrogate for a link's current condition might be a combination of traffic

volume and railroad financial condition. Though less precise than the

other two surrogates, it can be supported from existing data bases. Link

traffic volumes can be obtained from [18], ownership from the data

base of the FRA network model, and railroad financial condition from

Moody ' s

.

B.2.c. Current Attainable Speeds

To upgrade a rail link for high speeds is to lower its minimum safe

traversal time. Clearly some knowledge of currently attainable speeds

is necessary in this study. Also, such speeds are a possible surrogate

for the condition of roadways.

Railroads have working knowledge of the speed capabilities of

their lines, and they must classify segments according to the FRA Office

of Safety's six-step scheme (this need not be reported to a central

authority). Speed is one of the parameters associated with a link in the

FRA network model; but it is not available for every link, and is based

on 1973 (or earlier) Office of Safety classifications. (These classifications change rapidly.

)

The more viable railroads attempt to maintain segments to support

the speeds promised by their timetables. Maximum link speeds are being

inferred from timetables as part of the work done at Texas A&M University to

augment the FRA network model (COTR: Carl Fisher; timetables collected by

16
Thomas Dyer, Inc.). Although the figures so generated will not be

perfect, they will represent the railroads' operational goals.

We cannot rely on them for the mandated study since the studies are

concurrent

.

B. 3 Speed-Related Costs

Analyses of costs and benefits are to be made for several speeds,

some higher than those common for current freight operations. Therefore,

those costs which arise from or change substantially because of increased

speeds require specific treatment. The pertinence of categories of costs

depends on the mix of strategies used to assure high-speed service. Doorto-door transit time can be shortened (and/or its variance reduced) both

by physical changes in the system and by changes in the procedures of

pickup, sorting, and distribution. While direct relations of costs and

speed of freight movement are not well worked out, they are less difficult

o to evaluate and allocate among freight movements than are the costs of

procedural (operational) changes. An immediate effect of increased speeds

should be the more efficient use of rolling stock. Long-range benefits

should follow if rail management exploits the opportunities that improvements in trackage links afford for innovations in scheduling and marketing

B.3. a Capital Costs

In this study it is useful to think of any investment in a roadway

and its operating system as a capital cost if it is necessary before

17
high-speed service can begin. (it should be noted that this does not

conform to ICC accounting procedures, which do not, for example, treat

the laying of track as a capital investment.) Such costs are primarily

for track and roadbed and for signalling systems.

To estimate the costs of track and roadbed one would like a model

that relates the current roadway-condition and topographical characteristics of a segment to the cost-per-mile of upgrading needed for various

speeds. No such model exists, but there are cost studies of single

projects [l9,20], and certain organizations and individuals use rules of

thumb, based on experience, to cost-out significant refurbishing. The

FRA has estimated that on the average $250,000 is required to rebuild a

mile of very poor track into a 60 mph, heavy duty line. Such information

can be updated using the engineering price indices for the relevant

inputs

.

All in all, estimation of capital costs is the most difficult area

in which to find information sufficiently precise, stable, and reliable

to produce discriminant functions- adequate for ranking among linkimprovement candidates; and an engineering study with a cost-model output is beyond the scope of this study. However, after potential projects for upgrading are ranked according to their feasibility with

reference to other costs and benefits, this difficulty will probably

be eased. In some cases it should be clear that benefits are great

enough to overwhelm simple cost considerations; in other cases, benefits will be so small that they could not justify even very low costs.

Such judgments are easier to make within a range than they are to make

of an isolated project.

18
The literature offers several studies concerned with costing the

track and signalling improvements necessary for higher speeds. The first

[21] treats both capital and operating costs on selected routes; and the

second [22] includes a detailed study of capital costs for track and

structures. The above studies are valuable, but their usefulness here is

diminished because they deal with passenger service (which assumes high

speeds and low tonnages inappropriate to a study of freight operations )

So far as signalling costs are concerned, basepoint information is

contained in part in the FRA network model, of which the existing signalling system is a data element. Unlike track and roadbed costs, upto-date estimates of costs for purchase and installation of centralized

traffic control systems are available from suppliers.

B.3.b Maintenance Costs

The estimate of costs for upgrading rail segments for high-speed

operations must consider consequent changes in maintenance costs. Given

sufficient inputs, most of these can be assessed with the Transportation

Systems Center's (TSC) cost model [23], Although very little research has

been done on speed-related costs of maintenance, that study indicates that

maintenance-of-way is a weak quadratic function of operating speeds.

High speeds (and any resulting increase in volume) bring greater

track wear and demand the more frequent replacement of track according

to more rigorous standards. Wear increases on rolling stock also. However,

if locomotives and cars spend less time on a given job and move from place

h

19
to place more quickly, their greater productivity will be a countervailing factor to their increased wear; also, slow-moving stock will suffer

less wear and move more safely over improved tracks and roadbeds.

B.3.C Energy Efficiency

Fuel costs can be estimated with one of several train performance

calculator (TPC) computer programs. [24]. The TPC developed and maintained

by the Electromotive Division of General Motors has been used for this

purpose. A preliminary examination of the results suggests a fuel consumption proportional to the 3/2 power of the speed when speeds are in the

60-120 mph range.

Naturally, the effect of speed on fuel consumption varies greatly

with locomotive types, train configurations, and terrains. The speed of

maximum operating efficiency varies from 70% of top speed for aerated

engines to 95% of top speed for turbo-charged engines. In some configurations, aerodynamic effects are significant at speeds as low as 30

mph. Although increases in speed in the range below 25 mph can lead to

improved fuel efficiency, increases above 35 mph cause increased fuel consumption. Consultation of standard engineering works (e.g. [25]) should

kelp estimation in specific situations.

The application of overall gross statistics to a comparison of the

energy costs of moving goods by rail and by truck^ has produced, in

several studies with differing assumptions , an estimate of 3.5 greater

energy efficiency for rail movements [ 26 ] . (it must be emphasized that

^Railroads nominally compete with air and waterway modes as well. Timetariff relations are such, however, that there is no direct competition between air and rail either at present or in any plausible scenario. Rail

and water compete for certain low-value, low-tariff commodities. Any reasonable improvement in rail speed (unless accompanied by a tariff reduction)

is not expected to affect the modal split greatly.

20
this is a gross figure.) A Project Independence [27] report for TJSRA

uses a somewhat more refined methodology and suggests an upper hound of

2 as a more realistic estimate for the relative energy efficiency of

rail (for diverted shipments).

It should he recalled that energy is expended not only to move

revenue tonnage, hut also to move the carrying vehicles themselves. In

short trains, typical on many light-density rail branches, a high proportion of the energy goes to moving the locomotives alone. Further

dead weight is added when company policy or state law requires cabooses.

In a 197^- DOT comparison [28] of truck and rail fuel efficiencies in

assorted hypothetical configurations, it was concluded that in some

situations truck service is significantly more fuel- efficient than rail.

The break-even point between the two modes varies both with length of

train and with length of haul.

The assumptions of the comparison just mentioned have been criticized because they concern the diversion of branch-line freight, and its

results probably do understate the energy advantages of rail in those

situations which concern us.

Since the evidence suggests that the energy efficiency of rail

relative to highway [29] varies greatly and might fall below unity,

reliable information will require a detailed analysis based on distances

and lot sizes, with at least a rudimentary classification of commodities.

21
Section C Diversion of Freight to Rail: Benefits and Disutilities

C. 1 Fuel Consumption

In extreme cases fuel consumption for rail might he 1/3 or less

that for highway. On the other hand, the per-unit fuel consumption on

rail increases with speed, and becomes superlinear in the 60-80 mph

range [30 ]. There is a trade-off: energy savings for much of the

traffic diverted to rail against increased energy use for traffic

already on rail. One way to assure savings would be to provide highspeed capabilities only to certain trains serving especially speedsensitive shippers, perhaps with differential rates.

C. 2 Air Pollution

If one considers only the "average shipment," the diversion of

goods from truck to rail leads to a significant reduction of pollution

emissions per ton moved. On the average, a ton moved by truck instead

of rail produces six times as much carbon monoxide, 3.5 times as much

oxides of nitrogen, 2 times as much particulate matter, and .5 times

as much hydrocarbons [26].

Of course such figures must be affected by questions of energy

efficiency, since pollution is essentially proportional to the fuel consumed. A detailed analysis may not ignore differences in truck type

and performance. Energy efficiencies vary so much that it is reasonable

to posit some category of freight movement for which trains would yield

over 5 times more hydrocarbons than trucks.

For the evaluation of this cost-benefit element, it probably is

adequate to extend and refine existing methods

22
C. 3 Noise

Real changes in noise pollution would result from the diversion of

traffic from truck to rail. These changes are primarily rearrangements of

the spatio-temporal distribution of noise sources and so they cannot be

called improvements with any confidence. Indeed, the effect of noise

pollution is, in general, a problem that so far resists useful quantification.

Trucks, per unit, are quieter than trains, but the same

tonnage requires more trucks than rail cars. (The typical box car

carries from 1.6 to 2.8 times the load of a typical trailer [26 ]•)

Also, truck routes are more pervasive of a community than rail routes,

and the comparative noise effects of the different route patterns are

hard to evaluate.

Several relevant and appropriate sources [ 31, 32, 33, 3^] have been

identified. The last gives a particularly detailed analysis of comparative noise effects.

C. ^ Safety

Improvements in track, roadbed, and signalling systems in themselves make rail transport safer; but higher speeds with greater volumes

would be something of an offsetting factor. This complicated tradeoff

is poorly understood—naturally enough, given our lack of experience

with high-speed freight.

Using FRA Office of Safety accident literature (e.g., [ 35 , 36,37 ])» it

is possible to make rough comparisons between accident figures for better23
maintained and more poorly maintained track. Certainly track condition is

a significant safety factor. The Northeast Corridor High-Speed Rail

Passenger Service Improvement Project Task 6 Report notes that since

the introduction of Metroliner traffic in 1969 , the rate of freight

train derailments in the Corridor has been 60% below the national average.

In the same context, it should be noted [ 37 ] that in only a year a

single railroad of modest size (Lehigh Valley) suffered derailmentrelated damage losses of over $650,000. Clearly, safety is no small

economic matter.

Perhaps because they resist predictive analysis, safety issues

seldom loom large in rail studies. However, in a high-speed study we

must consider the safety problems attendant to multi-speed operations.

It would be desirable to reduce grade crossings to a minimum on

high-speed routes; and in that regard we must be aware of the fact that,

even if such upgrading takes place, local speed regulations for crossings

in populated areas could cancel at least some of the improvement in some

link transversal times. (The inventory of grade crossings is available

from the FRA Office of Safety and will be added to the network model

from Texas A&M. ) Given heavy tonnages at higher speeds, such operations

as the flow of hazardous commodities through populated areas must be

considered in a new light.

Although we can assume the safer movement of low-speed traffic over

upgraded track, this system benefit cannot be said to offset increased

employee injuries and fatalities if they are significant and directly

related to increased speeds. It will be more dangerous for the public

2b
to intrude upon higher-speed links; also high speeds and volumes bring a

greater likelihood of brush and forest fires caused by passing locomotives. To warn railroad personnel and the general public of increased

dangers will have some cost—probably small and probably part of a

larger safety information program.

The diversion of freight from truck to rail should lead to a reduction in the highway accident rate. A cost benefit analysis can use

the Dyer methodology [2b ] to estimate the cost of holding safety on the

upgraded system to the present system’s standards, and then to evaluate

any increased highway safety that can be attributed to the diversion of

traffic to high-speed rail.

C. 5 Effects on Users

For shippers now using rail transport, lower mean transit times

could reduce inventory costs, especially if such costs include a daily interest rate applied to the value of a shipment. Also, shipments would

not need to be planned so far in advance. To the extent that traffic is

diverted from other modes to rail, increased volume could bring futher

advantages, such as more frequently scheduled service.

Some shippers will lose the "warehouse" advantage of slow service,

but the long range importance of this loss is doubtful. Warehousing is

a somewhat unnatural use of transportation facilities; but if it is discovered that certain shippers need the "warehousing" advantage, special

arrangements might be made—not as a function of line-haul operations

but as a function of yard management.

25
A more awkward, matter is the effect of higher speeds on rates. If

higher speeds bring greater volume, and if greater volume permits lower

rates, shippers (and perhaps their customers) will benefit. On the

other hand, if higher speeds increase operating and maintenance costs,

and if increased costs require higher rates, shippers to whom speedy

shipment means little will suffer ( as will their customers ) . Here again

(as in C.l, above), perhaps some thought should be given to the provision of high-speed capabilities only to certain trains serving especially speed-sensitive shippers, perhaps with differential rates.

C. 6 Employment

In abandonment studies it is common to divide effects on employment into

those pertaining to railroad employment, direct (shipper) employment,

and indirect (multiplier) employment. An uncertain amount of the gross

total arrived at by these measures represent social transfers and should

be omitted in a total cost-benefit analysis. The methodology developed

by PIES [27 ] can be adapted to evaluate the residual effects on employment, if it is decided that they warrant inclusion in this study.

C. 7 Highway Maintenance

Diversion of freight from highway to rail will have some effect on highway maintenance costs. Although not expected to be large, these effects

should be considered in a cost-benefit analysis if they are quantifiable.

26
Section D. Model Concept

Several steps are involved in the proposed approach. The

first involves an initial screening process to identify those

network links which should be studied in greater detail. The aim of

this step is to eliminate from further consideration the links which

are unlikely under any reasonable scenario to be candidates for upgrading

to high-speed status. Although these links may be used to provide

connectivity and access to the main network, they will not have to be

considered in the choice process, thereby cutting down the size of the

problem

.

Several procedures will be used to identify links which are candidates for study in greater detail. The use of multiple objective functions,

both in the initial screening and in the optimization, reflects the fact

that choice of links to upgrade involves many tradeoff decisions which may

be in conflict. Is FRA aiming at improving the rail system to best serve

its current customers, or trying to divert as much business as possible from

other competing modes, primarily trucks? Or is the aim at the least costly

system to meet a specified level of service, or trying to buy as much service

as possible for a given budget level? Any of these goals is conceivable, and

the initial screening should not eliminate links in such a manner as to

preclude any of the corresponding scenarios.

One major initial effort will involve quantification of various

costs and benefits associated with the current rail system and with

possible upgrading of that system. As discussed earlier, much of the

link-dependent data needed to calculate costs of improvement are not

available because current track condition is not known. As a result,

our approach is to develop models using available substitute data, such
as current speeds, to estimate the appropriate costs. When better data

are available, they can be substituted in the process for model-developed

parameters. The link upgrading costs used in the models here will be

general order-of-magnitude estimates. They are not based on terrain or

other engineering details of the particular track segments since these are

not available for the rail system as a whole. The possibility (cf. Section

III B.2.b-c) of estimating current link condition from a combination of

traffic level and the financial condition of the railroad will be explored.

After initial screening, the network data base, cost/benefit quantifications, and commodity predictions are to be inputs to the central optimization model. This model will, for various scenarios defined by input

parameter values and optimization objectives, choose network links and combinations of links to be upgraded. The output of the central model will consist of several improvement projects, defined by the links to be improved

and associated levels of improvement, one for each of the scenarios.

The central optimization model will consist of several submodules,

each of which is itself a currently available model or procedure. Two

steps form the basis for the central model, and its operation consists

of alternating these steps until the link set to be improved remains

constant from step to step. It is expected that this will occur in three

to four iterations at the most, and if not 9 then manual alteration and

analysis will be introduced.

The two steps in the central model are:

1. regarding link capabilities as "given", allocate traffic

(by commodity class) among modes and then assign traffic to

specific rail links;

28
2. for a given allocation and assignment of traffic, choose rail links

to be improved.

The first step will use the Origin-Destination (0-D) commodity flow data, 0-D

travel times, and tariffs to compute the rail share of each 0-D commodity flow.

A link assignment of these flows will then be performed, probably using

the FRA assignment programs. The second step will use link volumes and

cost and benefit quantifications to choose rail links to improve. This

step will utilize various versions of mathematical programming optimization

models to select the projects for improvement.

After the central optimization model has been run for several different scenarios, the output link sets will be further analyzed for

consensus, and a ranking of links to be improved will be produced. It

is unlikely that ranking of each component of the link set will be required,

since much of the cost and benefit data are at too gross a level to permit

such detailed analysis of project alternatives. Thus the expected output of

the modeling approach described here is a classification of links which

differentiates degrees of desirability for multi-link improvement projects

as a function of expected scenarios. The post-analysis of the link sets

output from the optimization process will permit imposition of various

operational factors which may not have been included explicitly in the

optimizations. Possible examples include the assurance of contiguity of

improvement projects, and inclusion of project -staging criteria in the

link orderings. A summary of the costs and benefits associated with the

link improvement projects will be prepared.

Although this process has not included rail improvements to permit

high-speed passenger service, wherever improvements for freight service

occur on links used for passenger service also, some benefit to the pas29
senger service will accrue and will be tabulated. Since speeds for

high-speed passenger traffic would be greater than for high-speed

freight service and track improvements to permit high-speed service are

different for the two, a separate analysis will be performed to ascertain

where improvements in track might have an impact on passenger movements.

This will build on previous passenger corridor analyses and focus on

projected rail passenger share.

Figure III D.l is a flowchart of the basic methodology to be employed.

Models. or processes required are. shown in rectangular boxes; input data,

in six-sided boxes; intermediate data bases indicated by parallelograms. The models and data required in the various stages — initial

screening and analysis, steps 1 and 2 of the central optimization model,

and the link/project ranking and analysis — are also listed in Table III D.l.

The various sections of the flowchart and table will be referenced and

discussed more fully in the next three sections.

Section E. Initial Analysis and Processing

E.l Quantification of Costs and Benefits

Inputs to the central optimization model include numerical costs

and benefits associated with improving rail links to allow high-speed

operation. Two major benefits "drive" the optimization. The first of

these is the amount (ton-miles) of traffic diverted from truck to rail

by rail improvements. Since this represents a financial benefit to the

railroads, the societal value of the additional revenues generated by those

rail improvements is also important. A second benefit measure is the improved

service represented by total time-savings (ton-hours) to those who ship.

Additional benefits, such as energy savings, improved passenger service.

30
Figure III D.l: Flowchart of Basic Methodology
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better equipment utilization, reduced environmental pollution (primarily

air and noise) and improved safety will be quantified, not on a linkby-link basis, but rather in general summary tables associated with

particular link-improvement projects.

Quantifying benefits, though difficult, is not in this application

as difficult as quantifying costs, a situation the opposite of that

which normally occurs. As noted in Section III B.2, the main difficulty in

computing the costs of improving rail links (track and signalling systems)

is the lack of good data on the current condition of track. Because of

known deferred maintenance policies and go-slow orders, the generally

poor quality and deteriorating condition of track is known, but the actual

extent and degree on any piece of track is not systematically recorded.

To get around the lack of direct data concerning the current condition of track, the best available data, on several surrogates whose

values are, or should be, correlated with track condition are to be used.

The first of these is the average link speed available in the FRA rail

network data base. It is known that there are serious difficulties with

this data base, especially with the timeliness of the data, but it is the

only one available on a sufficiently wide scale. Updates and additional

data will be available from work being done now at Texas A & M, but are

not expected to be available until September 1977- Some data on 2% of the

links contained in the CONRAIL system were collected [ 38 ]. Though very

good data on a sample of links in the system, they may only represent one

type of system and may not be generalizable to the whole. In the absence

of good data, it is necessary to use available data and modeling techniques

such as regression analysis to relate the known link data and other characteristics, such as link volumes and railroad company financial viability.

3b
to estimate current link condition. Other models and information available

in the T. K. Dyer report [ 21 ] may also he used in this analysis.

A final comment should he made. All this modeling is predicated on

the assumption that the differences among the currently maintained qualities

of different track links are large enough for there to he significant

differences in the costs to improve the system to high-speed status. This

may not he the case. Pieces of track may have sufficiently similar characteristics that cost differences depend primarily on link length with

little variation resulting from track quality.

Operating costs for upgraded track must also be considered, since

increased maintenance -will he required to maintain high-speed service.

The operating cost model contained in the Transportation Systems Center (TSC)

report [ 23], which has speed as one of its parameters, will he used in

estimating the increases in operating costs as a function of design

speed.

E . 2 Initial Network Screening

The FRA network model has approximately 8 ? 800 links, hut about 80%

of them carry together less than one-third the total traffic [39 ].

Unless some of these offer especially attractive diversion potential, it

is unlikely that these links will ever be considered for high-speed

upgrading. The initial network screening process is designed to eliminate

obviously poor candidates and to focus on obviously attractive candidates

for upgrading.

One criterion for potentially desirable high-speed improvement is

the possibility of diverting revenue-producing freight shipments from

35
truck to rail. Thus part of the initial screening process will be the

identification of markets in which a comparatively small change in rail

transit time would result in a substantial increase in rail market share.

There may not be any such markets; substantial travel-time reductions may

be necessary to make rail competitive with truck transport. Any lesser

travel time reductions will benefit current rail shippers but will not

generate additional rail revenue. An initial examination of what level of

travel-time change for various markets would shift, say, 5% of truck freight

to rail, would identify any possible markets in which diversion is possible

at all and would also indicate the level of improvement required.

Input to such an analysis would be a commodity-flow model, a modal

split model, and transport impedances between zone pairs for the various

modes. Several sources of commodity-flow data exist, including that of TCC

[ 4o] , the Faucett study [ Ul] and the National Network data base [ i+2 , U 3

]

The National Networks study also has a calibrated modal split model

which could be used for this analysis. All have commodity flow predictions

for the time frame of interest. The modal split model could thus be

applied to the zone-to-zone commodity flows to ascertain what level of

change in travel time would divert some specified portion of the truck

traffic to rail in selected markets. Links associated with those markets

would become prime candidates for further analysis in step 2 (see Section

(III.F.2) of the central optimization model.

Another criterion for screening links as high-speed candidates is

volume. It is unlikely that lower-volume links would support the investment

required to improve them in order to allow high-speed operation, unless

substantial diversion could be expected. Speed has greatest impact on

36
high-volume links, and consequently "such links are the most likely candidates

for upgrading. Under section 503 of the "Rail Act", the FRA [l8] has classified track into several categories. Those carrying the highest volume

(over 20 million gross tons per year) are called "A-Mainline" . Other

considerations used in designating track as A-Mainline include : serving a

significant market, and meeting national defense needs for an interconnected

network. It is likely that the A-Mainline track system will provide a

basis on which further screening can he done, since track outside this

category would he unlikely to support upgrading.

Still other procedures to he used in screening include identification

of corridors of service and high volume 0-D pairs. These may lead to

choices of likely candidate links, and will certainly be useful for

post-optimization analysis and for decisions on likely project implementations.

Geographical partitioning to facilitate detailed examination of portions

of network may also he useful in screening.

A final criterion for network link screening is an analysis of cost

benefit ratios and differences in order to identify links for which the

return on the investment is at an acceptable level. (This will be discussed

in greater detail in Section F.2.b«) This procedure rests in part on the

shaky basis of poor cost estimates, but may provide at least a ranking or

general classification of link candidates for use in the later optimization

model.

The primary purpose of all these screening operations is to narrow

down the candidates for high-speed upgrading, both to facilitate easier

handling of network and optimization models in the computer, and also to

reduce the number of links for which cost data are critical to a level

that may allow reference to additional sources for that data.

3T
Section F. Central Optimization Model

F . 1 Step 1

Step I involves modal choice and link assignment to obtain link volumes

by commodity class from commodity-f low prediction input. As seen from the

flowchart in Figure III-D.l (page 31), the commodity- flow prediction process

is not inside the loop of alternating steps 1 and 2. It is an initialization

which needs to be performed only once to provide input to the subsequent

analyses. The total flow values (by all modes) do not depend on the link

improvements, Since we do not consider the additional transportation demand

induced by rail improvement, but only changes in distribution among modes,

the flow predictions do not vary.

Path computations, which are part of the assignment model, have two

purposes. They provide travel- time input for modal split computations,

and also paths to be used in assignment. Capacity impacts may be included

for both time calculations and assignment paths by including a volumedependent factor in the travel time estimation, as is done in the FRA

assignment model.

The modal split model chosen for use in this project must be sensitive

to travel time, which means that the time elasticity of rail market share

must be sufficiently large to reflect the expected time differences.

Figures III-F.l & 2 and Tables III-F.l & 2 show the results of using the

modal split model contained in the National Network Study [ U2 1 to analyze market

share and revenue changes resulting from reductions in travel time. Although

the market share changes are nominal for even quite large time changes, percent revenue changes are fairly large for even small time changes.

38
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Table III.F.l

RAIL SHARES AFTER DOOR-TO-DOOR TRAVEL TIME REDUCTIONS:

MANUFACTURED GOODS*

Rail %

of Market

Before

Change 10 20 30

Rail Share

% Decrease

4o

After Change

In Rail Time

50 60 70 80 90

10 .103 .106 .110 .115 .120 .128 .137 .153 .182

20 .205 .211 .218 .226 .235 .248 .264 .288 .333

30 .307 .314 .323 .333 .345 .361 .381 .410 .461

40 .4o8 .416 .426 .437 .451 .467 .489 • 519 .571

50 .508 .517 .527 .538 .552 .568 .589 .618 .666

6o .608 .616 .625 .637 .649 .664 .683 .709 .750

TO .707 .713 ,722 .731 .742 .754 ,770 .791 .823

8o .805 .811 .816 .823 .831 .840 .852 .866 .889

90 .903 .906 .909 .913 .917 .922 .928 .936 .947

100 1.000 1,000 1.000 1.000 1 .000 1.000 1.000 1.000 1.000

*Based

share

on a time elasticity of E

for a given 0-D pair.

= -0,3(l-p

r

), where p

r

is the rail

40
Table III.F.2

PERCENT INCREASE IN RAIL REVENUE DUE TO

DECREASE IN RAIL DOOR-TO-DOOR TRAVEL TIME*

Rail %

of Market

Before

Change 10 20

% Decrease in

30 40

Rail Door50-To-Door

6o

Time

70 80 90

10 2.87 6.21 10.00 14.65 20.37 27.55 37.49 52.60 81.52

20 2.5*+ 5.49 8.79 12.82 17.70 23.76 32.00 44.18 66.44

30 2.22 4.77 7.61 11,04 15.15 20.19 26.92 36.63 53.68

40 1.90 4.06 6.45 9.31 12.71 16,82 22.22 29.84 42.73

50 1.57 3.36 5.32 7.64 10.38 13.64 17.86 23.69 33.24

60 1.26 2.67 4.21 6.02 8.13 10.62 13.79 18.09 24.94

70 0.94 1.99 3.12 4.45 5.98 7-76 10.00 12.98 17.61

8o 0.62 1.32 2.06 2,92 3.91 5 .04 6.45 8.30 11.09

90 0.31 0,65 1.02 1,44 1.92 2.46 3.12 3.98 5.25

*Based on a time elasticity of E = -0.3(l-P

r

), where is the rail

share for a given 0-D pair.

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Small percentage changes in revenues received can represent great increases

in the actual amount of revenue. The tables and figures refer to the

revenue received for one 0-D pair. This must be allocated to appropriate

links and the link revenues summed over all 0-D pairs between which rail

movements use a particular link. However, if percentages from other pairs

are similar, the percentage increase in revenue for a link should be similar

to that in Table III.F.2.

Once the rail share of an 0-D volume has been identified, it must be

associated with the links over which it travels. This is known to be difficult, since many rail shipments do not follow the shortest distance path

or shortest time path, but rather follow a path determined by train schedules,

historical precedent and shipper instructions. There seems to be no one

consistent choice function with which path decisions are made. FRA has an

operating assignment process which can use a variety of parameters and

combinations of them to assign link loadings. As a start the FRA. travel

time criterion with a correction factor for capacity-caused delays will

be used. This involves iterating the assignment model to make the delay

incurred consistent with link volume. It is desirable that the link assignment criterion used be sensitive to changes in travel times. Otherwise

loading will not be affected by high-speed improvements, an unrealistic

situation which would lead to under-representing revenues accrued from the

increase in speed.

It will be necessary to devise a procedure for associating commodity

flow data, which may be given at a fairly gross level (l 80 or 500 zones for

example), to appropriate network load points. Once an initial loading has

been made, it may be possible to assign volumes only to a subnetwork of

43
main-line links, since changes' will occur only on a portion of the network

and access to that network will not be affected. This would somewhat

reduce the size of subsequent assignment steps, but may not be easily

accomplished. The most feasible process may be a complete network

assignment at each iteration of step 1, even though this will be expensive

and require a large amount of computer- time.

F.2 Step 2

Step 2 involves choosing which links to upgrade to high-speed capability.

As noted above, different applications of the central optimization model

will use several different models to reflect different choice criteria,

the three major of which are:

1. Minimize the cost of upgrading to provide a specified level of

high-speed service,

2. Maximize service provided subject to a budget constraint,

3. Maximize revenues from diversion.

Using the first of these criteria requires specifying the markets for

which provision of high-speed service is desired. The identification of these

potential markets (or corridors) for high-speed service is part of the

analysis in the initial screening and forms a basis for the definition of

several scenarios. For the second criterion, scenarios are specified by

the various budget levels to be considered. One method of defining them

is to postulate several system sizes, measured by the number of miles to be

upgraded, and to multiply those values by an average per-mile upgrading cost.

Maximization of diversion involves the choice of markets which offer a

potential for rail to capture a share of truck shipments. Choosing

hk
links to improve travel times for those markets is similar to the process

used for criterion 1, except it is not necessary that each market be

served by a path entirely consisting of high-speed links. Choosing

appropriate "upgrading corridors", with impacts on markets of potential

diversion, is an important step in the initial screening and essentially

a manual operation.

The formulations which correspond to criteria 1 and 2 both result in

mathematical programming models whose size is determined by how well the

screening process is able to reduce the set of links to be considered as

high-speed candidates. This is accomplished either (i) by identifying

markets to be served by high-speed rail or (ii) by identifying specific

links or link sets for which high-speed upgrading will produce large benefit

or (iii) by identifying specific links or link sets in which high-speed

upgrading will have little effect. The two model approaches will be discussed below.
F.2.a Minimizing Upgrading Costs for Specified Level of Upgrading

The underlying concept of this model is that of achieving a given

quality of service at minimum cost. This quality of service is to be

attained between each of a set of 0-D pairs stipulated by the user of

the model for a given "run", and different sets can be used in different

scenarios.

The stark version of the model, to be described now, operates

with simple dichotomies. A link either is, or is not, of high-speed

quality. An 0-D pair either enjoys high-speed quality of service,

evidenced by its being connected by at least one path of high-speed links,

or else it does not. There is an "adequate capacity" assumption; this

implies that high speed traffic does not interfere with either traffic

on unimproved links or high speed traffic of other 0-D pairs. This

should be checked, in the context of the high-speed network produced by

the model. _ _

To start the formalities, let N and L denote the node-set and

link-set of the rail network. With each link A e L is associated a binary

decision variable

x(A) =

1 if \ is brought to or already is high-speed status

0 otherwise.

and a positive problem datum (assumed given)

c(A) = cost of bringing A from present condition to high-speed

status

Thus the objective is to

minimize E{c(A) x(A): A £ L} .

In order to describe the constraints, other than

( 2 . 1 )

h6

x(A) e {0, 1} ( 2 . 2 )
let (0^, D^) denote the k-th of the K stipulated 0-D pairs. In

words, the model constraint associated with the k-th pair is that this

pair be joined by at least one path consisting entirely of links that have

been brought to high-speed status.

There are at least two different ways to formulate this condition

algebraically. For the first of these, let

= the collection of paths p joining (0k

, D ) , (2.3)

and let

f~ 1 if link X lies on path p , <$(X,p) = <

! 0 otherwise.

Introduce variables

y(p) £ {0, 1}

constrained by

| P | y (p) _< E{6(X,p) x(X): X e L} (2.4)

where |p| = E{6(X,p): X e L) denotes the number of links in p . Since

E{6(X,p) x(p )

X e L} is the number of links of p which are of highspeed quality, it follows that y(p) = 0 unless p has been made a highspeed path. Finally, form the constraints

£{y(p): P £ P

k

> > 1 for 1 < k < K . (2.5)

For the second formulation, consider a fictitious commodity

called high-speed travel" which is to be moved from 0, to D, A link

k k

X has capacity" 1 or 0, to accomodate high-speed travel, according as it

has been brought to high-speed status or not. The desired high-speed path

from 0^ to exists if and only if at least one unit of can flow
from 0 to D without violating these capacity conditions. Introduce

K. K. flow variables

f^(A) = flow of on link A , f^(A) C {0,1}

Ignoring for the moment the fact that links are undirected, we formulate

the conservation conditions

Z{fk

(i,j): (i,

j

) e L> - E{fk

(j,i): (j,i) e L} = 0 (2.6)

for all ieN - {0 , D } and all k , K. K.

the conditions assuring connectivity

£{fk

(0k’ J): (0k> j) e L} - Z{ f

k a. 0k

); (j , 0

k

) eL} - 1. (2.7)

£{£k

(j, D

k

): (j, D

k

) e L} - E{fk

(Dk

, j) : (D

fe> j)eL} = 1, (2.8)

and the capacity conditions

0 < f (A) < x(A) . (2.9)

Either formulation gives rise to a large integer linear problem that

one would not (sanely) attempt to set-up and solve explicitly by generalpurpose methods. The question is whether adequate "tailored" special-purpose

algorithms, exact or approximate, can be found.

We now consider some complications. They fall under two main

headings: noncircuity, and multichotomy

The point of the first heading is that the stark model may leave

some 0-D pairs joined by one or more high-speed paths, but all of them

very long or circuitous. This would not truly realize the level of service

concept intended by the model.

bd
There are several approaches that might be tried to guard against

this danger. In connection with (2.3), P might be limited to relatively

non circuitous paths. In (2.6 - 2.9), when dealing with the k-th 0-D pair

one could restrict attention to links located and oriented so as to be

reasonable possibilities for fairly direct movement from 0 to D . K. K. Identifying such links (and orientations) could be carried out using the

node-labels from a preliminary calculation which, for each k, used a labelsetting algorithm to build a shortest-path spanning tree rooted at 0^ .

(Some duplication can be avoided if several 0-D pairs have the same origin,

as seems likely.) For (2.6 - 2.9) it would be necessary to replace each

(undirected) link by a corresponding pair of oppositely directed arcs, but

for most links and each k, one of the two orientations will in fact be

unreasonable for "0, to D, " movement and can therefore be omitted.

k k

Under the heading of ,, multichotomy,, , we observe that our notion of

a link as high-speed or not, and the corresponding definition of highspeed connection of an 0-D pair, may well be unacceptably oversimplified.

The simple cost coefficients c(A) might need to be replaced by functions

c(A,t) = cost of upgrading link A to reduce its traversal

time to t ,

and the simple binary variables x(A) by variables

t(A) = traversal time for link A

Then (2.1) would be replaced by

minimize E{c(A,t(A)): A e L } . (3.1)

The "level of service" specification would include a stipulated value for

= maximum required traverse time, 0^ to D^ .

^9 .
Then P in (2.3) would be restricted to consist of paths p , from 0 to

K K

D. , for which

k

E{6(X,p)t(X) : X e L} < , (3.2)

so that the sets P^ depend on the t(X)'s. A full algebraic formulation

corresponding to (2.3 - 2.5) is not attempted here. For the formulation

(2.6 - 2.9), one might try replacing the "l's" of (2.7) and (2.8) with

other values related to the T^'s, and (2.9) by

0 < f (X) < t(X) .

Possibly the variables t(X) can be usefully discretized to 3-5 levels; then

one can replace each link by a bundle of parallel links, each with its

own (c,t) values.

50
F.2.b Maximizing Service for a Given Budget

The initial, admittedly simplistic, model is described below using

the following notation:

c 0 - cost for improving link £,

Xj v 0 - volume of demand over link

Xj t 0 - improvement in travel time on link Z, if it is improved,

B - budget level,

x. - j 1 if link Z is improved,

/ 0 if link Z is not improved.

Then we may state the optimization to be performed as:

Model 1

••— ““lYt . subject to: E ex. 5. B

Z - *

x^ e {0,1} for all Z ,

Examining these conditions shows that Model 1 turns out to be a very

special type of linear programming problem, namely, a "knapsack" problem.

Such problems have been studied extensively [44] and satisfactory algorithms

for their solution are known.

This formulation considers improvement as only a dichotomous variable;

either a link is improved or it is not. There are no different levels of

improvement or alternative improvements considered for a link. A later

model version will consider such factors.

The measure of system benefit, namely, improved service to "current"

users, is the product of the link volume, and the time saved; thus tonhours saved multiplied by speed improvement. Time saved alone would not

be sufficient to measure since we need to have time savings weighted by

volume, and volume benefit alone is not satisfactory since the time savings

depends on the link.

51
This formulation does not have any contiguity requirement, as does

that proposed in the previous section. Therefore it is possible that the

optimization might choose not to improve a link that lies between two

improved links. It is not clear that this is necessarily undesirable A_ since it could occur in one of several ways. The middle link may have a

significantly lower volume than its neighbors, in which case there is

little through-traffic to benefit from a high-speed line. The middle link

may not be amenable to much improvement, either because it is already

pretty fast or because there are special considerations (grade or other

terrain, environmental constraints, bridge structure etc.) which limit

possible improvement levels. A third way in which a link between two

improved links might not be chosen to be improved is if the cost of

improving the middle link was very high compared to that of the other

two. For any of these reasons, contiguity of improved trackage may not

result naturally from the optimization process. The optimization described

here will, however, tell which links are really most cost-effective for

improvement. Adding additional constraints on contiguity decreases the

return on investment, so that the solution to the above process is in

some sense an upper bound on the possible return from a given budget level.

As an initial approximation, in screening candidate links for further

study, one might examine the following optimization problem:

Model 2

max E x.

%

subject to:

(at^v^-c^) x

£

>_ 0, for all Z

x^ e {0,1}, for all Z.

52
where we can interpret a as a constant which gives +he value of a tonhour of improvement to the system. The solution of this model is

computationally trivial. This in effect picks all links which are

profitable, since it seeks to identify those links, which have the

property that

Model 3

A formulation which allows various levels of improvement on each

link is a direct generalization of Model 1. Three new variables are:

t^ - time saved by improving link Jl to level i

c^ - cost of improving link l to level i.

x^ -fo if link l is not improved to level i

\l if link i is improved to level i , or is already at or above level i (c„.=0).

The optimization problem then becomes:

max l l t„. v„ x„. 11 ' 11

subject to

lie

l i

„ . x„.

ill ill < B

x

£i

e {0,1}

Another formulation considers possible multi-link improvement "projects ,"

with likely candidates identified from the outputs of the preceding models.

This allows inclusion of contiguity requirements. If a project is implemented, all links affected by that project are improved. The notation to be

used in this model formulation includes:

53
yp- f 1 i

1° 1if project p is implemented

if project p is nut implemented

L(p) - the set of links to be improved by project p

Model ^

max * t

i x

i

X

subject to:

l c

a xa- B

X

SL 1 £{y

p

: A e L(p )

}

EJteL(p) x

*

> |L(p)| y

p

X£

d

y

p

e {0,1}

The first constraint is the budget constraint. The second constraint

insures that a link is improved only as a part of some project. The third

constraint makes sure that if a project is implemented, all links on that

project are improved.

Model 3 suffers from the difficulty of having a large number of

variables, namely, the product of the -number of links and the number of improvement levels of each link. As noted earlier, this may be cut somewhat

by initial screening of links to limit the choice to those with potential

for cost-effective improvement. Model 4 has many fewer variables, but at

the expense of requiring a designation of appropriate projects. These

could be single-link improvements, in which case the model reduces to

Model 1. Projects could also include contiguity requirements, since one

project may involve improving several links which are only improved if

all are improved.
Improvements could include yard improvements if yard imped rnces are

represented directly on network links. Projects encompassing yard upgrading

could thus be included in Model k. The initial screening should address the

question of whether it is possible by any level of improvement on rail links

alone to have a measurable impact on rail revenues. Various yard improvement

levels should also be considered, to identify those with greatest potential

for inclusion in the subsequent analysis in the central model.

The model described in Section P.2. a seeks to minimize cost

subject to a constraint that certain areas (Q-D corridors) must have highspeed service; the models described in this section seek to maximize

service for a given investment budget. Both are oriented toward the

current rail traffic. Since Step 1 includes a time-sensitive modal split

modal generating the "current” traffic for the next pass through Step

2, there will be some diversion from other modes within the alternating

two-step iterative approach. However, the model of the previous section

can be applied specifically to those 0-D pairs which offer greatest potential for diversion.

An alternate approach in choosing links to improve might be to

choose those links which give the greatest diversion from other modes

(primarily truck). This approach could be used in initial screening

to determine those links and markets for which improvement of a link or

set of links would divert some threshold level of traffic from competing

modes. Such an analysis would ignore budget constraints, and simply

investigate the possibility that substantial traffic from other modes can be diverted by any high-speed improvements of the type being considered.

If there is potentially divertable traffic, then the approach would

identify which projects would lead to diversion, making them desirable

candidates for further analysis. Diversion of traffic could be included

55
explicitly

additional

capture of

speed rail

in the objective functions of Models 1, 3 and either as an

summand or as an alternative to time-savings , provided that

a larger modal share is accepted as a major objective of highimprovement .

56
F.3 Link Improvement Rankings

The output of the centeal optimization model consists of a set of

links for each of the model runs, each run representing a different scenario.

It will he necessary, in responding to the requirements of Section 901-(6)

of the Rail Act, to provide a composite ranking of the links according

to their potentials for high-speed improvement. One method of achieving

such a link ranking is to rate the scenarios according to their likelihood

and to give that rating to each of the links output from the run of that

scenario; other links receive a rating of zero for the scenario. The final

rating for each link is the sum of its ratings for each individual run, and

links are then ranked on their ratings. This process would result in many

links receiving a rating of zero, since it is expected that many links will

never he chosen for high-speed improvement.

In addition to ranking links, it is also necessary to investigate

reasonable high-speed improvement projects. Since a composite ranking

scheme is likely to intersperse links from different geographical areas

,

some method of aggregating the individual link outputs into high-speed

corridors or other projects of improved link aggregates must he devised.

A major part of such an analysis is the identification of contiguous or

nearly contiguous sets of high-ranking links. One possible method to obtain

these is to start with the highest-rank link. Include it in the first high

speed aggregate subnetwork, and then attempt to extend that network by

appending any contiguous link with an acceptably high ranking. Continue

in this way until no more links can be added. Perform a similar network

"growing" process starting with the highest ranking link not in the first

subnetwork. Keep up this process until all links of acceptably high rating

have been included in some subnetwork.

57
After link aggregates have been identified, it is necessary to establish

some scheme for rating them. This might be accomplished using some function of

the individual link ratings as the composite rating for the aggregate.

Another approach is to rank projects on the basis of potential revenues or

some other benefit measure. Some consideration must be given to the staging

of the projects; whether to start implementing several at once perhaps (proceeding on each from the most profitable link out to less profitable ones),

or alternatively, to proceed one project at a time implementing them in order

of total return.

We emphasize that the procedures described in this document operate at

a fairly gross level of description of individual link characteristics.

When major projects have been identified by this process, it will still be

necessary to investigate further the practicality of the link choices and of

the appropriate detailed improvements to achieve high-speed capabilities

Many factors that were specifically excluded in the more macro-level of analysis described here must be considered at that point. Physical link

characteristics, such as grade, curvature, and bridge structures, will

affect the required improvements and the costs of those improvements.

Operational factors such as siding lengths, train sizes, train schedules,

and passenger operations must also be considered. Managerial decisions

and approaches can also have a great effect on the effectiveness of highspeed improvements. ' Political factors will undoubtedly enter the debate

and should at least be anticipated. Some reconciliation of the high-speed

links proposed in this process with the improvements or decisions made

under other sections of the Rail Act must be performed to ensure consistency of DOT recommendations to Congress. In particular, it is likely

58
that many of the factors that will influence the choice of links to improve to high-speed status will also characterize candidates for electrification. Coordination of the outputs from these two sections with studies

implementing other parts of the Rail Act is critical. Coordination with

others working on the Act will also be required to assure the latest

values of input parameters such as the current track condition data and

the yard operation parameters.

Project output will also identify improvements in rail properties

advantageous to the expeditious movement of passenger traffic. It is

impractical to combine the optimization of the two types of movement, passengers and freight, because of the difficulties of constructing composite

measures of benefits and costs which include both types of traffic. Passenger link improvements must also be coordinated with other recommendations

of DOT.

In addition to link and project rankings, output from this work will

include tabulation of costs and benefits associated with the various improvements. The benefits (described earlier) associated with environmental,

energy, safety, and passenger traffic will be quantified and identified

with specific projects where possible. Costs will also be displayed on

a link-by-link and project-by-project level, although it is again noted

that these are not detailed costs based on careful engineering analyses

of link characteristics.

59
Section G: Rail Passenger Service

From the early days of railroading, passenger and freight trains

have operated jointly over a common track network. One recent study [ U 5

]

suggests that this joint use can safely continue as passenger train speeds

increase, provided suitable control systems are installed which maintain

time and space separations and provide rapid notice to moving trains of

pfctentially hazardous situations. Although the future of rail transportation depends primarily on the efficient movement of freight, the expectation that passenger and freight service can safely share common track

networks suggests that the most attractive potential passenger-routes should

be explored for possible speed improvement. A means for identifying routes

is presented in this section.

In late 1971 s as part of the National Network Simulation Project,

the National Bureau of Standards identified eleven travel corridors

where estimated patronage indicated that passenger service could be

provided at reasonable cost. In alphabetical order, these corridors

were:

Boston—Washington

Buffalo—Syracuse—Albany—New York City

Chicago—Cincinnati

Chicago—Cleveland

Chicago—Minneapolis

Chicago—St. Louis

Cincinnati—Cleveland

Cincinnati—Detroit

Los Angeles—San Diego

60
Philadelphi a—Pittsburgh

Portland—Seattle

In these corridors, passenger service could be provided at a cost per

passenger mile of ten cents. When cost per passenger mile was plotted for

each corridor in increasing order, there was a sharp rise in cost per passenger mile after ten cents per mile. This breakpoint phenomenon persists when the curve is shifted upward by replacing 1971 costs with

1977 costs.

The freight-oriented analysis described earlier should be supplemented

by examining the effects on passenger-train patronage of increasing speeds

stepwise in these corridors from the 1972 speeds of 110 miles per hour.

As passenger-train speeds are increased, corridor travelers will increasingly divert to the rail mode. Priorities for improving rail passenger

train trackage will be established by relating the costs of the improvements (including terminal improvements) to corridor benefits achieved on

a regional basis. Such benefits would include reduced fossil-fuel

consumption, reduced air and noise pollution, lessened congestion for

highway and short-range air traffic, and reduced need for additional

highway lanes and air-terminal capacity.

The basic approach to estimating the effects of increased rail speeds

on passenger trains is identical to that used in the freight analysis.

The essential differences lie in the demand projection and modal choice

models. Again the concern is primarily with the increases in rail volumes

due to the diversion from other modes, and not with the additional demand

for travel that may be induced by the overall improvement in the transportation system.

6l
There are a number of intercity modal choice models available

Seven such models developed for the Northeast Corridor Transportation

Project have been tested on non-corridor data [46]. Model CN27 was

determined to be the best of those models on Northeast Corridor data;

and for non corridor data the authors assert "Model CN27 provides the

least amount of variability between observed and estimated rail volumes...".

An alternative to CN27 is CN22, which does not require that passenger

traffic be divided by purpose. Still another possibility is the modal choice

model developed for the National Network Simulation project* This model was

calculated on a national data base and suggests somewhat lower travel

time-elasticities than do the Northeast Corridor models. The rail travel

time exponents in the Northeast Corridor models varied between -1.5

and -3.38, while the national-level models have exponents greater than

-1.0. Tables C-l and G-2 illustrate the impacts of travel-time

reduction for a time exponent of -0.75-

The impacts of passenger train travel time reductions (speed increases)

will be explored in each of the major corridors and will be tabulated

subordered to freight listings.

62
Table G--1

RAIL SHARES AFTER DOOR-TO-DOOR TRAVEL TIME

REDUCTIONS : PASSENGER SERVICE*

Rail

share

before

travel time

change % 10 20 30

Travel Time Change

(percent)

1+0 50 60 70 80 90

10 .107 .116 .127 .11+0 .157 .181 .215 .271 .385

20 .213 .228 .21+6 .269 .296 .332 .381 .455 .584

30 .317 .336 .359 .386 .419 .460 .514 .589 .707

1+0 .1+19 .1+1+1 .1+67 .1+94 .529 .570 .622 .690 .789

50 .520 .51+2 .567 .595 ,627 .665 .712 .770 .849

60 .619 .639 .662 .688 .716 .749 .787 .834 .894

70 .716 .731+ .753 .774 .797 .823 .852 .886 .929

80 .812 .825 .839 .854 .871 .888 .908 .930 .957

90 .907 .914 .922 .930 .938 .947 .957 .968 .981

*Based upon time exponent of -0,75.

63
Table G-2

PERCENT INCREASE IN RAIL PASSENGER REVENUE DUE TO

DECREASE IN RAIL DOOR-TO-DOOR TRAVEL TIME*

Rail

fraction

of market

before

travel time

change $ 10 20 30

Travel Time Change

(percent

)

40 50 60 70 80 90

10 7.34 16.10 26.78 4o.i4 57.44 80.94 115.14 170.88 284.55

20 6.47 14.06 23.12 34.16 48.00 66.01 90.73 127.66 192.17

30 5.62 12.09 19.66 28.66 39.62 53.36 71.31 96.33 135.58

40 4.78 10.19 16.39 23.60 32.14 42.49 55.47 72.58 97.36

50 3.95 8.35 13.30 18.93 25.42 33.07 42.31 53.96 69.80

60 3.13 6.57 10.36 14.59 19.35 24.81 31.21 38.96 49.00

70 2.33 4.85 7.57 10.56 13.85 17.52 21.71 26.63 32.74

80 1.54 3.18 4.93 6.80 8.82 11.04 13.50 16.30 19.68

90 0.77 1.57 2.40 3.29 4.23 5.23 6.32 7.54 8.96

*Based upon time exponent of -0.75.

\

64
IV. A SIMPLIFIED APPROACH

We outline here an alternate approach to evaluation of high-speed

projects. It is simpler in form and more straightforward to implement,

amd, although it does not capture some subtleties of the prior approach,

it can he to compare user-supplied potential projects. The approach

emphasizes financial viability of the projects. The steps are:

1. Use the Transportation System Center (TSC) demand projections, by

by commodity class, which are available on tape for the forecast year. These

are from Bureau of Economic Analysis (BEA) area to BEA area and must be

allocated to the National Network System (NNS) zone system using the NBS

program [ 43 ].

2. Estimate rail shares using the NNS modal split model.

3. Assign 0-D flows to rail network using FRA network model to

produce link loadings.

4. Using tariff estimating equations , convert link loadings to link

0 , revenues: R^ . The superscript indicates the current scenario (0

representing no changes in the system) , and the subscript indicates the

link.

5. Specify an upgrading scenario S and introduce resulting link

speeds. Changes in yard times may also be introduced here.

6. Use the FRA network model to estimate new zone-to-zone rail times.

g g o

7. Calculate AT. . = T. . - T.

s the change in rail shipment time

1J 1J lj

between regions i and j

.

8. Estimate rail shares based on new rail shipment times.

9 • Assign resulting volumes to rail network to obtain link loadings.

g 10. Convert the link loadings to link revenues: R . X/

65
s S 0

11. Calculate AR = R - R , the change in revenue due to scenario S,

A/ X/ X/ for each link.

S S

12. Estimate link benefits based on AR , AT

X< 1 J .

13. Use the TSC cost model to estimate link operating and maintenance

costs: . Q

14. Combine with annualized capital improvement costs 1^ to obtain

Xj total costs :

15.

Categorize the link improvements in scenario S according to

chosen benefit -cost criterion.

Wide latitude is left in the choice of scenarios in step 5. This

allows great flexibility, and is the major difference between this approach

and that of section III. No optimization is employed here, providing

assurance that this approach can be carried out quickly. However, optimal

choice of scenarios is not guaranteed. (it may be that a link improvement

» with low cost-benefit measure is necessary to obtain high benefits from

some other improvement .

)

66
V. WORK PLAN

To implement the concepts developed in the preceding chapters, the

following tasks should be performed to meet the requirements of Section

901(6) of the Rail Act. These tasks have been detailed in the previous

text. We have been careful not to obscure the fact that one important

decision—selecting the "least unsatisfactory" method for estimating

link upgrading costs—remains to be settled during the study itself.

1. Coordinate with groups working on other relevant rail

studies (e.g. electrification, yards and terminals, deferred

maintenance)

2. Obtain and assimilate specific models to be used.

3. Examine and assimilate data sources.

4. Select (or construct if necessary) submodels required for

treating:

a. yard impedances

b. terminal impedances

c * tariff/revenue relations

d. commodity-flow prediction (all-modes total)

e. passenger-flow prediction (all-modes total)

5. Estimate costs:

a. select model (probably TSC's) for estimating maintenance and

operating costs

b. examine alternative procedures for estimating capital costs

of upgrading:

i. FRA link model (speeds)

ii. extension of Dyer approach (deferred maintenance)

iii. Texas A&M data base (timetables)
iv. possible estimation from freight volume and financial

status of owning railroad.

6. Set up procedure to quantify benefits within the model context:

a. revenues

b. opportunity costs of time saved

c. societal benefits

7. Perform initial screening based on:

a. markets with potential division

b. revenue potential

c * costs and benefits

8. Exercise evaluation approach of Section V!

a. choose scenarios (with FRA.)

b. exercise procedures

c. interim report

9. Select parameter settings (budget levels, yard impedances, etc.)

and adapt algorithms for optimization.

10. Apply central optimization model:

a. initialization (commodity-flow projections) from Task 8

b. step 1

i. modal split

ii. link assignment

c. step 2

i. choose links for upgrading

ii. check convergence; iterate if necessary

11. Develop and apply procedures for analyzing and aggregating model outputs

a. link ranking

b. project selection and staging

c. tabulate costs and benefits

68
12. Prepare final report

The timing for accomplishing these tasks is displayed in the acompanying

work schedule. We list below our anticipations for the various efforts (in

personnel-days) to be expended on the 12 tasks.

Tasks Personnel-days

1 . Coordination 5

2. Assimilation of models 15

3. Assimilation of data 10

4. Submodel selection 15

5. Cost estimation 30

6. Benefit quantification 5

7. Initial screening 50

8. Exercise of simplified approach 40

9. Parameter setting 10

10. Model application 60

11. Output analysis 40

12. Documentation 40

Total .. 320

The preceding estimates and work schedule are based on the assumption

that only 12 weeks will be ayailable for completion of the study. This

time limitation will impose severe pressures on the analysis. We urge

that, if possible, a larger period be sought so as to permit a more complete

and definitive treatment of this complex problem area, to allow for possible delays in receipt of ostensibly available data, and to allow for

problems with ostensibly "ready-to-go" software. The availability of

interim ’’outputs" from Task 8 may make such an extension more palatable.

69
Figure V-l Work Schedule
To provide a "ballpark" idea of the cost for executing this study

plan, we have estimated its cost if performed by our own group:

Category

personnel* (230 days)

$(in thousands)

62.1

consultants* (90 days) 27

travel 3

computer** 13

total 105.1

*Personnel costs for in-house staff are calculated

at an average of $270 per day, reflecting availability

of summer workers from universities if study is performed

during June-August 1977. Consultant costs are estimated

at $300 per day.

**The computer-cost estimate does not include application

of the FRA assignment model, which is assumed to be done

under FRA auspices.

71
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Interrailroad Freight Competition, Unpublished Draft.

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,

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Revitalization and Regulatory Reform Act of 1976, 45 USC 801.

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m

78
Railroad Freight Car Requirements for Transporting Energy, 1974-1985

,

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May l4, 15 and l6, 1975, Part 1, Serial No. 94-31, (US GPO), Washington

(1975).

Railroads - 1975 Hearings Before the Committee on Commerce United States

Senate Ninety-Fourth Congress First Session on S.793 et.al.. Miscellaneous

Legislation Dealing with the Branch Line and Rural Commuter Lines July l6,

17, and 18, 1975, Part 2 Serial No. 94-31, (US GPO), Washington (1975).

Railroads - 1975 Hearing Before the Surface Transportation Subcommittee

of the Committee on Commerce United States Senate Ninety-Fourth Congress

First Session on Legislation Relating to Rail Passenger Service July 25,

1975, Part 3, Serial No. 94-31, (US GPO), Washington ( 1975 )

A Review of Alternative Approaches to Federal Funding of Rail Rehabilitation,

US Congress Office of Technology Assessment, OTA C-25 (August 1975).

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Assessment, 0TA-T-14 (December 1975).

Reymond, R. D. , Troup, K. F. , System Requirements and Benefits of a Terminal Information System for the Kansas City Railroads, US Dept,

of Transportation, FRA, Report No. FRA-OPPD-76-6 (August 1976).

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Maryland, College Park (1975).

79
Samuelson, R. D. , Roberts, P. 0. , A Commodity Attribute Data File for

Use in Freight Transportation Studies, Center for Transportation Studies

MIT, CTS Report 75-20, Cambridge, MA (Nov. 1975).

Schuessler, R. W. and Cardellichio, P. A., NTP Commodity Flow Projections

-Data and Methods Description, US Dept, of Transportation, Transportation

Systems Center, Report No. SS-212-U1-33, Cambridge, MA (no date).

Shapp, M. J. , A United States Rail Trust Fund, Bureau of Planning Statistics, Pennsylvania Department of Transportation.

A Shattered Railroad Becomes A Land Company, Business Week (January 2k,

1977).

Siddiqee, M. W., Investigation of Sorting and Train Formation Schemes for

a Railroad Hump Yard, Proc . , Fifth International Symposium on the Theory

of Traffic Flow and Transportation American Elsevier (1972).

Siddiqee, M. W. , D'Esopo, D. A. Tuan, P. L. , Blocking and Train Operations

Planning, Stanford Research Institute, SRI Project 3759 (June 1975).

Simulating a Railroad Via Cybernet, Operations Research Newsletter, Control

Data Corporation, Issue No. 3 6 (October 197).

Smith, A. B. , Piggyback Potential for Conrail Progressive Railroading

(November 1975).

Southern Pacific’s West Colton Yard, Railway System Controls (October

1973).

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Studies on the Demarid for Freight Transportation, Vol. I, Mathematica,

Princeton (August 1967 )

.

Studies on the Demand for Freight Transportation, Vol. II, Mathematica,

Princeton (May 1969 ).

Studies on the Demand for Freight Transportation, Vol. Ill, Mathematica,

Princeton (May 1969 ).

Summary and Analysis of Accidents on Railroads in the US FRA office of

Safety (Annual Accident Bulletin) #1^3, US Department of Transportation,

Washington (197*0.

80
Summary and Analysis of Rail-Highway Grade Crossing Accidents in the

US FRA Office of Safety, US Department of Transportation, Washington

(1974).

Swanson, C. G. , Nene, V. D., Martin, R., Lenard, M. , The Energy and

Environmental Impact of Railroad Electrification, Mitre Corporation

(March 1977).

Swanson, C. G. , Identification of Candidate Segments for Railroad Electrification, MITRE Working Paper No. 12122, MITRE Corp., METREK Division.

Track Inspection with State-Owned Recorder Car, Railway Track and Structues (April 1975).

Theory and Implementation of Cost and Benefit Analysis of Transportation

Systems: The NECTP, Office of High Speed Ground Transportation Report

No. NECTP-225, US Department of Transportation (December 1969)

Transportation Related Publications, Massachusetts Institute of Technology, Center for Transportation (Fall, 1976).

Troup, K. F. , Railroad Classification Technology: An Introductory Analysis

of Function and Operation, for U.S. Department of Transportation (May 1975).

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Ontario (October 1975).

Welty, G., Terminal Efficiency: Technology and Technique, Railway Age

(August 27, 1973).

West Colton. . .Southern Pacific’s New Pivotal Yard, Progressive Railroading

(Sept. -Oct. 1973).

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Car Allocation, IBM Systems Journal, Volume 8, No. 2 (1969).

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81
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[1] Winch, David M. , The Economics of Highway Planning, U. of Toronto

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[2] American Association of State Highway Officials, Road User Benefit

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[3] Moring, Herbert, Transportation Economics, Ballinger, Cambridge,

Mass. , 1976.

[4] Pennsylvania Statewide Rail Plan, Phase II, Submission to the

FRA, Commonwealth of Pennsylvania, Department of Transportation

(December 9> 1975)*

[5] Michigan Railroad Plan, Phase II, Michigan Department of State

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[7] McLynn, J. M. , An Overview of Fully Competitive Modal Choice Models,

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[ 8 ] McLynn, J. M. , Watkins, R. H. , Multimode Assignment Model, Approaches

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[ 9 ] McLynn, J. M. , Woronka, T., Passenger Demand and Modal Split Models,

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[11] Railroad Classification Yards: Installations 1976-192^, Union

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[12] Welty, G. , Terminal Efficiency: Technology and Technique, Railway

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82
[14] West Colton. . .Southern Pacific's New Pivotal Yard, Progressive

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83
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,

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(February 1975)

[33] Community Impacts from the Abandonment of Rail Service, USRA (197*0*

[ 3

)4 ] Transportation Noise and Noise from Equipment Powered by Internal

Combustion Engines, Environmental Protection Agency (1971)*

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the U.S., FRA Office of Safety, U.S. Dept, of Transportation (197*0*

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Office of Safety, U.S Dept, of Transportation (1976).

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Transportation (197*0*

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and Northeast Region, USRA, U.S. Dept, of Transportation, (February

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[*+2] Jackson, R. H. F., A National Network Data Base System, NBS Report

NBSIR 7 5- 911 s U.S. Dept, of Commerce (September 1975).

84
[ i+3 ] Lozier, D. , Stiehler, J. R. , Machine-Readable Mapping from

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(September 1975)-

[UU] Hu, T. C. , Integer Programming and Network Flows, Addison-Wesley

Publishing Co. (Reading Mass., 1969), pp. 311-316.

[45] Northeast Corridor High Speed Rail Passenger Service Improvement

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[k6] Bennett, J. C. , Ellis, R. H. , Prokopy, J. C. , Cheslow, M. D. , A

Comparative Evaluation of Intercity Modal Split Models, pp. 83-86,

Transportation Research Record No. 526, Washington, DC, (l97^)»
NBS-114A irev. a-78)

U.S. DEPT. OF COMM.

BIBLIOGRAPHIC DATA

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1. PUBLICATION OR REPORT NO.

NBSIR 79-1724

on No. 3. Accession No.

4. TITLE AND SUBTITLE

Selecting Rail Properties for Improvement: An

Analysis Plan

5. Publication Date October 1980

*. Performing Organization Code

tv--

7. AUTHOR(S)

J. M. McLynn, R. H. Watkins, R. E. Schofer, J. F. Gilsinn,

W. G. Hall f and C. R. Johnson

8. Performing Organ. Report No.

9. PERFORMING ORGANIZATION NAME AND ADDRESS

NATIONAL BUREAU OF STANDARDS

DEPARTMENT OF COMMERCE

WASHINGTON, DC 20234

/Work Unit No.

11. Contract/Grant No.

12. SPONSORING ORGANIZATION NAME AND COMPLETE ADDRESS (Street, city. State, ZIP) 13. Type of Report & Period Covered

15. SUPPLEMENTARY NOTES

[~~1 Document describes a computer program; SF-185, FIPS Software Summary, is attached.

16. ABSTRACT (A 200-word or leas factual summary of moat significant information. If document includes a significant bibliography or

literature survey, mention it here.)

Section 901- (6) of the Railroad Revitalization and Regulatory Reform

Act of 1976 (PL 94-210) calls for a listing and prioritization of rail

properties to be improved to permit high-speed operations. This

report identifies key factors entering the choice of links for such

upgrading, and formulates an analytical methodology (and implementation

plan) to support the decision process.

17. KEY WORDS (six to twelve entries; alphabetical order; capitalize only the first letter of the first key word unless a proper name;

separated by semicolons)

Benefit-cost analysis; combinatorial optimization; high-speed rail,

mathematical models, modal split, network analysis

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