U.S. Department of 

Transportation 

Office of Research and 

Development 

Washington, DC 20590 

 Next Generation High-Speed 

 Rail Program 

DOT/FRA/RDV-00/05 Final Report 

DOT-VNTSC-FRA-00-03 August 2000 

Assessment of Risks for High-Speed 

Rail Grade Crossings on the Empire 

Corridor 

Federal Railroad 

Administration 

This document is available to the public through the National 

Technical Information Service, Springfield, VA 22161. 

This document is also available on the FRA web site 

at www.fra.dot.gov.

Research and Special Programs Administration 

John A. Volpe National Transportation Systems Center 

Cambridge, MA 02142-1093 
 

NOTICE 

This document is disseminated under the sponsorship of the 

Department of Transportation in the interest of information exchange. 

The United States Government assumes no liability for its contents or 

the use thereof. 

NOTICE 

The United States Government does not endorse products or 

manufacturers. Trade or manufacturers’ names appear herein sorely 

because they are considered essential to the objective of this report. 
REPORT DOCUMENTATION PAGE Form Approved 

 OMB No. 0704-0188 

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188), Washington, DC 20503. 

1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE 

August 2000 

3. REPORT TYPE AND DATES COVERED 

Final Report 

4. TITLE AND SUBTITLE 

Assessment of Risks for High-Speed Rail Grade Crossings on the Empire Corridor

6. AUTHOR(S) 

Mark Mironer, Michael Coltman, and Robert McCown (FRA) 

5. FUNDING NUMBERS 

RR003/R0047 

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 

U.S. Department of Transportation 

Research and Special Programs Administration 

John A. Volpe National Transportation Systems Center 

Cambridge, MA 02142-1093 

8. PERFORMING ORGANIZATION 

REPORT NUMBER 

DOT-VNTSC-FRA-00-03 

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 

U.S. Department of Transportation 

Federal Railroad Administration 

Office of Railroad Development 

1120 Vermont Avenue, NW 

Mail Stop 20 

Washington, D.C. 20590 

10. SPONSORING/MONITORING 

 AGENCY REPORT NUMBER 

DOT/FRA/RDV-00/05 

11. SUPPLEMENTARY NOTES

12a. DISTRIBUTION/AVAILABILITY STATEMENT 

This document is available to the public through the National Technical Information Service, 

Springfield, VA 22161. This document is also available on the FRA web site at 

www.fra.dot.gov. 

12b. DISTRIBUTION CODE 

13. ABSTRACT (Maximum 200 words) 

The report describes a risk-based approach for assessing the implications of higher train speeds on highway-railroad grade crossing 

safety, and allocating limited resources to best reduce this risk. To predict accident frequency, an existing DOT model was 

extended to include higher speeds. Accidents were statistically grouped according to crash mechanics. By using historical data 

and crashworthiness analysis, the severity of an accident was estimated independently for the highway users and train occupants

based on a number of factors. These included accident type, type of highway vehicle, type of train, and train speed. 

The Empire Corridor in New York State was used to illustrate the application of the approach. The study concludes that the 

increased risk due to higher train speeds can often be more than offset by implementing standard crossing improvements. The 

analysis shows that improving the highest risk crossings in a corridor, rather than the crossings with the highest train speed,

produces the greatest benefit. The report finds that the risk to highway users saturates at train speeds around 65 mph, and that the 

risk to train occupants does not increase dramatically with train speed. The report recommends an incremental migration to 

improved safety. 

15. NUMBER OF PAGES 

50 

14. SUBJECT TERMS 

High-speed rail, grade crossing, highway-rail intersection, risk assessment, corridor assessment, 

Empire Corridor, Amtrak, New York State, Federal Railroad Administration, FRA. 

16. PRICE CODE 

17. SECURITY CLASSIFICATION 

 OF REPORT 

Unclassified

18. SECURITY CLASSIFICATION 

 OF THIS PAGE 

Unclassified

19. SECURITY CLASSIFICATION 

 OF ABSTRACT 

Unclassified

20. LIMITATION OF ABSTRACT 

NSN 7540-01-280-5500 Standard Form 298 (Rev. 2-89) 

Prescribed by ANSI Std. 239-18 

298-102 
REPORT DOCUMENTATION PAGE Form Approved 

 OMB No. 0704-0188 

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188), Washington, DC 20503. 

1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE 

August 2000 

3. REPORT TYPE AND DATES COVERED 

Final Report 

4. TITLE AND SUBTITLE 

Assessment of Risks for High-Speed Rail Grade Crossings on the Empire Corridor

6. AUTHOR(S) 

Mark Mironer, Michael Coltman, and Robert McCown (FRA) 

5. FUNDING NUMBERS 

RR003/R0047 

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 

U.S. Department of Transportation 

Research and Special Programs Administration 

John A. Volpe National Transportation Systems Center 

Cambridge, MA 02142-1093 

8. PERFORMING ORGANIZATION 

REPORT NUMBER 

DOT-VNTSC-FRA-00-03 

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 

U.S. Department of Transportation 

Federal Railroad Administration 

Office of Railroad Development 

1120 Vermont Avenue, NW 

Mail Stop 20 

Washington, D.C. 20590 

10. SPONSORING/MONITORING 

 AGENCY REPORT NUMBER 

DOT/FRA/RDV-00/05 

11. SUPPLEMENTARY NOTES

12a. DISTRIBUTION/AVAILABILITY STATEMENT 

This document is available to the public through the National Technical Information Service, 

Springfield, VA 22161. This document is also available on the FRA web site at 

www.fra.dot.gov. 

12b. DISTRIBUTION CODE 

13. ABSTRACT (Maximum 200 words) 

The report describes a risk-based approach for assessing the implications of higher train speeds on highway-railroad grade crossing 

safety, and allocating limited resources to best reduce this risk. To predict accident frequency, an existing DOT model was 

extended to include higher speeds. Accidents were statistically grouped according to crash mechanics. By using historical data 

and crashworthiness analysis, the severity of an accident was estimated independently for the highway users and train occupants

based on a number of factors. These included accident type, type of highway vehicle, type of train, and train speed. 

The Empire Corridor in New York State was used to illustrate the application of the approach. The study concludes that the 

increased risk due to higher train speeds can often be more than offset by implementing standard crossing improvements. The 

analysis shows that improving the highest risk crossings in a corridor, rather than the crossings with the highest train speed,

produces the greatest benefit. The report finds that the risk to highway users saturates at train speeds around 65 mph, and that the 

risk to train occupants does not increase dramatically with train speed. The report recommends an incremental migration to 

improved safety. 

15. NUMBER OF PAGES 

50 

14. SUBJECT TERMS 

High-speed rail, grade crossing, highway-rail intersection, risk assessment, corridor assessment, 

Empire Corridor, Amtrak, New York State, Federal Railroad Administration, FRA. 

16. PRICE CODE 

17. SECURITY CLASSIFICATION 

 OF REPORT 

Unclassified

18. SECURITY CLASSIFICATION 

 OF THIS PAGE 

Unclassified

19. SECURITY CLASSIFICATION 

 OF ABSTRACT 

Unclassified

20. LIMITATION OF ABSTRACT 

NSN 7540-01-280-5500 Standard Form 298 (Rev. 2-89) 

Prescribed by ANSI Std. 239-18 

298-102 
iii 

PREFACE 

Highway-rail intersections or at-grade crossings are numerous in emerging high-speed corridors, 

averaging more than one crossing per mile of track. While the existing U.S. methodology for 

determining appropriate grade crossing warning devices is risk based, it does not fully address 

high-speed operations. Recent guidelines have been published recommending grade separation 

or fail-safe, full-barrier systems with vehicle detection technology. The cost of grade separation, 

or the full-barrier systems, may be a significant impediment to the introduction of high-speed 

service. If the objective is to reduce risk while improving rail passenger service, careful 

corridor-based analyses are required to achieve the greatest benefit within the limited budgets 

available for most of these projects. 

This report is intended to give the reader a better understanding of the sources of risk in a highspeed corridor and show how these sources of risk change with increased train speeds. The 

report then evaluates the effectiveness of competing risk reduction alternatives. 

This report was prepared by the Volpe National Transportation Systems Center in support of the 

United States Department of Transportation, Federal Railroad Administration’s (FRA) Office of 

Railroad Development. The authors wish to thank Grady Cothen and Bruce George of the 

FRA’s Office of Safety, and Richard McDonough of the New York State Department of 

Transportation for their assistance and critical review during the conduct of this study. 

 
iv 

METRIC/ENGLISH CONVERSION FACTORS 

ENGLISH TO METRIC METRIC TO ENGLISH

LENGTH (APPROXIMATE) LENGTH (APPROXIMATE) 

1 inch (in) = 2.5 centimeters (cm) 1 millimeter (mm) = 0.04 inch (in) 

1 foot (ft) = 30 centimeters (cm) 1 centimeter (cm) = 0.4 inch (in) 

1 yard (yd) = 0.9 meter (m) 1 meter (m) = 3.3 feet (ft) 

1 mile (mi) = 1.6 kilometers (km) 1 meter (m) = 1.1 yards (yd) 

 1 kilometer (km) = 0.6 mile (mi)

AREA (APPROXIMATE) AREA (APPROXIMATE) 

1 square inch (sq in, in2) = 6.5 square centimeters 

(cm2

) 

1 square centimeter (cm2

) = 0.16 square inch (sq in, in2) 

1 square foot (sq ft, ft2) = 0.09 square meter (m2) 1 square meter (m2) = 1.2 square yards (sq yd, 

yd2

) 

1 square yard (sq yd, yd2) = 0.8 square meter (m2) 1 square kilometer (km2) = 0.4 square mile (sq mi, mi2) 

1 square mile (sq mi, mi2) = 2.6 square kilometers 

(km2

) 

10,000 square meters (m2

) = 1 hectare (ha) = 2.5 acres 

1 acre = 0.4 hectare (he) = 4,000 square meters (m2) 

MASS - WEIGHT (APPROXIMATE) MASS - WEIGHT (APPROXIMATE) 

1 ounce (oz) = 28 grams (gm) 1 gram (gm) = 0.036 ounce (oz) 

1 pound (lb) = 0.45 kilogram (kg) 1 kilogram (kg) = 2.2 pounds (lb) 

1 short ton = 2,000 

pounds (lb) 

= 0.9 tonne (t) 1 tonne (t) = 

= 

1,000 kilograms (kg) 

1.1 short tons

VOLUME (APPROXIMATE) VOLUME (APPROXIMATE) 

1 teaspoon (tsp) = 5 milliliters (ml) 1 milliliter (ml) = 0.03 fluid ounce (fl oz) 

1 tablespoon (tbsp) = 15 milliliters (ml) 1 liter (l) = 2.1 pints (pt) 

1 fluid ounce (fl oz) = 30 milliliters (ml) 1 liter (l) = 1.06 quarts (qt) 

1 cup (c) = 0.24 liter (l) 1 liter (l) = 0.26 gallon (gal) 

1 pint (pt) = 0.47 liter (l) 

 1 quart (qt) = 0.96 liter (l) 

1 gallon (gal) = 3.8 liters (l) 

1 cubic foot (cu ft, ft3) = 0.03 cubic meter (m3) 1 cubic meter (m3) = 36 cubic feet (cu ft, ft3) 

1 cubic yard (cu yd, yd3) = 0.76 cubic meter (m3) 1 cubic meter (m3) = 1.3 cubic yards (cu yd, yd3)

TEMPERATURE (EXACT) TEMPERATURE (EXACT) 

[(x-32)(5/9)] °F = y °C [(9/5) y + 32] °C = x °F

QUICK INCH - CENTIMETER LENGTH CONVERSION

0 1 2 34 5

Inches

Centimeters 01 3 2 6 11 4 5 7 13 8 9 10 12

QUICK FAHRENHEIT - CELSIUS TEMPERATURE CONVERSION

 -40° -22° -4° 14° 32° 50° 68° 86° 104° 122° 140° 158° 176° 194° 212°

 

°F

 °C -40° -30° -20° -10° 0° 10° 20° 30° 40° 50° 60° 70° 80° 90° 100°

For more exact and or other conversion factors, see NIST Miscellaneous Publication 286, Units of Weights and 

Measures. Price $2.50 SD Catalog No. C13 10286 Updated 6/17/98
TABLE OF CONTENTS 

Section Page

1. INTRODUCTION......................................................................................................... 1 

 1.1 Purpose.............................................................................................................. 1 

 1.2 Background ....................................................................................................... 1 

 1.3 Risk ................................................................................................................... 3 

 1.4 Outline............................................................................................................... 4 

2. ACCIDENT PREDICTION MODEL........................................................................... 5 

 2.1 Introduction....................................................................................................... 5 

 2.2 Overview of Model ........................................................................................... 5 

3. SEVERITY MODELING ............................................................................................. 9 

 3.1 Introduction....................................................................................................... 9 

 3.2 Approach........................................................................................................... 9 

 3.3 Severity of Crash Scenarios ............................................................................ 11 

 3.3.1 Train into Highway Vehicle................................................................ 11 

 3.3.2 Highway Vehicle into Train................................................................ 15 

 3.4 Scenario Weights ............................................................................................ 16 

 3.5 Composite Severity......................................................................................... 17 

4. EFFECTIVENESS OF GRADE CROSSING WARNING DEVICES ...................... 21 

 4.1 Introduction..................................................................................................... 21 

 4.2 Change in Accident Rate Using the Prediction Model ................................... 21 

 4.3 Effectiveness and Cost of Other Warning Devices......................................... 21 

5. APPLICATION OF METHODOLOGY TO EMPIRE CORRIDOR......................... 23 

 5.1 Introduction..................................................................................................... 23 

 5.2 Safety History.................................................................................................. 23 

 5.3 Accident Prediction......................................................................................... 25 

 5.4 Determination of Risk..................................................................................... 26 

 5.5 Model Validation ............................................................................................ 29 

 5.6 Proposed High-Speed Section......................................................................... 30 

 5.7 Six Highest-Risk Crossings ............................................................................ 31 
TABLE OF CONTENTS (Cont.)

Section Page

 5.8 Comparison of Risk Reduction Strategies ...................................................... 34 

6. CONCLUSIONS AND RECOMMENDATIONS ..................................................... 37 

 6.1 General Comments.......................................................................................... 37 

 6.2 Suggested Actions for the Empire Corridor.................................................... 37 

 6.3 Suggested Modifications to the FRA Guidelines............................................ 37 

 6.4 Effectiveness of New Grade Crossing Treatments ......................................... 38 

APPENDIX 

 Baseline Statistics on Examined Crossings and Predicted Accident Rate................ A-1 

REFERENCES .....................................................................................................................R-1
LIST OF FIGURES 

Figure Page

3-1 Severity Element Breakdown........................................................................................10 

3-2 Fatalities per Accident on Autos Struck by Passenger Train........................................12 

3-3 Fatalities per Accident on Trucks Struck by Passenger Train ......................................12 

3-4 Fatalities per Accident on Truck-Trailers Struck By Passenger Train..........................13 

3-5 Modeled Severity to Train Occupants (Impact Only, No Derailment); 

 Train Striking Highway Vehicle ...................................................................................13 

3-6 Additional Modeled Severity to Train Occupants in Severe Derailment .....................15 

3-7 Total Predicted Severity to HV Occupants for Different Vehicle Mixes .....................17 

3-8 Total Predicted Severity to Train Occupants for Different Vehicle Mixes...................18 

3-9 Predicted Aggregate Severity for Different Vehicle Mixes ..........................................18 

3-10 Total Predicted Aggregate Severity (Baseline Vehicle Mix)........................................19 

5-1 Safety History on Corridor Section Since 1980 (Crashes at Crossings Since 

 Closed Excluded)..........................................................................................................25 

5-2 Map of Section Examined.............................................................................................26 

5-3 Estimated Risk at 27 Crossings (Shown South to North).............................................27 

5-4 Cumulative Estimated Current Risk Travelling North .................................................27 

5-5 Estimated Risk at Each Crossing, Ranked by Total Risk .............................................28 

5-6 Estimated Risk to Train Occupants at Each Crossing, Ranked by Risk .......................28 

5-7 Estimated Risk to Highway Vehicle Occupants at Each Crossing ...............................29 

5-8 Detail of Proposed High-Speed Section........................................................................30 

5-9 Risk at Each Crossing with Proposed Speed Increases.................................................31 
LIST OF FIGURES (Cont.) 

Figure Page

5-10 Comparison of Total Risk: 6 Highest-Risk Crossings vs. Remaining 21 ....................32 

5-11 Comparison of Risk on Train Only: 6 Highest-Risk Crossings vs. 

 Remaining 21................................................................................................................32

5-12 Comparison of Risk on Highway Vehicle Only: 6 Highest-Risk Crossings vs. 

 Remaining 21................................................................................................................33

5-13 Cumulative Total Risk with Increased Train Speed and Six Improved Crossings.......34 

5-14 Comparison of Total Risk for Risk-Reduction Strategies ............................................35 

LIST OF TABLES 

Table Page

1-1 Current Guidelines..........................................................................................................2 

2-1 Crossing Characteristic Factors ......................................................................................6 

3-1 Severity of Vehicle-into-Train Crashes ........................................................................15 

3-2 Distribution of Highway Vehicle Types .......................................................................16 

4-1 Effectiveness and Cost of Crossing Improvements ......................................................22 

5-1 Current Speeds and Warning Devices ..........................................................................24 

5-2 Details of the Six Highest Risk Crossings ....................................................................33 

5-3 Summary of Results......................................................................................................34 

1 

1. INTRODUCTION 

1.1 PURPOSE 

This study develops a risk-based approach for assessing the implications of higher train 

speeds on highway-railroad grade crossing safety and allocating limited resources to best 

reduce this risk. While it is reasonable to assume that increasing train speed at any 

individual crossing will increase the risk to both train and highway vehicle occupants, the 

magnitude of this increase, especially relative to the risk at other crossings, is less clear. 

With limited resources to apply to grade crossing improvements, it is important that the 

available funds be spent where they will do the most good. This analysis shows where 

opportunities for greatest improvements are located. 

 

A section of the Empire Corridor in New York State is used to illustrate the application of 

this approach. 

1.2 BACKGROUND 

High-speed rail passenger service is being encouraged in the United States as evidenced 

by legislation such as the Intermodal Surface Transportation Efficiency Act of 1991 

(ISTEA), the Swift Rail Development Act of 1994, the Transportation Equity Act for the 

21st Century (TEA 21), and the FRA’s Next Generation High-Speed Rail Program. As a 

result of this legislation and other initiatives, 10 high-speed rail passenger service 

corridors have been designated in the United States. High-speed rail operations on these 

and other emerging corridors could eventually result in train speeds above 110 mph. 

These higher train speeds, resulting in shorter trip times, are important in attracting new 

riders to passenger rail. While higher speeds and greater numbers of trains without 

improvements in warning devices at crossings will increase the risk to both highway 

vehicle occupants and train passengers and crew, there are also many public benefits to 

higher speeds, including diversion of highway travelers from congested roads onto trains. 

The goal is to attain this improvement in service while reducing the risk to both rail and 

highway users. 

Historically, crossing improvements have been implemented incrementally. Efforts have 

included the standardization and implementation of crossbucks, lights, and gates. As 

high-risk crossings were identified, first through an examination of accident data, then 

through the use of risk assessment techniques, the warning devices were upgraded at 

high-risk crossings, or the crossings were closed, or were grade separated. Once the risk 

was reduced at these highest-risk crossings, the next highest-risk crossings were 

addressed, and so on. In some cases, use of more effective warning devices has improved 

safety, making increases in train speed acceptable, with the improved service leading to 

greater revenues and ultimately more funds being available for more safety 

improvements. 
Only recently has train speed been a focus in terms of risk. The FRA sponsored a study 

conducted by Battelle [1] under the direction of the Volpe Center to review international 

methodologies for evaluating and improving the effectiveness of grade crossings, warning 

devices, and crossing barriers. The study showed that many countries use risk assessment 

techniques to assess individual crossings, but rarely quantify corridor impacts or other 

optimization strategies. 

The FRA does not specifically require the installation of special warning devices at grade 

crossings. Standard highway practices require at least crossbucks on public crossings. 

Recognizing that these risks must be addressed if high- speed rail service is to be realized, 

the FRA has proposed guidelines for the installation of motorist warning and train 

protection devices at grade crossings on the designated high-speed rail corridors. In 

summary, the FRA Guidelines call for the actions shown in Table 1-1. 

Table 1-1. FRA Guidelines 

Rail 

Speed 

(mph) Public Crossings Private Crossings 

80 to 110 Eliminate all redundant or 

unnecessary crossings. Install the 

most sophisticated traffic 

control/warning devices compatible 

with the location, e.g., median 

barriers, special signing, fourquadrant gates. Automated devices 

should be equipped with constant 

warning time equipment. 

Close, grade separate, or provide a 

secured barrier or automatic devices 

for private crossings. Device or 

barrier should extend across the 

entire highway on both sides of the 

track, should normally be closed and 

opened on request, if no train is 

approaching, for a period of time 

sufficient to cross the tracks. 

111 to 

125 

Protect rail movement with full width 

barriers capable of absorbing impact 

of highway vehicle. Include a failsafe vehicle detection capability 

between barriers. Notify 

approaching trains of warning device 

or barrier failure, or of an intruding 

vehicle in sufficient time for the train 

to stop short of the crossing without 

resorting to emergency brake 

application. 

Protect rail movement with full width 

barrier or gate, normally closed and 

locked, capable of absorbing impact 

of a highway vehicle. Gate lock or 

control should be interlocked with 

train signal and control system and 

released by a railroad dispatcher. A 

fail-safe vehicle detection or video 

system should monitor the area 

between the barriers. The crossing 

should be equipped with a direct link 

telephone to the railroad dispatcher. 

Above 

125 

Close or grade separate all highwayrail crossings. Close or grade separate all highwayrail crossings. 

The implementation of these guidelines would result in a significant level of risk 

reduction. In the case of grade separation for speeds exceeding 125 mph, the risks will be 
reduced to essentially zero. The 111 to 125 mph section for public crossings is more 

ambiguous. There are currently no widely available devices that satisfy these 

requirements which may necessitate grade separation to satisfy the guidelines. There are 

significant cost impacts associated with implementation of the guidelines on the 

designated high-speed rail corridors. These corridors include about 2,000 public and 900 

private crossings over some 3,000 route miles. At a cost of $3 million for a typical grade 

separation, upgrading these crossings to the requirements of the guidelines will incur 

significant costs and exhibit other impacts to the states, communities, and railroads 

affected. The high cost of these safety requirements could serve as a significant 

impediment to furthering the implementation of high-speed rail service. Other motorist 

warning and train protection device alternatives applied in a “corridor” approach may 

achieve similar levels of risk at a much lower cost. 

It should also be noted that while the guidelines present recommendations for speeds 

under 110 mph, the practice has been to apply less costly warnings, if any, at such 

crossings. A large effort would be required to bring all crossings into compliance with 

these guidelines, even at present train speeds. 

While the guidelines are an excellent ultimate goal, given limited budgets for grade 

crossing improvements, an incremental approach is needed to reach that goal. By 

focusing on the highest-risk crossings regardless of train speed, safety can be 

incrementally improved as much as resources allow until the goals are reached. 

 

In 1998, the FRA finalized new track safety standards (June 22, 1998). In these new 

rules, the FRA requires a carrier to submit warning or protection plans for crossings 

where the speeds are authorized above 110 mph. The High-Speed Track Safety 

Standards, Federal Register, CFR 49, Part 213.347 (b)[2] state: 

If train operation is projected at Class 7 speed for a track segment that will include 

rail-highway grade crossings, the track owner shall submit for FRA’s approval a 

complete description of the proposed warning/barrier system to address the 

protection of highway traffic and high-speed trains. 

An important aspect of the required submittal might include an estimate of corridor risk. 

The new high-speed standards prohibit crossings where track speeds exceed 125 mph. 

1.3 RISK 

The concept of ‘risk’ requires some explanation. Risk is the product of the probability of 

an event occurring, and the severity of that event. The units of these terms vary greatly 

from study to study. In this report, the probability is defined as the predicted number of 

grade crossing accidents along a certain set of crossings per year. The severity is defined 

in terms of fatalities (both on the train and highway vehicle) per accident. Therefore, the 

risk in this study is defined as predicted fatalities per year at that set of crossings. 
Other possible measures of severity include injuries, property damage, and other impacts 

and costs, such as delays and public perception. Fatalities were chosen as being an 

essential measure of safety without some of the ambiguity involved in injury counts. The 

other measures are open to even more interpretation, although it is likely that the 

crossings where the most fatalities occur also would be the most risky by these other 

measures as well. 

Since risk is the product of these two quantities, if at one crossing there is one accident 

per year with one fatality, and at another crossing there is only one accident every 10 

years, but there are ten fatalities in that crash, the statistical risk is the same at each 

crossing – one fatality per year. 

1.4 OUTLINE 

Sections 2 through 4 describe the methods used in this analysis. Section 2 focuses on the 

accident prediction model and Section 3 on the severity model. Section 4 presents the 

costs and effectiveness of crossing warning alternatives. Section 5 applies these models to 

a set of crossings in New York, the Empire Corridor. Finally, Section 6 summarizes 

these results, and presents recommendations. 
2. ACCIDENT PREDICTION MODEL 

2.1 INTRODUCTION 

The first half of the risk equation is accident probability or frequency. The chance of an 

accident at a particular crossing varies depending on many of the characteristics of the 

crossing itself. To predict accident frequency, an existing DOT model was used [3]. 

Since the model is described and validated in depth in the referenced report, this section 

will present only a brief overview of the model. 

It should also be noted that since the overall analysis approach is modular, it is quite easy 

to substitute another accident prediction model, such as one specific to the site being 

examined. 

2.2 OVERVIEW OF MODEL 

The model was derived by “applying nonlinear multiple regression techniques to crossing 

characteristics stored in the US DOT/FRA Highway-Rail Grade Crossing Inventory and 

to accident data contained in the FRA Railroad Accident/Incident Reporting System.” [4] 

This approach yielded a formula that would predict the probable number of accidents at a 

given crossing, based on the data found in the Inventory. The equation is dominated by 

the exposure index term that combines the average daily traffic count and the number of 

trains. This equation is: 

 a = K x EI x MT x DT x HP x MS x HL 

 

 where, 

a = un-normalized accident prediction (accidents/year at the crossing) 

K = constant for initialization of factor values at 1.00 

EI = factor for exposure index based on product of highway and train traffic 

MT = factor for number of main tracks 

DT = factor for number of through trains per day during daylight 

HP = highway paved factor 

MS = factor for maximum timetable speed 

HL = factor for number of highway lanes 

Table 2-1 gives the formulas to determine each of these factors based on the data in the 

Inventory. 
6 

Table 2-1. Crossing Characteristic Factors 

Crossing 

Category 

Formula 

Constant K 

Exposure 

Index EI 

Main 

Tracks 

Factor 

MT 

Day Thru 

Trains 

Factor DT 

Highway 

Paved 

Factor HP 

Max 

Speed 

Factor 

MS 

Highway 

Lanes 

Factor HL 

Passive 0.0006938  c t⋅ +



 



 0 2

0 2

0 37 .

.

. 1  d +



 



 0 2

0 2

0 178 .

.

.

e

-0.5966(hp-1) e0.0077ms 1.0 

Flashing 

Lights 

0.0003351  c t⋅ +



 



 0 2

0 2

0 4106

.

.

.

e

0.1917mt  d +



 



 0 2

0 2

0 1131 .

.

. 1.0 1.0 

e

0.1826(hl-1)

Gates 0.0005745  c t⋅ +



 



 0 2

0 2

0 2942

.

.

.

e

0.1512mt  d +



 



 0 2

0 2

0 1781 .

.

. 1.0 1.0 

e

0.1420(hl-1)

General Form of Accident Prediction Formula: a = K x EI x MT x DT x HP x MS x HL 

c = annual average number of highway vehicles per day (total both directions) 

t = average total train movements per day 

mt = number of main tracks 

d = average number of through trains per day during daylight 

hp = highway paved (yes = 1, no = 2) 

ms = maximum timetable speed, mph 

ht = highway type factor value 

hl = number of highway lanes 

To obtain the normalized value, one must multiply the predicted value by the appropriate 

normalizing constant found in each year’s Highway-Rail Crossing Accident/Incident and 

Inventory Bulletin. These constants were last modified in 1992, and were 0.8239 for 

passive crossings, 0.6935 for crossings with flashing lights, and 0.6714 for gated 

crossings. 

There are several notes regarding this model. First, the referenced report describes how 

the accident prediction, as found from the above equation, should be weighted with the 

actual accident history to obtain a more accurate prediction estimate. In determining the 

effect of increasing speeds at a given crossing, however, the history is no longer 

applicable, and so the un-weighted prediction is used here. Additionally, while the 

preferred method of obtaining an estimate for the accident rate, when the protection on a 

crossing is improved, is to apply an effectiveness rate to the baseline prediction, once 

again, because of the changed speeds, this method was not used. Instead, the accident 

rate is predicted from the foregoing equations. 
As mentioned previously, the model was derived by applying regression techniques to the 

data in the grade crossing inventory. There may be crossing characteristics that affect 

accident probability that are not included in the inventory, such as sight distance and 

crossing geometry – they do not appear in the model. The model does not distinguish 

between freight and passenger trains, since the distinction is not made in the inventory. 

While the model does incorporate many factors, it should be noted that the dominant 

variables are the type of crossing warning, and the train and highway vehicle traffic 

density. 

The regression showed that for Flashing Lights and Gates, train speed does not affect 

accident probability. The implication for this analysis is that at higher speeds, the 

increase in risk is due entirely to severity, rather than accident probability. 

Since the model was based on historical accident data, it could not have been verified on 

the higher speeds being considered here. To assume that the insensitivity to train speed at 

active crossings that holds from 0 to 80 mph extends to higher speeds as well seems 

reasonable, however. Similarly, using the same formula for passive crossings at speeds 

above 80 mph, as is used at lower speeds, is the best approximation at the present time. 

Finally, although the accident prediction model includes a severity calculation, it was not 

used here for several reasons. First, there is no differentiation between freight trains and 

passenger trains (the latter have a much greater severity potential). Secondly, the output 

is the likelihood of a fatal accident, not a prediction of numbers of fatalities, which is 

needed for this risk analysis. Third, the model was not designed for higher speed 

accidents, and since the focus of this study is the risk at such crossings, an independent 

severity model was needed. 

Accident probability, the first element of risk, is calculated as described above. The next 

step is to determine the severity as a function of train speed, which will be described in 

the following section. 

9 

3. SEVERITY MODELING 

3.1 INTRODUCTION 

In generating a model of severity as a function of train speed, the authors attempted to be 

as realistic as possible, while still being confident that the actual severity could be no 

worse than that which is predicted. There have been too few grade crossing accidents 

involving high-speed trains to provide significant statistics directly indicating severity. 

Therefore, wherever possible, the FRA data bases of accidents at lower speeds were used 

as a starting point. 

The severity model is modular, so that if new data become available, the model can be 

easily changed to incorporate the refinements. 

3.2 APPROACH 

The severity of a grade crossing accident depends on a number of factors, including 

accident type, type of highway vehicle, type of train, and train speed. In order to model 

severity, accidents were statistically broken down into categories with distinct crash 

mechanics. Each of these categories could then be examined using historical data, 

statistics, and crashworthiness analysis to predict severity. This approach is illustrated on 

Figure 3-1. 

The first part of Figure 3-1 is the top level tree, where accidents are broken into the two 

main branches; Train Striking Highway Vehicle and Highway Vehicle Striking Train. 

Since this analysis focuses on high-speed rail operations and is intended to set an upper 

bound for risk, it was assumed that all trains involved in the predicted accidents were 

passenger trains, and that the trains traveled at the maximum allowable track speed. 

The middle section of Figure 3-1 shows the breakdown of the highway-vehicle-into-train 

crash mode. Since autos, trucks and truck-trailers account for 99.1 percent of the grade 

crossing accidents involving passenger trains (excluding pedestrians and unspecified 

vehicles), only those three categories are considered in this analysis. As can be seen from 

the figure, in each case, some harm to both the train and the highway vehicle is generated 

from the impact itself. Then, if the accident causes a derailment, additional harm may be 

realized. 

The final section of Figure 3-1, the tree structure for train-into-highway-vehicle, is based 

on a similar logic. 

The following section describes how the values of harm for each branch of the tree were 

determined. 
10

Train Into

Highway Vehicle

Highway Vehicle

Into Train

Grade Crossing Accident

Additional Train

Severity

Derailment No Derailment

Train Severity

Highway Vehicle Severity

Auto

Additional Train

Severity

Derailment No Derailment

Train Severity

Highway Vehicle Severity

Truck

Additional Train

Severity

Derailment No Derailment

Train Severity

Highway Vehicle Severity

Truck-Trailer

Highway Vehicle into Train

Additional Train

Severity

Derailment No Derailment

Train Severity

Highway Vehicle Severity

Auto

Additional Train

Severity

Derailment No Derailment

Train Severity

Highway Vehicle Severity

Truck

Additional Train

Severity

Derailment No Derailment

Train Severity

Highway Vehicle Severity

Truck-Trailer

Train into Highway Vehicle

Figure 3-1. Severity Element Breakdown
11 

3.3 SEVERITY OF CRASH SCENARIOS 

The severity models were based on the crash data bases and were supplemented with 

engineering analysis where necessary. 

3.3.1 Train Into Highway Vehicle 

The harm to the highway vehicle is considered first. As expected, the accident data show 

that the severity generally increases with increasing train speed. Between 70 and 80 mph, 

the severity levels off, presumably because the maximum harm for the impact location is 

being done to the highway vehicle and increased speed cannot do more damage. The 

number is somewhat less than the average occupancy since some accidents are survivable 

regardless of train speed. For example, if the train just “nicks” the front of the highway 

vehicle, then most often there will be no fatalities even at high train speeds. Since the 

severity of the impact is related to the kinetic energy involved, a quadratic relationship 

was used to model the severity below the peak speed. Figures 3-2, 3-3, and 3-4 show the 

modeled curve compared to the actual data for the three vehicle types. Since the train 

speeds of greatest interest to this analysis are between 80 and 125 mph, the quadratic 

relationships do not affect those results but are included for completeness. 

The accident data base was first analyzed to model fatalities on the train. Of the 9,615 

passenger train grade crossing accidents between 1975 and 1995, there were 42 deaths of 

train occupants (including crew). This represents an historical severity rate of about 

0.00437 fatalities per accident. However, 34 of these fatalities occurred between 1975 

and 1978. Most likely this dramatic change in fatality rate is due to the improvements in 

train crashworthiness and crossing warnings that were applied in this time period. From 

1979 through 1995, there were 8 fatalities in the 6,742 accidents, for a rate of 0.0019 

fatalities per accident. With such a small number of fatalities, it was not possible to 

derive a relationship between train speed and fatalities on the train. Instead, it was again 

assumed that harm to the train was related to kinetic energy and hence quadratic in form 

[5]. Furthermore, the crash energy is related to the mass of the vehicle struck, so one 

would expect the severity when striking an auto to be less than striking a truck, which in 

turn is less than striking a truck-trailer. Figure 3-5 shows the relationship representing the 

severity of striking each type of vehicle. 

For purposes of this study, a conservative severity relationship was assumed. For 

example, Figure 3-5 shows that the severity to train occupants of a truck-trailer collision 

at 80 mph is about 0.28 fatalities per accident. In the data base, there were 104 accidents 

involving truck-trailers at train speeds of 71-80 mph from 1975-95, resulting in 6 

fatalities of train occupants, a rate of 0.06 fatalities per accident. But the potentially 

higher probability of a catastrophic crash involving this type of vehicle justifies this 

conservative estimate. 
12 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80

Train Speed

Severity (Fatalities/Accident)

Actual 1975-1995 Model

Figure 3-2. Fatalities per Accident on Autos Struck by Passenger Train 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

5

101520253035404550556065707580

Train Speed

Severity (Fatalities/Accident)

Actual 1975-1995 Modeled

Figure 3-3. Fatalities per Accident on Trucks Struck by Passenger Train 
13 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

5

101520253035404550556065707580

Train Speed

Severity (Fatalities/Accident)

Actual 1975-1995 Modeled

Figure 3-4. Fatalities per Accident on Truck-Trailers Struck by Passenger Train 

0

0.2

0.4

0.6

0.8

1

5 25 45 65 85 105 125 145

Train Speed (MPH)

Severity (Fatalities/Accident)

Auto

Truck

Truck Trailer

Figure 3-5. Modeled Severity to Train Occupants (Impact Only, No Derailment); 

Train Striking Highway Vehicle 
14 

Additional harm to the train occupants can occur if the train derails. Therefore, the rail 

equipment accident data base was cross-indexed with the grade crossing data base to 

obtain data on derailments caused by grade crossing accidents. Since the format of the 

rail equipment data base changed after 1991, the 4 years 1992-1995 were used. 

Of the 1,018 passenger train accidents at grade crossings in this period, there were 13 

derailments. Of these, 10 were train striking truck trailer, 2 were train striking truck, and 

one was auto striking train. There were no fatalities on any of the trains. In examining 

the 19,267 crashes of all remaining types of trains at grade crossings, it was shown that 

there were 72 additional derailments. Again, there were no fatalities, and the majority 

involved truck trailers, followed by trucks and then autos. From the passenger train data, 

probabilities of a derailment of a passenger train striking a highway vehicle were derived 

as 14 percent for truck trailers, 2 percent for trucks, and 0.2 percent for autos. Since most 

of these derailments were of low severity, it was further assumed that only 5 percent were 

serious enough to be life-threatening.1

 Finally, assuming that the fatalities on a train 

resulting from a derailment are related to the square of the train speed, the relationship 

shown on Figure 3.6 indicates the additional number of fatalities incurred on the train if it 

is involved in a severe derailment. Additionally, the train speed may affect the 

probability of a severe derailment. In the absence of any fatality data, however, a flat 

rate, with the speed effects included in the severity curve, provides a reasonable estimate 

of the likely severity from derailment. 

The probability that a train in a given crash will derail is actually a function of many 

factors. Perhaps the most important is the geometry of the crossing. In examining the 

risk on a corridor, crossings with sharp turns or steep embankments must be considered. 

An additional note on interpreting the implications of this model. Consider a severity of 

0.5 fatalities/accident as an example. While one fatality every two crashes could produce 

that severity level, it could also mean that there could be nine crashes without a fatality, 

and one where five people are killed. 

 

1

 A sensitivity analysis was conducted regarding the percentage of severe derailments. If the proportion 

were increased to 20 percent, total risk would increase by 0.7 percent, and risk to train passenger by 6 

percent. Even if it were assumed that all derailments were “severe,” the total risk would increase by 4 

percent, and risk to train passengers would increase by 36 percent, a relatively minor increase for 20 times 

more derailments. 
15 

0

0.5

1

1.5

2

2.5

3

3.5

5

101520253035404550556065707580859095

100105110115120125

Train Speed (MPH)

Fatalities/Severe Derailment

Figure 3-6. Additional Modeled Severity to Train Occupants in Severe Derailment 

3.3.2 Highway Vehicle Into Train 

This was a relatively infrequent crash mode, with generally extremely low harm to the 

occupants of the train, but often great severity to the highway vehicle. 

First, consider the highway vehicle. While the severity is highly dependent on the speed 

of the vehicle, the train speed does not seem to contribute to severity. For example, 

historically, there have been a significant number of fatalities for highway vehicles 

striking a stopped train. Therefore, for this analysis, it was assumed that the severity to 

the vehicle occupants was independent of the train speed. It was further assumed that 

increasing the speed of the trains would not affect the severity on the highway vehicles. 

To determine the severity (expressed in predicted fatalities per accident), the accident 

data from 1975-1995, were examined to see how many of each accident type occurred, 

and the number of fatalities that resulted. For example, there were 751 cases of autos 

running into passenger trains, with 163 fatalities as a result, so that the severity was 

163/751 = 0.217 fatalities/accident. Two sets of data were examined, vehicles into all 

trains, and vehicles into passenger trains, and the more severe result was used (“passenger 

train” data for autos and trucks, “all train” data for truck-trailers). These results are 

shown in Table 3-1. 

Table 3-1. Severity of Vehicle-Into-Train Crashes 

Autos Trucks Truck Trailers 

Vehicle Fatalities/Accident 0.217 0.16 0.091 

Train Fatalities/Accident 0.01 0.01 0.01 
16 

In the 21 years of accident data from 1975 to 1995, there were no train fatalities resulting 

from any of the 984 cases of a train being struck by a highway vehicle [5]. Prior to 1975, 

there had been several tragic examples of fatalities resulting from this crash type, but 

changes in rail car crashworthiness make statistics from this era inapplicable to the 

current situation. Clearly, in the absences of any historical data, it is impossible to derive 

any empirical model. Since fatalities are still possible, however, particularly if the train 

derails, it was conservatively assumed that there was one train fatality per hundred 

accidents, for all vehicle types, including any harm from a possible derailment. This 

represents all the severity for train occupants in highway vehicle-into-train accidents on 

Figure 3-1. 

3.4 SCENARIO WEIGHTS 

In the preceding sections, the severity for each scenario on the fault tree was determined. 

To derive an overall severity figure, the distribution of these scenarios must be calculated, 

and the values combined. Again, the accident data base was used to derive these values. 

Working down the tree, the first level is whether or not the train strikes the vehicle, or the 

vehicle strikes the train. For all types of trains, this distribution is around 75 percent and 

25 percent, respectively. However, from the data for 1975-1995, looking only at 

passenger trains, 84 percent of the time the train struck the vehicle. Since this study 

focuses on passenger trains, and this leads to a more conservative result, these values 

were used. 

Similarly, the distribution of highway vehicles involved in accidents with passenger trains 

was also used. Approximately 8 percent of these crashes involved pedestrians or 

motorcycles, but these are unlikely to cause deaths on the train, and also are unlikely to be 

more severe at speeds greater than 80 mph. Therefore, since this analysis focuses on the 

sensitivity of risk to train speed, accidents with pedestrians and motorcycles are not 

modeled here. Instead, it is assumed that all accidents fall into one of the three categories 

shown on the tree: autos, trucks, and truck trailers. The distributions are listed in Table 

3-2. Vehicles such as school buses are not included here. Statistically, they comprise 

only a fraction of a percent of the accident problem, and so an analysis such as this would 

not give the problem much weight. However, it is essential that the risk to school bus 

passengers be adequately addressed, and so it should be considered on a qualitative rather 

than a quantitative basis. 

Table 3-2. Distribution of Highway Vehicle Types 

 Autos Trucks Truck Trailers 

Percent of Vehicle Type 72.6 19.5 7.9 

It is important to realize that the actual mix of vehicles at a particular crossing could have 

a major impact on the severity of accidents. A crossing used primarily by light vehicles 
17 

will have high severity for the highway vehicle passengers, and relatively low severity for 

the train. If, however, the crossing is used primarily by heavy trucks, the severity to the 

highway vehicle is lower, but the severity to the train increases. Figures 3-7 and 3-8 

show the effect of vehicle mix on the severity to the highway vehicle and train 

respectively, while Figure 3-9 shows the effect on the total severity As one can see from 

the figure, even a shift from vehicle traffic consisting only of automobiles to one of all 

truck-trailers has a smaller effect on total severity than one might expect. 

It should also be noted that crossings with significant traffic of special types of vehicles, 

e.g., school buses and hazardous material carriers, need to be assessed independently 

rather than on the “average severity” approach used here. 

3.5 COMPOSITE SEVERITY 

When the data from Sections 3.3 and 3.4 are combined, an overall severity curve can be 

determined. This is illustrated on Figure 3-10. The initial rapid slope is due to the 

fatalities in the highway vehicles. Above a train speed of 80 mph, the more gradual 

increase is due to the fatalities on the train. 

By combining the severity in this section with the accident probability estimates in 

Section 2, the estimated risk at each crossing can be obtained. The following section 

examines some of the methods for reducing these risks. 

0

0.2

0.4

0.6

0.8

1

5

152535455565758595

105115125

Train Speed (MPH)

Severity (Fatalities/Accident)

All Autos .73a/.19t/.08tt .2a/.3t/.5tt All Truck Trailer

Figure 3-7. Total Predicted Severity to HV Occupants for Different Vehicle Mixes
18 

0

0.2

0.4

0.6

0.8

1

5

152535455565758595

105115125

Train Spe e d (M PH)

Severity 

(Fatalities/Accident)

A ll A utos .73a/.19t/.08tt .2a/.3t/.5tt A ll Truck Trailer

Figure 3-8. Total Predicted Severity to Train Occupants for Different Vehicle Mixes 

0

0 .1

0 .2

0 .3

0 .4

0 .5

0 .6

0 .7

0 .8

0 .9

1

5

152535455565758595

105115125

T r ain S p e e d (M P H)

Severity (Fatalities/Accident)

A ll A u to s .73 a /.1 9 t/.0 8tt .2a/.3t/.5tt A ll T r u c k T r a ile r

Figure 3-9. Predicted Aggregate Severity for Different Vehicle Mixes 
19 

Figure 3-10. Total Predicted Aggregate Severity (Baseline Vehicle Mix) 
20 
21 

4. EFFECTIVENESS OF GRADE CROSSING WARNING 

DEVICES 

4.1 INTRODUCTION 

The previous sections have described a method for quantifying risk at grade crossings. 

This section will present the approach used for determining how that risk can be 

decreased by improving the warning devices at grade crossings. 

4.2 CHANGE IN ACCIDENT RATE USING THE PREDICTION MODEL 

As described in Section 2, the accident prediction model was based on regression 

performed independently for each of the three warning types examined (passive, flashing 

lights, and gates). The “Rail-Highway Crossing Resource Allocation Procedure User’s 

Guide” prescribes applying an effectiveness rate to the accident rate for the lesser warning 

to determine the accident rate with the new warning device. This approach was not used 

here for several reasons. 

First, that method was not intended to deal with increases in speed. Since passive 

crossing accident rates in the model are dependent on speed, and active crossings are not, 

there is an ambiguity as to whether to apply the effectiveness before or after calculating 

the effects of the speed. 

Second, since the accident history of the crossing was not used (for the reasons outlined 

in Section 2), it was assumed that in the steady state, two crossings with identical 

characteristics should have the same accident rate. 

Finally, at least for the crossings to be examined in the following section, applying the 

accident prediction formula to the improved crossing rather than using the effectiveness 

numbers lead to a more conservative result. For example, putting gates on a particular 

passive crossing (in this case the Pirate Canoe crossing) results in a 75 percent 

effectiveness using the formula, rather than the 80-85 percent found in the user’s manual. 

Similarly, changing a crossing with flashing lights (e.g., the Hamilton Paper crossing) to 

gates achieves an effectiveness of 41 percent, rather than the 69 percent in the manual. 

4.3 EFFECTIVENESS AND COST OF OTHER WARNING DEVICES 

While the regression model can be used as described above for gates and flashing lights, 

there is no formula that can be applied to advanced warning devices such as median 

barriers and four-quadrant gates. Therefore, effectiveness rates must be used instead. 
22 

The “Sealed Corridor” study conducted by the North Carolina Department of 

Transportation and the Norfolk Southern Corporation [7] examined the effectiveness of 

these devices. While the results are preliminary, they provide a reasonable approximation 

of the effectiveness of such systems. These values are shown in Table 4-1. 

The cost estimates were based on values found in the literature [6,7,8]. It is assumed that 

all types of warning devices can be installed at all crossings, and that the cost is the same 

at each crossing, regardless of the particular geometry of that crossing. 

Table 4-1. Effectiveness and Cost of Crossing Improvements 

 Median 

Barriers 

4-Quadrant Gates Grade Separation 

Effectiveness (Over Gates) 30% 40% 100% 

Cost Estimate $20,000 $125,000-250,000 $1,000,000-3,000,000 

As for installing gates on passive crossings, the cost was also estimated at $125,000-

250,000 as well, and as mentioned above, the effectiveness was determined using the 

accident prediction model. 

Finally, for some low-volume passive crossings, the risk can be essentially eliminated by 

either closing the crossings or installing dispatcher controlled gates, or another form of 

locked gates. However, the “cost” of such modifications is heavily dependent on factors 

other than the equipment needed, such as whether or not alternate routes exist, the nature 

of the highway vehicle traffic at the crossing, etc. Therefore, no figures are given here. 

The next section examines a section of the New York Empire Corridor, applying the 

methodology discussed. 
23 

5. APPLICATION OF METHODOLOGY TO EMPIRE 

CORRIDOR 

5.1 INTRODUCTION 

The Empire Corridor runs between New York City and Buffalo, New York. There is 

considerable interest in the effect of raising train speeds on a particular portion of this 

corridor along the Hudson River. New York State has a goal of eventually reducing the 

travel time between Albany and New York City to less than 21⁄2 hours. The increased 

ridership and revenue could then be applied toward additional investments, leading to a 

zero-risk corridor with no at-grade crossings. However, before the train speed can be 

increased, improvements must be made to the Corridor so that even with the higher train 

speed, corridor safety is improved. Therefore, this 94-mile section of the Empire 

Corridor, located between Poughkeepsie and Albany, was chosen to illustrate the 

methodology described in this report. 

5.2 SAFETY HISTORY 

Since 1980, trains have been running at a maximum speed of 110 mph over sections of 

the Empire Corridor. While the train speed varies based on geometry and proximity to 

stations, most crossings have a maximum speed of 110, 95, or 90 mph. These are shown 

in Table 5-1. 

In the 17 years of train operations at these higher speeds, there have been only three 

accidents at public crossings, with no fatalities. This includes 10 days in 1995, when 

trains were operated without incident at speeds as high as 125 mph during a special test. 

The record at private crossings is much less encouraging. In the same period of time, 

there were 15 accidents at private crossings, resulting in 5 injuries and 5 fatalities. Two 

of these crossings subsequently have been closed; therefore, the accidents at these 

crossings are not included in the analysis. Figure 5-1 shows the distribution of the 16 

accidents and 4 fatalities along the section studied. 
24 

Table 5-1. Current Speeds and Warning Devices 

Crossing Mile Post 

 Current 

Allowable 

Train Speed Crossing Type 

Manitou 46.04 80 Gated Public 

King’s Dock 47.40 80 Passive Private 

Bank St. 62.55 80 Gated Public 

Pirate Canoe 71.00 90 Passive Private 

Captains 3 75.95 90 Gated Private 

Private Rd. 81.00 95 Passive Private 

Pok Yacht 81.59 90 Gated Private 

River Rd. 83.70 95 Gated Public 

Tivoli Dock 98.95 95 Gated Private 

Clermont 100.70 95 Passive Private 

Private Rd 102.60 90 Passive Private 

Cheviot 103.70 90 Gated Public 

Dock Rd. 106.10 90 Gated Public 

Oak Hill 109.50 90 Passive Private 

Hallenbeck 111.98 90 Passive Private 

Broad St. 114.30 50 Gated Public 

Ferry Rd. 121.95 90 Gated Public 

Ice House 122.25 90 Passive Private 

Stuyvesant 124.81 110 Gated Public 

Hook Boat 126.98 110 Passive Private 

Walsh’s 133.85 110 Passive Private 

Green Av. 134.00 110 Gated Public 

Scott St. 134.20 110 Gated Public 

Hamilton Printing 134.90 110 Flashing Lights Private 

Staat’s 137.20 110 Gated Public 

Ablee’s 139.10 110 Passive Private 

Teller's 139.98 110 Gated Private 

(Proposed High-Speed Section Shaded) 
25 

0

1

2

3

4

Manitou

King’s Dock

Bank St.

Pirate Canoe

Captains 3

Private Rd.

Pok Yacht

River Rd.

Tivoli Dock

Clermont

Private Rd

Cheviot

Dock Rd.

Oak Hill

Hallenbeck

Broad St.Ferry Rd.

Ice House

Stuyvesant

Hook Boat

Walsh’s

Green Av

Scott St.

Hamilton Printing

Staat’s

Ablee’s

Teller's

Number of Incidents

Accidents Fatalities

Figure 5-1. Safety History on Corridor Section Since 1980 (Crashes at Crossings 

Since Closed Excluded) 

The five fatalities occurred as the result of the following three accidents: 

• December 8, 1985. Tivoli Dock Crossing. Three Highway Vehicle Occupants Killed. 

• January 27, 1990. Mile Post 133.05. One Highway Vehicle Occupant Killed. This 

crossing has since been closed. 

• August 14, 1997. Hamilton Printing. One Highway Vehicle Occupant Killed. 

No train occupant fatalities have occurred in this time frame. 

5.3 ACCIDENT PREDICTION 

Data were obtained from the Grade Crossing Inventory and the New York State 

Department of Transportation on the characteristics of 27 crossings in the area of interest, 

as shown on Figure 5-2. This section extends from Philipston (mile post 46), just north 

of Peekskill to Rensselaer (mile post 140), south of Albany. Table 5-1 lists the crossings, 

the mileposts, the current allowable train speed and the type of crossing. The data for 

each crossing were imparted into the accident prediction model described in Section 2. 

The Appendix lists this data, as well as the predicted accident rate at each crossing. 
26 

Figure 5-2. Map of Section of Empire Corridor Examined 

5.4 DETERMINATION OF RISK 

Section 3 presented the severity model derived for this project. Risk was derived by 

multiplying the accident rate by the severity for the given train speed at each crossing. 

Figure 5-3 shows the current estimated risk at each crossing as one travels north on the 

corridor. This risk is broken down into risk to train occupants, highway vehicle 

occupants, and the total of these two risk elements. 

Figure 5-4 displays this same data as a running total of the cumulative risk as one 

traverses the corridor. By looking at the final bar, one can see that there are several thick 

bands, which represent higher risk crossings where improvements may have the greatest 

benefit. 

Figures 5-5, 5-6, and 5-7 rank these data from lowest to highest risk, with Figure 5-5 

based on total risk, and Figures 5-6 and 5-7 ranking on risk to train occupants and 

highway vehicle occupants respectively. Note that the same six crossings appear at the 

highest risk end of all three figures (Pirate Canoe, Hook Boat, Hamilton Printing, Bank 

Street, Manitou Road and Captains 3), although the order changes slightly. 
27 

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

Manitou

King’s Dock

Bank St.

Pirate Canoe

Captains 3

Private Rd.

Pok Yacht

River Rd.

Tivoli Dock

Clermont

Private Rd

Cheviot

Dock Rd.

Oak Hill

Hallenbeck

Broad St.Ferry Rd.

Ice House

Stuyvesant

Hook Boat

Walsh’s

Green Av

Scott St.

Hamilton Printing

Staat’s

Ablee’s

Teller's

Risk (Fatalities/Year)

Train HV Total

Figure 5-3. Estimated Risk at 27 Crossings (Shown South to North) 

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Manitou

King’s Dock

Bank St.

Pirate Canoe

Captains 3

Private Rd.

Pok Yacht

River Rd.

Tivoli Dock

Clermont

Private Rd

Cheviot

Dock Rd.

Oak Hill

Hallenbeck

Broad St.Ferry Rd.

Ice House

Stuyvesant

Hook Boat

Walsh’s

Green Av

Scott St.

Hamilton Printing

Staat’s

Ablee’s

Teller's

Risk (Fatalities/Year)

Figure 5-4. Cumulative Estimated Current Risk Travelling North 
28 

0.00

0.02

0.04

0.06

0.08

0.10

Private Rd

Oak Hill

Scott St.

Walsh’s

Teller's

Clermont

Ablee’s

Cheviot

Dock Rd.

Ferry Rd.

Ice House

Stuyvesant

Tivoli Dock

Private Rd.

Pok Yacht

Broad St.

Green Av

Staat’s

King’s Dock

Hallenbeck

River Rd.

Captains 3

Manitou

Bank St.

Hamilton Printing

Hook Boat

Pirate Canoe

Risk (Fatalities/Year)

Train HV Total

Figure 5-5. Estimated Risk at Each Crossing, Ranked by Total Risk 

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

0.01

Private Rd

Oak Hill

Scott St.

Walsh’s

Teller's

Broad St.

Clermont

Cheviot

Dock Rd.Ferry Rd.

King’s Dock

Ice House

Tivoli Dock

Private Rd.

Pok Yacht

Ablee’s

Hallenbeck

Stuyvesant

Green Av

Staat’s

River Rd.

Captains 3

Manitou

Bank St.

Hamilton Printing

Hook Boat

Pirate Canoe

Risk (Fatalies/Year)

Figure 5-6. Estimated Risk to Train Occupants at Each Crossing, Ranked by Risk 
29 

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

Scott St.

Private Rd

Oak Hill

Walsh’s

Teller's

Clermont

Ablee’s

Cheviot

Dock Rd.Ferry Rd.

Stuyvesant

Ice House

Tivoli Dock

Private Rd.

Green Av

Staat’s

Pok Yacht

King’s Dock

Broad St.

Hallenbeck

River Rd.

Captains 3

Manitou

Hamilton Printing

Bank St.

Hook Boat

Pirate Canoe

Risk (Fatalities/Year) Figure 5-7. Estimated Risk to Highway Vehicle Occupants at Each Crossing 

5.5 MODEL VALIDATION 

Risk for the model is defined in terms of fatalities and is derived from the number of 

accidents and the severity (number of fatalities) of those accidents. Over the 18-year 

period reviewed, there were 16 accidents at the crossings still open, a rate of 0.89 per 

year. (Crossings that were closed were not included in calculating either the actual or 

predicted amounts.) The model would predict, after adjusting the coefficients of the 

prediction formulas, 10.9 accidents over the same 18 years. Some improvements may 

have been made at crossings during the review period, which would account for the 

variation. 

In the 18-year period, there were four fatalities at these crossings, or 0.22 fatalities per 

year and 0.25 fatalities/accident. The model predicts 5.9 fatalities over the same period. 

These predicted fatalities – the risk – are determined by calculating the expected fatalities 

per year at each crossing, summing over all crossings to obtain a 0.33 rate, and then 

multiplying by 18 years to obtain 5.9 fatalities per year (0.33 x 18). 

It is tempting to appraise model validity by comparing actual to predicted values, as given 

above, for accidents and fatalities. For reasons to be discussed, however, such 

comparisons should not serve as a test of model validity, or even indicate that it is 

necessary to establish “validity” in such a manner. (Also, it might be pointed out that 

because accident and particularly fatality counts are small for both actual and predicted 

values, absolute differences are small, but large in percentage terms. This makes validity 

assessment under any conditions problematic.) 

The risk assessment model is intended as an analytic methodology to provide guidance in 

allocating the scare funds available to upgrade warning levels in a manner as to reduce 
30 

the most risk per unit of funds. The relative scaling of risk among a group of candidate 

crossings may be all that is needed; and not an absolute value which tracks the historical 

account, provided that the model does not exhibit a bias of over- or under-predicting one 

or several crossings among the 27. 

What the risk model lacks is an adjustment based on the actual experience. A number of 

improvements have been made to crossings over the period, including the introduction of 

some active warning device systems. The crossings are also equipped with special signs 

warning “high-speed trains,” and, in some cases, limited storage space for cars between 

the tracks and Route 9J. An historical adjustment makes allowances for difficult-toquantify crossing characteristics like road and track geometry, sight distance, distractions, 

signage, and sidings. And over an 18-year period, the model could not account for 

differences in driver or train operating characteristics. These changes essentially shift the 

parameter values of the model. Without the historical adjustment, the model prediction 

should not be expected to equal the actual experience, particularly for a small group of 

crossings such as the 27 of this application. Yet, the model still can provide guidance in 

allocating funds among the crossings by upgrading warnings based on relative risk. 

5.6 PROPOSED HIGH-SPEED SECTION 

It has been proposed that the maximum train speed should be increased from 110 mph to 

125 mph along a 15-mile section of this corridor, from Stuyvesant to Teller. Figure 5-8 

shows the location of the nine crossings included within this section of the corridor. Stuyvesant Hook Boat Walsh’s Green Scott Hamilton Printing Staat’s Ablee’s Teller

North

Figure 5-8. Detail of Proposed High-Speed Section 

In addition, one crossing outside of the high-speed section currently at 50 mph (Broad 

Street) will be increased to 60 mph. 

Figure 5-9 shows the risk at these crossings if the speed were increased without changing 

the current warning systems or crossing configurations. As could be expected, the risk 

increases at each crossing, although the increase is relatively modest. 
31 

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Manitou

King’s Dock

Bank St.

Pirate Canoe

Captains 3

Private Rd.

Pok Yacht

River Rd.

Tivoli Dock

Clermont

Private Rd

Cheviot

Dock Rd.

Oak Hill

Hallenbeck

Broad St.Ferry Rd.

Ice House

Stuyvesant

Hook Boat

Walsh’s

Green Av

Scott St.

Hamilton Printing

Staat’s

Ablee’s

Teller's

Risk (Fatalities/Year)

Figure 5-9. Risk at Each Crossing with Proposed Speed Increases 

According to the current guidelines for high-speed train operations, these nine 125-mph 

crossings would have to meet the stringent requirements set out in Table 1-1. There are 

no currently available systems that satisfy these requirements, so the crossings would 

need to be either grade-separated or closed. If the risk at these nine crossings is reduced 

to zero, the total predicted risk on the 27 crossing section would become 0.23 fatalities 

per year. The cost of such changes would be high. However, since the vehicle traffic at 

the Hamilton Printing crossing is 250 vehicles per day, and Green Avenue and Staat’s 

Island each average about 50 vehicles per day, closing is not an acceptable option and 

grade separation would be needed, at a total cost of $3 to $9 million. As discussed in 

Section 4.3, estimating the cost for the remaining six crossings is difficult, but the total 

cost could increase substantially. 

5.7 SIX HIGHEST-RISK CROSSINGS 

The analysis in Section 5.4 highlighted the six crossings that displayed the highest risk 

regardless of whether or not total risk or risk to either the train or highway vehicle is 

considered. This section further examines these crossings. 

Figure 5-10 compares the risk at these six crossings to that at all of the remaining 

crossings. These six alone account for significantly more than half the total risk on the 

entire 96-mile section studied. Figures 5-11 and 5-12 show that this is also true when 

considering risk only on the train or the highway vehicle. 
32 

0.00

0.04

0.08

0.12

0.16

0.20

6 Riskiest Other 21

Risk (Fatalities/Year)

Figure 5-10. Comparison of Total Risk: 6 Highest-Risk Crossings vs. Remaining 21 

0.000

0.005

0.010

0.015

0.020

0.025

6 Riskiest Other 21

Risk (Fatalities/Year)

Figure 5-11. Comparison of Risk on Train Only: 6 Highest-Risk Crossings vs. 

Remaining 21 
33 

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

6 Riskiest Other 21

Risk (Fatalities/Year)

Figure 5-12. Comparison of Risk on Highway Vehicle Only: 6 Highest-Risk 

Crossings vs. Remaining 21 

Table 5-2 shows some of the details of these six crossings. Only two of these six are in 

the high-speed section, and half of the crossings have gates. Also, four of these six are 

private crossings.

Table 5.2 Details of the 6 Highest-Risk Crossings 

Crossing Private/Public Current Warning Current Speed Proposed Speed 

Pirate Private Passive 90 mph 90 mph 

Hamilton Private Flashing Lights 110 mph 125 mph 

Hook Private Passive 110 mph 125 mph 

Bank Public 2-Quad Gates 80 mph 80 mph 

Manitou Public 2-Quad Gates 80 mph 80 mph 

Captains 3 Private 2-Quad Gates 90 mph 90 mph 

Based on the data in Section 4, if the train speed were increased as proposed, and each of 

these crossings were improved so that the crossings that are currently passive or have 

only flashing lights had two-quadrant gates instead, and the currently gated crossing 

became four-quadrant gates, the total risk on the entire section examined would be 

reduced to 0.23 fatalities per year. The cost of these upgrades is generally in the 

$125,000 to $250,000 range, for a total of $750, 000 to $1.5 million. 

Figure 5-13 shows the cumulative total risk with the increased speed and the 

improvements described above. Comparing this with Figure 5-4, the baseline case, the 
34 

bands on the bars are much more uniform, with no prominent wide bands of high risk as 

before. 

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Manitou

King’s Dock

Bank St.

Pirate Canoe

Captains 3

Private Rd.

Pok Yacht

River Rd.

Tivoli Dock

Clermont

Private Rd

Cheviot

Dock Rd.

Oak Hill

Hallenbeck

Broad St.Ferry Rd.

Ice House

Stuyvesant

Hook Boat

Walsh’s

Green Av

Scott St.

Hamilton Printing

Staat’s

Ablee’s

Teller's

Risk (Fatalities/Year)

Figure 5-13. Cumulative Total Risk with Increased Train Speed and Six Improved 

Crossings 

5.8 COMPARISON OF RISK REDUCTION STRATEGIES 

Figure 5-14 compares the baseline risk, the high-speed proposal, the risk following 

application of the current guidelines, and the risk if the six riskiest crossings are 

improved. As can be seen, similar levels of risk result from both improvement schemes. 

The costs are quite different, however. The high-end estimate to address the six high-risk 

crossings is still only half the lowest estimate for following the guidelines for the 125-

mph crossings. Table 5-3 summarizes these results. 

Table 5-3. Summary of Results 

Strategy Improvements Cost Risk at 125 mph 

Eliminate All HighSpeed Crossings 3 Grade Separations 

6 Closures 

$3-9 Million + 0.23 fatalities/year 

Improve Six Highest 

Risk Crossings 

3 Upgrades to 2-Quad 

3 Upgrades to 4-Quad 

$0.75-1.5 Million 0.23 fatalities/year 
35 

Figure 5-14. Comparison of Total Risk for Risk-Reduction Strategies 

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

Current

Situation

Proposed

Higher Train

Speeds

Improvements

at Six

Crossings

Only

Zero Risk on

High Speed

Section

Risk (Fatalities/Year)

Train HV Total
36 
37 

6. CONCLUSIONS AND RECOMMENDATIONS 

6.1 GENERAL COMMENTS 

The risk to train passengers from grade crossing accidents does not increase as 

dramatically with train speed as one might initially expect. Passenger trains are 

significantly crashworthy and newer trains promise even greater levels of protection. 

There have been eight fatalities on passenger trains in grade crossing accidents from 1979 

to 1995. Furthermore, no fatalities resulted from a highway vehicle striking a train in 20 

years of data. Available data suggest that 110 mph is not exceptionally more risky than 

80 mph. Finally, there does not seem to be reason to believe that discontinuous changes 

in risk occur above 110 mph; rather, severity most likely increases smoothly. Risk is 

dominated by exposure – the number of highway vehicles and trains using a given 

crossing. Conventional warning systems significantly reduce the probability of accidents. 

In preliminary tests, new warning systems such as four-quadrant gates and median 

barriers reduce even further the rate at which motor vehicle drivers violate crossing 

devices. 

6.2 SUGGESTED ACTIONS FOR THE EMPIRE CORRIDOR 

Examining the results in Section 5 leads to two conclusions. First, the increase in risk 

due to the proposed increases in train speed can be more than offset by implementing 

fairly standard crossing improvements. Second, the total risk on the section of the 

Corridor examined is driven by several high-risk crossings, some of them located outside 

of the high-speed section. By improving the highest-risk crossings, regardless of the 

maximum train speed, the greatest benefit can be realized. 

6.3 SUGGESTED MODIFICATIONS TO THE FRA GUIDELINES 

The FRA guidelines should offer additional flexibility to employ alternative risk 

reduction options, especially in the 110-125 mph speed regime. As indicated by this 

study, train speed can be increased, while significantly reducing risk, by proper 

application of warning devices and at a cost less than required by application of the FRA 

guidelines. Permission to use alternative options should be based on site-specific, 

objective analyses of the risks, using an approach such as that presented in this study. 

Implementing the guidelines would achieve high levels of grade crossing safety. These 

levels, however, are not immediately attainable. What is needed is an incremental 

migration from the existing practices to ones leading to improved safety. By examining 

the particulars of the specific crossings, including train speed, as well as other factors, one 

can effect the greatest increase in safety, until there are sufficient resources to fully 

implement the guidelines. 
38 

6.4 EFFECTIVENESS OF NEW GRADE CROSSING TREATMENTS 

While the best available data were used to estimate the effectiveness of new types of 

grade crossing treatment, such as four-quadrant gates and median barriers, these data are 

still preliminary. More research is needed into these and other approaches that offer 

potential for risk reduction at lower cost than grade separation. With data on the costs 

and effectiveness of these systems, the methodology presented in this report can be used 

to allocate available crossing improvement funds to effect the greatest possible 

improvement in safety for a given corridor. 
A-1 

APPENDIX. BASELINE STATISTICS ON EXAMINED CROSSINGS AND PREDICTE

RATE

Name AADT Train Mvmt Main Tracks Day Trains Hwy Paved Max Spd Hwy Type Hwy Lanes Gated Public: Manitou 167 68 2 41 1 80 6 2 Bank St. 281 68 2 41 1 80 6 2 River Rd. 143 22 2 13 1 95 6 2 Cheviot 25 22 2 13 1 90 6 2 Dock Rd. 25 22 2 13 1 90 6 2 Broad St. 500 22 2 13 1 50 6 2 Ferry Rd. 25 22 2 13 1 90 6 2 Stuyvesant 25 22 2 13 1 110 6 2 Green Av 50 20 2 13 1 110 6 2 Scott St. 0 20 2 13 1 110 6 2 Staat’s 50 20 2 13 1 110 6 2 Flashing Lights, Private: Hamilton Printing 250 22 2 13 1 110 6 2 Passive Crossings: King’s Dock 2 68 2 41 2 80 6 2 Pirate Canoe 50 68 2 41 1 90 6 2 Private Rd. 5 22 2 13 2 95 6 2 Clermont 2 22 2 13 2 95 6 2 Private Rd 0 22 2 13 2 90 6 2 Oak Hill 0 22 2 13 2 90 6 2 Hallenbeck 10 22 2 13 2 90 6 2 Ice House 5 22 2 13 2 90 6 2 Hook Boat 20 22 2 13 1 110 6 2 Walsh’s 0 22 2 13 2 110 6 2 Ablee’s 2 22 2 13 2 110 6 2 Gated Private Crossings: Captains 3 50 68 2 41 1 90 6 2 Pok Yacht 50 22 2 13 1 90 6 2 Tivoli Dock 30 22 2 13 1 95 6 2 Teller's 2 22 2 13 2 110 6 2 
R-1 

REFERENCES 

1. Luedeke, J.F. “Evaluation of Highway-Railroad Grade Crossing Decision 

Methodologies, Volume 4: Safety of Highway-Railroad Grade Crossings 

on High-Speed Guided Ground Transportation Corridors,” Battelle. 

Unpublished Draft Report to Volpe National Transportation Systems 

Center, August 7, 1997. 

2. Code of Federal Regulations, 49CFR, Subpart 213.347(b), Part II, Track 

Safety Standards; Final Rule. Federal Railroad Administration, 

Department of Transportation, Office of the Federal Register, National 

Archives and Records Administration, Vol. 63, No. 119, June 22, 1998. 

3. Farr, E. “Rail-Highway Crossing Resource Allocation Procedure, User’s 

Guide, Third Edition,” Implementation Package. DOT/FRA/OS-87/10, 

August 1987. 

4. Tyrell, D. Volpe National Transportation Systems Center. Personal 

Communication. Discussion of results of data analysis performed to 

support train car side impact study, June 1997. 

5. Tyrell, D., and J.C. Dorsey. “Consequences of Highway-Railroad atGrade Crossing Collisions.” Project Memorandum, Volpe National 

Transportation Systems Center, October 1995. 

6. Mead, K. “Railroad Safety: Status of Efforts to Improve Railroad 

Crossing Safety,” GAO Report/RCED-95-191, August 1995. 

7. Reilly, M.J., R.H. Hines, and A.E.Tanner. “Rail Safety/Equipment 

Crashworthiness, Volume 1: A Systems Analysis of Injury Minimization 

in Rail Systems,” Report No. FRA/ORD-77/73, I, July 1978. 

8. Worley, P., and A. Mastrangelo. “Sealed Corridor,” Draft Study Results, 

North Carolina Department of Transportation and Norfolk Southern 

Corporation Report, 1997.