U.S. Department 

Of Transportation 

Federal Railroad Administration 

Success Factors in the Reduction of Highway-Rail Grade 

Crossing Incidents from 1994 to 2003 

___________________________________________________________________________ 

Office of Research 

and Development 

Washington, D.C. 20590 

__________________________________________________________________________ 

DOT/FRA/ORD-09/05 This document is available to the 

public through the National Technical Information Service, 

Springfield, VA 22161 

April 2009 

 
ii

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Success Factors in the Reduction of Highway-Rail Grade Crossing Incidents from 1994 to RR-03/EB013 

2003 

6. AUTHOR(S) 

Suzanne Horton, Anya Carroll, Mina Chaudhary, Tashi Ngamdung, Jonathan Mozenter, 

David Skinner 

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8.PERFORMING ORGANIZATION 

U.S. Department of Transportation REPORT NUMBER 

Research and Innovative Technology Administration DOT-VNTSC-FRA-09-01 John A. Volpe National Transportation Systems Center 

Cambridge, MA 02142 

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U.S. Department of Transportation AGENCY REPORT NUMBER 

Federal Railroad Administration

Office of Research and Development DOT/FRA/ORD-09/05 

1120 Vermont Avenue, SW 

Wash ton. DC 20590

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Safety of Highway-Railroad Grade Crossings series 

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

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13. ABSTRACT (Maximum 200 words) 

Between the years 1994 and 2003, incidents at highway-rail grade crossings declined by 41.2 percent. The reasons for this 

decline were unknown. The John A. Volpe National Transportation Systems Center was tasked by the Federal Railroad 

Administration to identify the salient success factors in highway-rail grade crossing incident reduction. The success factors 

were analyzed and investigated using various qualitative and quantitative methods. Ten factors were identified as the most 

influential safety factors. The ten factors are: Commercial Driver Safety, Locomotive Conspicuity, More Reliable Motor 

Vehicles, Crossing Closure and Grade Separation, Sight Lines Clearance, Warning Device Upgrades, the Grade Crossing 

Maintenance Rule, the Section 130 Program, Operation Lifesaver, and Railroad Mergers. Commercial Driver Safety, 

Locomotive Conspicuity, More Reliable Motor Vehicles, Sight Lines Clearance, and the Grade Crossing Maintenance Rule 

were quantitatively analyzed with data from the Railroad Accident Incident Reporting System; they impacted 54 percent of 

the incidents and accounted for 79 percent of the reduction in incidents. 

14. SUBJECT TERMS 15. NUMBER OF PAGES 

Accident reduction, highway-rail intersections, safety 114 

16. PRICE CODE 

17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20. LIMITATION OF ABSTRACT 

 OF REPORT OF THIS PAGE OF ABSTRACT 

 Unclassified Unclassified Unclassified
iv

Acknowledgements 

The U.S. Department of Transportation (USDOT) Federal Railroad Administration 

(FRA) Office of Research and Development sponsored the work leading to this report, 

“Success Factors in the Reduction of Highway-Rail Grade Crossing Incidents from 1994 

to 2003.” The authors would like to extend special appreciation to James Smailes, 

Program Manager of High-Speed Rail and Grade Crossings, Track Research Division, 

FRA, for his guidance and support. 

The authors would also like to acknowledge John Choros and Michael Coltman, 

Structures and Dynamics Division, USDOT John A. Volpe National Transportation 

Systems Center (Volpe Center), as the task managers for this work. This project was 

conducted under the auspices of the Highway-Rail Grade Crossing Safety Research 

Program, Rail and Transit Systems Division, Volpe Center. 
v

Contents 

Illustrations ........................................................................................................................ vi 

Tables................................................................................................................................ vii 

Executive Summary............................................................................................................ 1 

1.0 Introduction................................................................................................................... 4 

1.1 Background............................................................................................................... 4 

1.2 Research Methodology ............................................................................................. 4 

1.3 Summary of Results.................................................................................................. 5 

1.4 Similar Research ....................................................................................................... 7 

2.0 Research Approach ....................................................................................................... 9 

2.1 Databases .................................................................................................................. 9 

2.2 Changes in Database Structures................................................................................ 9 

2.3 Analytic Tools Used ............................................................................................... 10 

3.0 Phase I......................................................................................................................... 11 

3.1 Exposure ................................................................................................................. 11 

3.2 Success Factor Identification .................................................................................. 12 

3.3 Metric Selection...................................................................................................... 14 

4.0 Phase II........................................................................................................................ 15 

4.1 Quantitative Analysis.............................................................................................. 15 

4.2 Summary of Results................................................................................................ 22 

5.0 Phase III ...................................................................................................................... 24 

5.1 Isolating Success Factors ........................................................................................ 24 

5.2 Final Percent Impact and Percent Reduction.......................................................... 31 

5.3 Kendall Partial Rank Correlation Coefficient......................................................... 34 

5.4 The Section 130 Program........................................................................................ 37 

5.5 Operation Lifesaver ................................................................................................ 38 

5.6 Railroad Mergers–A Potential Success Factor ....................................................... 39 

5.7 Summary of Results................................................................................................ 42 

6.0 Findings....................................................................................................................... 44 

6.1 Lessons Learned...................................................................................................... 44 

6.2 Recommendations for Next Steps........................................................................... 45 

7.0 Conclusions................................................................................................................. 49 

Appendix A. FRA Accident/Incident Report................................................................... 51 

Appendix B. Comprehensive List of Success Factors..................................................... 53 

Appendix C. Percent Impact and Percent Reduction....................................................... 61 

Appendix D. Distribution of Incidents by Type of Vehicles........................................... 73 

Appendix E. An Analysis of Severity.............................................................................. 76 

Appendix F. Activation Failure ....................................................................................... 80 

Appendix G. Breakdown of Isolated Success Factors..................................................... 82 

Appendix H. Kendall Partial Rank Correlation for Crossing Closure............................. 90 

Appendix I. Kendall Partial Rank Correlation for Warning Device Upgrades ............... 98 

Appendix J. Railroad Mergers ....................................................................................... 104 

Appendix K. Additional Sources ................................................................................... 113 

Acronyms........................................................................................................................ 119 

Glossary .......................................................................................................................... 120 
vi

Illustrations 

Figure 1: Pareto Chart of Factors Involved in Incidents (Percent Impact)........................ 7 

Figure 2: Pareto Chart of Factors that Contributed to a Reduction of Incidents (Percent 

Reduction)................................................................................................................... 7 

Figure 3: Incident-Injury and Incident-Fatality Rates ...................................................... 14 

Figure 4. Percent of VMT and Number of Incidents for Commercial versus NonCommercial Vehicles................................................................................................ 16 

Figure 5. Incident Rate per Billion VMT for Commercial, Non-Commercial, and All 

Motor Vehicles.......................................................................................................... 17 

Figure 6. Incident Severity to the Train Occupant from Automobile, Truck, or TruckTrailer........................................................................................................................ 18 

Figure 7: Proportion of Incidents by Time of Day .......................................................... 19 

Figure 8: Activation Failure............................................................................................. 20 

Figure 9. Venn Diagram of Factors A,B,C (pre-isolation) .............................................. 25 

Figure 10. Venn Diagram of Isolated Factors and Isolated Interactions ......................... 25 

Figure 11. Incidents Attributed to Commercial Drivers for 1994-2003 .......................... 27 

Figure 12. Incidents Attributed to Locomotive Conspicuity for 1994-2003 ................... 28 

Figure 13. Incidents Attributed to Sight Lines Clearance for 1994-2003........................ 29 

Figure 14. Incidents Attributed to the Grade Crossing Maintenance Rule for 1994-2003

................................................................................................................................... 30 

Figure 15. Incidents Attributed to More Reliable Motor Vehicles for 1994-2003.......... 31 

Figure 16. Pareto Chart of Factors Involved in Incidents (Percent Impact) .................... 33 

Figure 18. Kendall Partial Rank Correlation Coefficients for Crossing Closure ............ 35 

Figure 19. Kendall Partial Rank Correlation Coefficients for Warning Device Upgrades

................................................................................................................................... 37 

Figure 20. Highway-Rail Grade Crossing Incidents 1994-2003 ..................................... 43 
vii

Tables 

Table 1. Final Percent Impact and Percent Reduction....................................................... 2 

Table 2. Phase II Percent Impact and Percent Reduction .................................................. 6 

Table 3. Factor Key for Pareto Charts ............................................................................... 6 

Table 4. Exposure Index and Incident Index ................................................................... 11 

Table 5. Phase II Percent Impact and Percent Reduction ................................................ 23 

Table 6. Equations for Isolated Factors and Their Isolated Interactions ......................... 26 

Table 7. Final Percents Impact and Percents Reduction.................................................. 32 

Table 8. Factor Key for Pareto Charts ............................................................................. 32 

Table 9. Kendall Partial Rank Correlation Coefficient for Crossing Closure ................. 35 

Table 10. Kendall Partial Rank Correlation Coefficient for Warning Device Upgrades 36 

Table 11. Literature Relevant to the Section 130 Program.............................................. 38 

Table 12. Literature Relevant to Operation Lifesaver ..................................................... 38 

Table 13. Class I Railroads at the Beginning and End of the Study Period .................... 40 

Table 14. Yearly Rates of Decline in Public and Private Crossing Incidents.................. 41 

Table 15. Public Crossing Incidents Reported for UP and Major Merger Partners ........ 41 

Table 16. Public Crossing Incidents Reported for BNSF and Major Merger Partners.... 42 
1

Executive Summary 

Between the years 1994 and 2003, incidents at highway-rail grade crossings in the United 

States declined by 41.2 percent. This decline was likely in response to the variety of 

highway-rail grade crossing safety improvement programs that were conducted during 

that time period. The United States Department of Transportation (USDOT) Federal 

Railroad Administration (FRA) Office of Research and Development tasked the USDOT 

Research and Innovative Technology Administration John A. Volpe National 

Transportation Systems Center (Volpe Center) with determining the most influential 

safety factors responsible for the reduction of incidents from 1994 to 2003. 

During the first phase of the project, the research team began to identify all of the 

possible factors that may have influenced safety at highway-rail grade crossings during 

the study period. This was done through extensive literature reviews and group 

discussions. The team then categorized the comprehensive list of success factors by the 

projected impact on incident reduction and perceived difficulty to analyze. At the 

conclusion of Phase I, the factors that rated a high projected impact were selected for 

further analysis. Those factors were Commercial Driver Safety, Locomotive 

Conspicuity, More Reliable Motor Vehicles, Sight Lines Clearance, Grade Crossing 

Maintenance Rule, Crossing Closure and Grade Separation, Warning Device Upgrades, 

Traffic Signal Preemption, Operation Lifesaver, and the Section 130 Program. 

The second phase of the project was a quantitative analysis of the factors that were rated 

easy or moderate for perceived difficulty to analyze (Commercial Driver Safety, 

Locomotive Conspicuity, More Reliable Motor Vehicles, Sight Lines Clearance, Grade 

Crossing Maintenance Rule, Crossing Closure and Grade Separation, Warning Device 

Upgrades, and Traffic Signal Preemption). The research team used two metrics to 

determine each factor’s contribution to incident reduction. The percent impact is the 

percentage of incidents, from 1994 to 2003, that can be attributed to behaviors that the 

factor was attempting to change. The percent reduction is the percentage of incidents 

reduced, from 1994 to 2003, that can be attributed to the safety countermeasures for a 

factor. At the conclusion of project’s phase, the team elected to pursue seven of the eight 

quantitatively analyzed factors in the next phase. The eighth, Traffic Signal Preemption, 

did not reflect a strong influence on reducing highway-rail grade crossing incidents. 

The quantitative analysis in the second phase of this study was based on data available 

from the Railroad Accident Incident Reporting System–Highway-Rail Grade Crossing 

(RAIRS Grade Crossing) database. Because the data fields from the database were used 

to categorize the incidents by success factor, one incident could be assigned to more than 

one factor. This resulted in an overestimation of the factor effects and an overlap of 

incidents among factors. Phase III of the study focused on isolating the effects of each 

success factor, where possible, and analyzing the factors that were labeled difficult or 

very difficult to analyze in Phase I. 

The factor isolation was applied to five of the seven remaining factors that were analyzed 

in Phase II (Commercial Driver Safety, Locomotive Conspicuity, More Reliable Motor 
2

Vehicles, Sight Lines Clearance, and Grade Crossing Maintenance Rule). Each incident 

was assigned to a single factor, some combination or interaction of factors, or a category 

of other factors. The other factors category included the identified factors that were not 

able to be isolated. The percent impact and percent reduction were recalculated to more 

accurately reflect the influence that each factor had on the reduction in incidents. The 

cumulative percent impact was 55 percent, and the cumulative percent reduction was 80 

percent. 

Table 1: Final Percent Impact and Percent Reduction 

Factor Percent Impact Percent Reduction

Commercial Driver Safety 21.8% 34.6% 

Locomotive Conspicuity 15.0% 13.6% 

Sight Lines Clearance 2.6% 3.6% 

Grade Crossing Maintenance Rule 1.1% 3.1% 

More Reliable Motor Vehicles 1.9% 3.1% 

Combined Interactions 12.8% 21.9% 

Other Factors 44.7% 20.0% 

The two factors from phase II that could not be isolated were Crossing Closure and Grade 

Separation and Warning Device Upgrades. The team tested for correlation between the 

number of closed, separated, and upgraded crossings and the number of incidents. The 

test revealed a positive correlation, indicating that closing, separating, or upgrading 

crossings affected the frequency of incidents. 

Two of the factors identified in Phase I were rated very difficult to analyze. The 

researchers collected little data on Operation Lifesaver and the Section 130 Program. 

Both are broad reaching programs that encompass the effects of the other identified 

success factors. However, related studies and reports have been published, and these 

served as the basis for a qualitative analysis of these two factors. The qualitative analysis 

unveiled positive impacts from both factors on incident reduction. 

Data mining efforts during the course of the study revealed an additional potential 

success factor. Railroad mergers appeared to have an impact on safety along the rail 

lines. As major mergers occurred in the mid-1990s, railroad operations expanded, but the 

number of incidents dropped dramatically. This indicated some safety benefit or 

efficiency of operation as a result of the merger activity. 

The analyses conducted in this study identified ten success factors in highway-rail grade 

crossing incident reduction. These 10 factors can account for the majority of the 

reduction in incidents. The 10 success factors are: 

• Commercial Driver Safety 

• Locomotive Conspicuity 

• More Reliable Motor Vehicles 

• Sight Lines Clearance 

• Grade Crossing Maintenance Rule 

• Crossing Closure and Grade Separation 
3

• Warning Device Upgrades 

• Operation Lifesaver 

• The Section 130 Program 

• Railroad Mergers 

Of the factors that were quantitatively analyzed, Commercial Driver Safety and 

Locomotive Conspicuity were responsible for the largest reductions in incidents from 

1994 to 2003. 
4

1.0 Introduction 

1.1 Background 

In 1994, the USDOT’s Rail-Highway Crossing Safety Action Plan [1] set a goal to reduce 

incidents and fatalities nationwide by at least 50 percent over 10 years. From 1994 to 

2003, incidents between trains and highway-users at highway-rail grade crossings were 

reduced by 41.2 percent, from 4,979 to 2,924. Fatalities during the same time period 

were reduced by 48 percent, from 617 to 324. The varied efforts to improve safety 

yielded positive results. During the April 2003 meeting of the National Academy of 

Sciences Transportation Research Board’s Committee for Review of the FRA’s Research 

and Development Program, the Committee requested that the FRA review the incident 

statistics for the Action Plan time period (1994 – 2003) and identify the salient success 

factors for the reduction in those incidents. Success factors are the safety initiatives that 

were the most successful in reducing incidents at highway-rail grade crossings during the 

years 1994 through 2003. 

FRA tasked the Volpe Center to determine which success factors had the greatest 

influence on highway-rail grade crossing safety during the period 1994-2003. It is 

important to know what factors yielded the greatest reduction in grade crossing incidents. 

This enables future initiatives to be planned to maximize safety. 

1.2 Research Methodology 

The approach to this problem involved three phases of analysis. Phase I was a qualitative 

screening of information. In this phase, brainstorming, literature reviews and data mining 

were used to develop a comprehensive list of potential success factors. The projected 

impact of the factors on incident reduction was used to identify the major contributors to 

incident reduction. 

Phase II was an analysis of the top success factors. This phase was a preliminary analysis 

of the incident data. The team used two metrics to determine the benefit of each success 

factor. The percent impact is the percentage of incidents, from 1994 to 2003, that can be 

attributed to behaviors that the factor was attempting to change. The percent reduction is 

the percentage of incidents reduced, from 1994 to 2003, that can be attributed to the 

safety countermeasures for a factor. The percentages were used to determine which 

success factors should be further analyzed. The database used for this analysis may have 

attributed grade crossing incidents to multiple factors. This overlap of incidents may 

have resulted in inflated impact and reduction percentages for the factors. Isolating the 

factors was the focus of Phase III. 

Phase III involved addressing the overestimation of the factors’ effects by isolating each 

factor and the interactions among factors. It was important to assign each incident, from 

1994 to 2003, to one of the three following categories: a single factor, a combination of 

factors, or no identified factor. This method validated that the factors found were indeed 

success factors for incident reduction. The percent impact and percent reduction were 
5

refined to more accurately reflect the effect of selected factors on incident reduction. The 

limitations of the data necessitated that other factors be analyzed using other methods. 

For some of the factors that required a different methodology, a measure of correlation 

between the factor and the reduction in incidents was performed. Other factors could 

only be analyzed qualitatively. 

1.3 Summary of Results 

The results of each project phase fed into the analyses conducted in subsequent phases. 

At the end of Phase I, the projected impact on incident reduction filtered out the ten most 

likely success factors. Eight were analyzed quantitatively in Phase II: Commercial 

Driver Safety, Locomotive Conspicuity, More Reliable Motor Vehicles, Sight Lines 

Clearance, Grade Crossing Maintenance Rule, Crossing Closure and Grade Separation, 

Warning Device Upgrades, and Traffic Signal Preemption. Two were analyzed 

qualitatively in Phase III: Operation Lifesaver and the Section 130 Program. Additional 

research revealed another potential success factor, Railroad Mergers. 

Table 2 lists the percent impact and percent reduction of the eight factors analyzed in 

Phase II are listed in Table 2. Two numbers are associated with Commercial Driver 

Safety and the Grade Crossing Maintenance Rule because database changes occurred in 

1997 that affected the analyses of these factors. On the basis of these numbers, Traffic 

Signal Preemption was not pursued in Phase III. 
6

Table 2. Phase II Percent Impact and Percent Reduction 

Factor % Impact % Reduction 

Commercial Driver Safety 30.04/26.25 52.99/51.75 

Crossing Closure and Grade 4.73 16.22 

Separation 

Grade Crossing 2.23/3.41 3.07/13.92 

Maintenance Rule 

Locomotive Conspicuity 24.7 30.3 

More Reliable Motor 7.54 11.19 

Vehicles 

Sight Lines Clearance 5.13 8.81 

Traffic Signal Preemption 1.82 0 

Warning Device Upgrades 3.01 8.25 

In Phase III, the effects of five success factors were isolated. The other five factors were 

unable to be isolated and were analyzed separately. These analyses indicated that they 

also contributed to the reduction in incidents. The five factors that were analyzed 

separately were Crossing Closure and Grade Separation, Warning Device Upgrades, 

Operation Lifesaver, the Section 130 Program, and Railroad Mergers. 

The five isolated factors were attributed a percent impact and reduction. The Pareto 

charts, shown below in Figure 1 and Figure 2, illustrate the results. The key is found in 

Table 3. The five factors and the interactions between them impacted 54 percent of the 

incidents from 1994-2003. Nearly 80 percent of the reduction in incidents, from 1994-

2003, can be attributed to the five selected factors or interaction of those factors. 

Table 3: Factor Key for Pareto Charts 

Factor Type Description* 

A Unidentified Factors 

B Commercial Driver Safety 

C Locomotive Conspicuity 

D Sight Lines Clearance 

E Grade Crossing Maintenance Rule 

F More Reliable Motor Vehicles 

G Combined Interactions 

*Detailed description of factors available in the section on Phase I
7

100%

54% 55% 52%

22%

37%

50%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

BCGDF E

Factor

Percent of Total

A

Figure 1: Pareto Chart of Factors Involved in Incidents (Percent Impact) 

57%

100%

80% 77% 70% 74%

35%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

BGCDEFA

Factors

Percent of Total

Figure 2: Pareto Chart of Factors that Contributed to a Reduction of Incidents 

(Percent Reduction) 

1.4 Similar Research 

A study on a similar topic area was conducted by Mok and Savage entitled “Why Has 

Safety Improved at Highway-Rail Grade Crossings?” [2]. The Mok and Savage study 

identifies and investigates possible factors that are influential in the reduction of incidents 

and fatalities at grade crossings. The focus of the study was the reduction in grade 

crossing incidents and fatalities from 1975 to 2001. The methodology used was a 

negative binomial regression. The greatest influence on safety was attributed to highway 

safety improvements such as drunk driving, enforcement, and improved emergency 
8

response. Other influential factors identified in the study were warning device upgrades, 

Operation Lifesaver, locomotive alerting lights and crossing closure. 

The FRA research described in this report attempts to answer the same question of what 

is responsible for the reduction in grade crossing incidents. The FRA research study 

period was from 1994 to 2003. This is the period covered by the 1994 Rail-Highway 

Crossing Safety Action Plan [1]. By 1994, many of the major highway safety 

improvements had been realized and did not continue to yield the same safety benefits. 

From 1994 to 2003, many rules and safety initiatives specific to rail and grade crossing 

safety were enacted. In the FRA study, only incidents were examined. The decision to 

use incidents as the metric for safety is described in Section 3.3. 

Similar to the Mok and Savage study, the FRA study conducted background literature 

reviews and research on grade crossing safety. The factors considered in both studies 

were Warning Device Upgrades, Operation Lifesaver, Locomotive Conspicuity and 

Crossing Closure. The main difference between the two studies is the FRA research does 

not attempt to develop a regression model for incidents. It makes the assumption that 

certain factors can be approximated by data fields in the RAIRS Grade Crossing 

database. The data fields were examined for reductions in incidents and those reductions 

were attributed to a particular factor. For example, for the factor Commercial Driver 

Safety, incidents from the data field “type of vehicle” were analyzed from 1994 to 2003. 

Factors that were more complex and could potentially encompass the effects of other 

identified factors that were analyzed qualitatively. 

The results of the Mok and Savage study differed from the FRA study. The regression 

model approach derived weighted coefficients for each factor. The coefficients attributed 

a portion of the reduction in incidents to the factors. The FRA study examined each 

factor individually and tried to isolate each factor’s effects. Although the methodologies 

and results differed, both studies identified Warning Device Upgrades, Operation 

Lifesaver, Locomotive Conspicuity and Crossing Closure as success factors. 

 

 
9

2.0 Research Approach 

2.1 Databases 

The researchers identified six databases as resources for the analyses of grade crossing 

incidents from the paper, “Documenting the Strength and Weakness of Databases for Use 

in Analyzing Highway-Railroad Grade Crossings” [3]. These databases are as follows: 

• National Highway-Rail Crossing Inventory (Crossing Inventory) 

• Highway-Railroad Crossing Inventory Geographical Information System 

(GIS) Layer 

• RAIRS Grade Crossing 

• RAIRS Rail Equipment 

• Fatal Accident Reporting System (FARS) 

• National Automotive Sampling System General Estimate System 

Of the six databases identified, the team only used two in the analyses of grade crossing 

incidents for this project: the Crossing Inventory and the RAIRS Grade Crossing 

databases. These two were selected because others were limited in the data available for 

grade crossing incidents or they did not contain complete data for the 10-year study 

period. 

FRA developed the Crossing Inventory database in 1970 and the FRA Office of Safety 

manages the database. It contains all U.S. public and private highway-rail grade 

crossings, with detailed current and historical information on individual crossings. The 

railroad submits the crossing data and the States submit the highway information 

voluntarily. Crossing data should be submitted if a crossing is closed, opened, or the 

level of protection is changed. However, changes are voluntary, which could lead to 

outdated or incomplete data, as noted in the paper, “Grade Crossings of Northeastern 

Illinois: An Analysis of the FRA Grade Crossing and Grade Crossing Accident 

Inventories, and an Analysis of the Potential Impacts from the Horn Sounding 

Requirement of the Swift Rail Development Act” [4]. 

FRA developed the RAIRS Grade Crossing database in 1975 and the FRA Office of 

Safety manages the database. It contains all incidents involving a highway user and 

railroad equipment. Information on the train involved is available, as well as detailed 

information on crossing characteristics. Unlike the RAIRS Rail Equipment database, no 

dollar threshold exists for the reporting of an incident. The railroad reports all incidents 

that involve railroad on-track equipment and highway users of public and private 

highway-rail grade crossings to FRA. A copy of the current FRA Highway-Rail Grade 

Crossing Accident Incident Report Form is available in Appendix A. 

2.2 Changes in Database Structures 

The Crossing Inventory database underwent changes in the 10-year study period. The 

most notable change occurred in 1997 when the database was expanded to be Y2K 
10

compliant. A narrative field was added to the database, and additional information was 

collected on whistle bans and passenger trains. In addition in 2000, the category, fourquadrant gate warning devices, was added to the warning device field. 

 

FRA added new fields and modified some of the existing fields in the RAIRS Grade 

Crossing database. Two new fields, driver gender and whistle ban, were added in 1997. 

The modified data fields that may have affected the analyses are type of vehicles, 

position, railroad equipment, type of equipment, and signal. All modifications to the 

above-mentioned fields were undertaken in 1997. Pickup truck, van, and other motor 

vehicles were added to the type of vehicle field; trapped was added to the position field; 

commuter train, single train, cut of cars, and maintenance/inspection equipment were 

added to the type of equipment field; alleged and confirmed warning signal greater than 

60 seconds, less than 20 seconds, and confirmed no warning signal were added to the 

signal field. 

2.3 Analytic Tools Used 

The researchers used two different data mining tools, Accident Data Analytical 

Prospective Tool (ADAPT_X) Version 3.6 and SAS, Version 8.02, in the analyses of 

grade crossing incident data. They both utilized the same database, the RAIRS Grade 

Crossing database, but used different techniques to query the incident data. 

Kenny Williams, formerly of the Structures and Dynamics Division of the Volpe Center, 

developed ADAPT_X. It is a front-end Microsoft Excel program that uses raw data 

extracted from the RAIRS Grade Crossing database. The data is filtered using selected 

criteria. The advantages of using this tool are that it is user friendly and it accounts for 

the multiple entries of a single incident in the database. Multiple entries may occur when 

two railroads are involved in the same incident, and both file reports with FRA. Instead 

of only one incident being listed, two incidents are recorded. In these cases, the integrity 

of the data is compromised. 

SAS is a statistical software tool designed for data access, transformation, and reporting. 

It includes ready-to-use programs for data manipulation, information storage and 

retrieval, descriptive statistics, and report writing. The advantage of using SAS is that it 

comes with ready-to-use statistical programs that provide extensive statistical 

capabilities. 

Both analytic tools were used and produced similar results. The number of incidents 

generated by SAS was slightly higher than the number of incidents generated by 

ADAPT_X. The team expected this result because ADAPT_X eliminates the multiple 

entries of a single incident. Because ADAPT_X is more reliable and easier to use, it was 

decided that ADAPT_X would be the primary tool for the analyses of grade crossing 

incidents. 
11

3.0 Phase I 

When the Transportation Research Board (TRB) Oversight Committee posed the 

question, “Why was there an incident reduction from 1994 to 2003?,” no prior research 

had been conducted in this area. During this period, multiple safety countermeasures, 

laws, and programs could have affected driver behavior at highway-rail grade crossings. 

Societal changes and technological evolution could also have been influential. It was a 

multifaceted real world problem that needed a creative solution. 

3.1 Exposure 

The first approach was to examine the exposure levels at highway-rail grade crossings 

during the study period. This was to ensure that exposure levels were not the reason for 

the reduction in highway-rail grade crossing incidents. An ideal way to measure 

exposure at grade crossings would be to take the product of the average annual daily 

traffic (AADT) and trains per day for each crossing. The AADT and trains per day in the 

Crossing Inventory, however, are not updated every year and are therefore not reliable. 

Instead, the overall national vehicle miles traveled (VMT), train miles traveled (TMT), 

and number of crossings was used to calculate an exposure index for each year. Table 4 

shows VMT, TMT, number of crossings, exposure index, overall number of incidents, 

and incident index for the years 1994 through 2003. The exposure index is equal to the 

normalized product of VMT, TMT, and the number of crossings. The incident index is 

equal to the overall number of incidents divided by the exposure index. 

Table 4: Exposure Index and Incident Index 

Exposure Index 

 Vehicles 

Miles 

Traveled 

(Millions) 

Train Miles 

Traveled 

(Millions) 

Number of 

Crossings 

Exposure 

Index 

Overall 

Number of 

Incidents 

Incident 

Index 

1994 2,359,984 655.1 272724 4.2164 4979 1180.9

1995 2,422,775 669.8 268454 4.3564 4633 1098.8

1996 2,482,202 670.9 265695 4.4246 4257 1009.6

1997 2,552,233 676.7 262268 4.5296 3863 916.2 

1998 2,628,148 682.9 259240 4.6527 3508 832.0 

1999 2,690,241 712.5 257565 4.9370 3489 827.5 

2000 2,746,925 722.9 254288 5.0495 3502 830.6 

2001 2,797,339 711.6 252491 5.0261 3237 767.7 

2002 2,855,756 728.9 250243 5.2090 3077 729.8 

2003 2,890,893 748.6 242734 5.2531 2924 556.6 

Percent 

Change 

+22.5% +14.3% -11.0% +24.6% -41.2% -52.9%

Sources FHWA, Office 

of Highway 

Policy 

Information 

Highway 

Statistics 

(1994-2003) [5] 

Office of the 

Inspector 

General (2004). 

Audit of the 

Highway -Rail 

Grade Crossing 

Safety Program, 

[6]

FRA RailHighway Crossing 

Inventory Bulletin

(1994-1996) [7], 

FRA Railroad 

Safety Statistics 

Annual Report

(1997-2003) [8]
12

3.2 Success Factor Identification 

The research methodology for this project began with trying to identify all possible 

highway-rail grade crossing incident reduction success factors. The comprehensive list 

of success factors was derived from brainstorming and literature reviews. The team used 

their knowledge and experience in grade crossing research to discuss possible factors. 

Extensive research was conducted via the World Wide Web, published reports, State and 

Federal regulations, and other documentation. This helped identify any factors that were 

overlooked and refine the comprehensive list of potential success factors. 

The next step in this phase was to select the most probable success factors. The 

comprehensive list was organized in a matrix (see Appendix B) that contained various 

pieces of information necessary to make the selection. The team reviewed the data 

sources available for each factor, the methods of analysis, the projected impact on 

incident reduction, and the perceived difficulty of analysis. The projected impact was 

based on a scale of low, medium, or high. The factors with a high projected impact were 

believed to have affected incident reduction from 1994 to 2003. The perceived difficulty 

of analysis was based on a scale of easy, medium, and very difficult. The factors that had 

a high projected impact and were easy or medium to analyze were selected for 

quantitative analysis in Phase II. The researchers used the Crossing Inventory database 

and the RAIRS Grade Crossing database for this quantitative analysis. Other factors with 

a high projected impact and a difficult or very difficult level of analysis were evaluated 

differently. 

The team selected 10 success factors for further analysis in Phase II. The following lists 

a description of each, in no particular order. The matrix in Appendix B includes 

descriptions of all possible success factors. Operation Lifesaver and the Federal 

Highway Administration’s (FHWA) Section 130 Program will be analyzed qualitatively 

because a thorough analysis would require an intensive data collection process that is 

beyond the scope of this study. The other eight factors will be analyzed quantitatively. 

Commercial Driver Safety. During the period 1994-2003, a greater emphasis was 

put on commercial driver safety. The Motor Carrier Safety Improvement Act 

established the Federal Motor Carrier Safety Administration (FMCSA) and 

emphasized commercial vehicle safety. The primary mission of FMCSA is to 

reduce crashes, injuries, and fatalities involving large trucks and buses. In 

October 1999, the law on Commercial Driver Disqualification (Title 69 Code of 

Federal Regulations (CFR) 48104) stated that commercial drivers convicted of 

violating the highway-rail grade crossing warning devices would have their 

Commercial Drivers’ Licenses (CDL) suspended. This factor relates to the 

commercial vehicle involvement field within the RAIRS Grade Crossing 

database. 

Locomotive Conspicuity. Making locomotives more conspicuous aids drivers in 

not only seeing an oncoming train, but judging its distance and speed. A final 

rule, Locomotive Safety Standards (49 CFR 229), published in March 1996, 
13

effective December 1997, stated that all locomotives exceeding 20 mph over the 

crossing must have auxiliary alerting lights in addition to the headlight. This 

factor relates to the visibility, railroad equipment, and type of accident fields 

within the RAIRS Grade Crossing database. 

More Reliable Motor Vehicles. During the period of study, automobiles were 

improved to be safer and more reliable. A more reliable vehicle reduces the 

likelihood of breaking down or stalling on the tracks and subsequently being 

struck by an oncoming train. This factor relates to the stalled vehicle field within 

the RAIRS Grade Crossing database. 

Sight Lines Clearance. The clearing of vegetation and removal of obstructions 

surrounding a grade crossing enables highway-users to observe the tracks and a 

possible oncoming train at farther distances from the crossing. Adequate sight 

distance allows highway-users to stop safely and the risk of collision with an 

unexpected train is reduced. This factor relates to the obstruction of track view 

field within the RAIRS Grade Crossing database. 

Grade Crossing Maintenance Rule. In 1995, the Final Rule on Grade Crossing 

Signal System Safety (49 CFR 234) was issued. This rule stated that railroads 

must implement specific maintenance, inspection, and testing requirements for 

active crossing warning systems. The regular maintenance and inspection would 

reduce the risk of warning device malfunction. This factor relates to the active 

warning device malfunction fields within the RAIRS Grade Crossing database. 

Crossing Closure and Grade Separation. Crossing closures may have impacted 

the safety of highway-rail grade crossings over the Action Plan period (1994 to 

2003). In 1991, the FRA Administrator recommended closure for 25 percent of 

all crossings. Closures and grade separations reduce the risk of a collision to 

nearly zero because the vehicle and train paths no longer intercept at that location. 

This factor relates to the update reason and type and position fields in the 

Crossing Inventory database. 

Warning Device Upgrades. When crossing warning devices are upgraded to 

devices with a higher effectiveness value, the risk of a collision at the crossing is 

reduced. This factor relates to the upgrade reason and warning device code fields 

from the Crossing Inventory database. 

Traffic Signal Preemption. Traffic signal preemption is recommended for 

highway-rail crossings equipped with active warning devices and with a 

signalized highway intersection within 200 feet. The normal sequence of traffic 

control signal indication at a nearby highway intersection is preempted upon the 

approach of trains. The goal is to avoid entrapment of vehicles on the highwayrail crossing by conflicting highway traffic control signals and the highway-rail 

grade crossing active warning devices. The factor relates to the traffic light 

interconnection/preemption field in the Crossing Inventory database. 
14

Operation Lifesaver. Operation Lifesaver is an education and awareness program 

dedicated to ending tragic collisions, fatalities, and injuries at highway-rail grade 

crossings and on railroad rights of way. 

 

Section 130 Program. Highway funds are appropriated by Congress under 

Section 130, Title 23 of the United States Code for improvements to the safety of 

highway-rail grade crossings. The funds are appropriated by State and each State 

has its own crossing improvement plan. The Section 130 Program overlaps with 

other success factors since this money is used to close, separate, and upgrade 

crossings. 

3.3 Metric Selection 

Before conducting quantitative analyses on the selected factors, the team chose the 

appropriate metric to measure the improvement in crossing safety. In this study, 

incidents were found to be the appropriate metric. Figure 3 shows a graph of the injury 

per incident rate and fatality per incident rate for each year of study. From 1994 to 2003, 

the rates remained constant even though incident rates were decreasing. The proportion 

of injury-incidents and fatality-incidents stayed the same over the course of the study. 

Incidents, injuries, and fatalities were all declining at the same rate. The reduction in 

incidents did not vary by incident severity. 

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

0.450

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Year

Inj&Fat/Incident

Inj/Incident Fat/Incident Linear (Inj/Incident) Linear (Fat/Incident)

Figure 3: Incident-Injury and Incident-Fatality Rates 
15

4.0 Phase II 

Operation Lifesaver and the Section 130 Program were not analyzed quantitatively 

because a thorough analysis would require an intensive data collection process and these 

two factors may encompass other factors. Phase II includes a discussion of a qualitative 

analysis. 

4.1 Quantitative Analysis 

The research team quantitatively analyzed eight success factors. Detailed analysis is 

available in Appendix C. The objective was to rank their effectiveness based on two 

metrics, percent impact and percent reduction. The percent impact is the percentage of 

incidents, from 1994 to 2003, that can be attributed to behaviors that the factor was 

attempting to change. The percent reduction is the percentage of incidents reduced, from 

1994 to 2003, that can be attributed to the safety countermeasures for a factor. For a few 

factors, the team concluded additional quantitative analysis on available data. These 

analyses supported the percent impact and percent reduction results. The following 

analyzes eight factors. Table 5 summarizes the percent impact and percent reduction 

results are included at the end of this section. 

4.1.1 Commercial Driver Safety 

Percent Impact 30.04/26.25, Percent Reduction 52.99/51.75 

For the analyses of commercial driver safety, the researchers used incidents from vehicles 

requiring a CDL (trucks, truck-trailers, buses, and school buses), as identified by the type 

of vehicles field in the RAIRS Grade Crossing database, to calculate the percent impact 

and the percent reduction. Two percentages are provided for each percent impact and 

reduction, because in 1997 two categories, pick-up truck and van, were added to the type 

of vehicle field. These types of vehicles may have been included in either the truck or 

auto category pre-1997. The number of incidents was 1784 in the year 1994, 1182 in the 

year 1997, and 695 in the year 2003. 

Commercial Driver Safety has high values for percent impact and percent reduction. It 

was of interest to further examine the success of this factor. One approach was to 

compare the proportion of VMT between commercial and non-commercial vehicles over 

the 10-year period and determine any effect on the number of incidents. 

Figure 4 shows the percent of VMT and the number of incidents for commercial and noncommercial vehicles. The figure below shows that over the 10-year period, the proportion 

of commercial VMT stayed the same, around 8 percent. Commercial vehicle incidents, 

however, decreased from 1,784 in 1994 to 695 in 2003. Although commercial VMT 

make up about 8 percent of the overall VMT, they were involved in 35.8 percent of 

incidents in 1994 and 23.7 percent of incidents in 2003. 
16

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Percent of VMT

0

500

1000

1500

2000

2500

3000

3500

Number of Incidents

Commercial VMT Non Commercial VMT 

Commercial Vehicle Incidents Non-Commercial Vehicle Incidents

Figure 4. Percent of VMT and Number of Incidents for Commercial versus NonCommercial Vehicles 
17

The research team also compared the rates of incidents for commercial vehicles, noncommercial vehicles and all vehicles. Commercial vehicle and non-commercial vehicle 

incident rates start in 1997 because before this year the categories pickup truck and van 

did not exist. Figure 5 shows the incident rate per one billion VMT for commercial, noncommercial, and all motor vehicles. Commercial vehicles have the highest rate of 

incidents per VMT but they also have the largest reduction. The incident rate for 

commercial vehicles decreased by 47.6 percent, from 5.96 in 1997 to 3.12 in 2003. This 

is compared to a 29.2 percent decrease in the non-commercial vehicle incident rate. 

Examining the amount of commercial vehicle traffic on the roads from 1994 through 

2003 revealed that commercial vehicles were involved in a larger percentage of incidents 

than non-commercial vehicles. The incident rate decrease, however, was more dramatic 

for commercial vehicles than non-commercial vehicles. The results from these analyses 

suggest that Commercial Driver Safety did have a significant impact on the reduction in 

highway-rail crossing incidents. 

0.000

0.500

1.000

1.500

2.000

2.500

3.000

3.500

4.000

4.500

5.000

5.500

6.000

6.500

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Year

Rate per billion miles travelled

All Motor Vehicle Incident Rate Commercial Vehicle Incident Rate

Non Commercial Vehicle Incident Rate

Figure 5. Incident Rate per Billion VMT for Commercial, Non-Commercial, and 

All Motor Vehicles

The reduction in commercial vehicle incidents at grade crossings is of interest because an 

incident involving a commercial vehicle may have serious consequences. Although 

commercial vehicle incidents decreased significantly over the 10-year period, they 

accounted for 23.7 percent of all incidents in 2003. The commercial vehicle incidents 
18

have higher rates of injuries and fatalities to train occupants than non-commercial vehicle 

incidents. Truck, truck-trailer, and automobile incidents comprise about 83 percent of all 

incidents in the study period. (See Appendix D.) The team used automobile, truck, and 

truck-trailer injuries and fatalities obtained from the RAIRS Grade Crossing database to 

compare the severity of incidents involving commercial and non-commercial vehicles. 

(Appendix E provides an analysis of severity.) Figure 6 shows the severity to train 

occupants in terms of injuries, from incidents involving automobiles, trucks, and trucktrailers. 

 

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Year

Severeity (Injuries/Incident)

Auto Truck Truck-Trailer

Figure 6. Incident Severity to the Train Occupant from Automobile, Truck, or 

Truck-Trailer 

As expected, Figure 6 shows that the incidents involving commercial vehicles have 

greater consequences to train occupants than regular automobiles. The reports, PostIncident Aggravating Risk Factors in Highway-Rail Grade Crossing Crashes [9] and 

Assessment of Risks for High-Speed Rail Grade Crossings on the Empire Corridor [10], 

assert that incidents involving commercial vehicles have a greater chance of derailing a 

train. A derailment is a greater danger to train occupants. The spike that occurred in 

1999 resulted from a severe accident and subsequent derailment in Bourbonnais, IL. An 

Amtrak train struck a tractor-trailer and derailed, killing 11 passengers on board. 

4.1.2 Locomotive Conspicuity 

Percent 24.7, Percent Reduction 30.3 

The FRA published the report, Use of Auxiliary External Alerting Devices to Improve 

Locomotive Conspicuity [11], in 1995. It stated that there was a significant difference in 

the effectiveness of locomotive alerting lights between the daylight and the night. The 

lights provided more benefit in the night. Therefore, incidents in which the rail 

equipment struck the highway user at dusk, dawn or dark, from the RAIRS Grade 
19

Crossing database, were used to measure the effects of increased Locomotive 

Conspicuity. Visibility, railroad equipment, and type of accident fields were chosen as 

representatives of this factor. The number of incidents decreased from 1320 in 1994 to 

696 in 2003. 

Figure 7 shows a graph of the proportion of incidents where rail equipment struck the 

highway user by time of day. The proportion of incidents over the study period increased 

for incidents at day and decreased for incidents at dark, dawn or dusk. The decrease in 

proportion of incidents at dark or dusk indicates that safety is improving during the night. 

This lends credit to using the selected data fields for the analysis of Locomotive 

Conspicuity. 

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Year

Proportion of Incident

s

Rail Equipment Struck Highway User at Day 

Rail Equipment Struck Highway User at Dark, Dawn and Dusk

Figure 7: Proportion of Incidents by Time of Day 

4.1.3 Grade Crossing Maintenance Rule 

Percent Impact 2.23/3.41, Percent Reduction 3.07/13.92

The researchers decided that warning signal malfunctions would be classified into three 

categories: excessive warning time, abbreviated warning time, or no warning. The signal 

field in the RAIRS Grade Crossing database identifies incidents that have both alleged 

and confirmed warning device malfunction. There are two percentages for percent impact 

and reduction, one from 1994 to 2003 and another from 1997 to 2003. In 1997, five new 

categories were added to the signal data field. The percent impact was determined from 

the percentages of the total number of incidents that identified a warning device 

malfunction. The percent reduction was determined from the number of fewer incidents 

that identified a warning device malfunction in 2003 versus 1994, or 2003 versus 1997. 

The number of incidents with a warning device malfunction reduced from 204 in 1997 to 

73 in 2003. 
20

To further analyze the effectiveness of the Grade Crossing Maintenance Rule, the 

researchers used a number of activation failures was used to determine the rate at which 

active warning devices fail. Figure 8 shows the activation failure rate. Activation 

failures from 1992 and 1993 were obtained from the report, Highway-Railroad Grade 

Crossing Active Signal Systems Analysis [12]. The researchers obtained activation failure 

data from the years 1998 and 1999 electronically from FRA and the years 2000 through 

2003 were obtained from the FRA Activation Failure records. The FRA was not 

collecting activation failure data during the years 1994 through 1997, hence, the gap in 

the data. The activation failure rate is equal to the number of activation failures times 

100 divided by the total number of active crossings in the Nation. The activation failure 

rate sharply declined after the maintenance rule was implemented. This suggests a safer 

crossing environment after the rule. (See Appendix F for more detailed data.) 

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

1992 1993 1994 1995 1996 1997 1998 &

1999

2000 2001 2002 2003

Year

Per 100 Crossing

Activation Failure Rate

Figure 8: Activation Failure

4.1.4 Crossing Closure and Grade Separation 

Percent Impact 4.73, Percent Reduction 16.22 

When a crossing is closed or grade separated, the risk of a collision between a highway 

user and a train is minimal. Because no further incidents at those crossings are likely to 

occur, it is difficult to derive the effect of crossing closures and grade separations on 

incident reduction between 1994 and 2003. No fields in the RAIRS Grade Crossing 

database could be used for the analyses of Crossing Closure and Grade Separation. A 

new methodology was developed to estimate the impact of closures and grade separations 

on the decrease in incidents over the 10-year period. From 1984 to the year a crossing 

was closed or separated, the accident history was used to obtain an average number of 

incidents per year. The researchers used inferential extrapolation to derive a probable 

number of incidents. The probable number of incidents for a particular year was the 

number of incidents that would have occurred in that year if the crossings closed or 

separated between 1994 and that year had not been closed or separated. The following 

formulas determined the percent impact and the percent reduction: 
21

(1) Percent Impact = (the sum of probable incidents between 1994 and 2003)/(the 

sum of the actual and probable incidents between 1994 and 2003) x 100. 

(2) Percent Reduction = (the change in the probable number of incidents between 

1994 and 2003)/(the change in the sum of the actual and probable incidents 

between 1994 and 2003) x 100. 

4.1.5 Warning Device Upgrades 

Percent Impact 3.01, Percent Reduction 8.25

Warning Device Upgrades posed a similar problem as Crossing Closure and Grade 

Separation. No fields in the RAIRS Grade Crossing database could be used for the 

analysis of Warning Device Upgrades. The method developed for Crossing Closure and 

Grade Separation was modified to evaluate the effectiveness of Warning Device 

Upgrades. All Warning Device Upgrades were categorized into one of three different 

types: passive to flashing lights, passive to gates or flashing lights to gates. Each type of 

upgrade has an effectiveness value associated with it. The effectiveness values were 

obtained from the 1987 Rail-Highway Crossing Resource Allocation Procedure–A User’s 

Guide, Third Edition [13]. 

 

• Passive to Flashing Lights = 0.70 

• Passive to Gates = 0.83 

• Flashing Lights to Gates = 0.69 

The data for Warning Device Upgrades was based on available data from the State of 

Illinois and then extrapolated to obtain national data. For each year from 1994 to 2003, 

an accident rate for each type of warning device was developed. The accident rate was 

equal to the number of incidents at a crossing with a particular warning device divided by 

the total number of crossings with that warning device. The probable number of 

incidents for a particular year was the number of incidents that would have occurred in 

that year if those crossings upgraded between 1994 and that year had not been upgraded. 

The percent impact and percent reduction formulas were the same as used for Crossing 

Closure and Grade Separation. 

4.1.6 More Reliable Motor Vehicles 

Percent Impact 7.54, Percent Reduction 11.19

The position field in the RAIRS Grade Crossing database identifies incidents where the 

highway vehicle was stalled on the tracks at the time of impact. The percent impact for 

More Reliable Motor Vehicles was determined from the percentage of incidents from 

1994 to 2003, in which the highway vehicle was stalled on the crossing. A vehicle can 

stall for a variety of reasons, not all of which are related to the reliability of the motor 

vehicle. Therefore, the effect of this factor may be over inflated because there RAIRS 

Grade Crossing database does not provide additional information on the cause of the stall. 

In the ten years of the study period, 2,824 incidents out of the 37,471 total incidents 

resulted from vehicles being stalled on the crossing. The percent reduction was 

determined by finding the number of fewer incidents attributed to stalled vehicles that 

occurred in 2003 versus 1994. The number of incidents that involved stalled vehicles 

dropped from 412 to 182. 
22

4.1.7 Sight Lines Clearance 

Percent Impact 5.13, Percent Reduction 8.81

The view field in the RAIRS Grade Crossing database describes seven categories of 

visual obstruction at the highway-rail grade crossing at the time of impact: permanent 

structure, standing railroad equipment, passing train, topography, vegetation, highway 

vehicles, and other. The researchers used all seven categories in the analysis except 

passing train and highway vehicles. These two were not included because it is difficult to 

eliminate them through safety countermeasures. The number of incidents decreased from 

294 in 1994 to 113 in 2003. 

4.1.8 Traffic Signal Preemption 

Percent Impact 1.82, Percent Reduction 0.0 

Limited data was available for this factor. Incidents at crossings with warning devices 

connected to highway traffic signals were queried from the RAIRS Grade Crossing 

database. The number of crossings with traffic signal preemption was available from the 

Crossing Inventory database, but it was not possible to distinguish the number of 

crossings equipped with traffic signal preemption by year. Therefore, the effectiveness of 

traffic signal preemption could not be analyzed sufficiently. Another approach was taken 

involving the position of the highway vehicle during an incident at a crossing equipped 

with traffic signal preemption. The position of interest was trapped. A vehicle could 

become trapped on a grade crossing by stopped traffic due to a red traffic signal ahead. 

Traffic signal preemption could reduce the risk of traffic queuing onto a crossing. 

However, no data was available to determine the reason the vehicle became trapped. 

Since the number of preempted crossings makes up less than 2 percent of overall 

crossings, and the percentage of incidents at preempted crossings did not change over the 

10-year period, it was decided not to further pursue this factor. 

4.2 Summary of Results 

Table 5 summarizes the eight success factors with their percent impact and reduction. 

Where two percents are shown in the same cell, the first percent is based on the time span 

1994 to 2003. The second percent is based on the time span 1997 to 2003. These factors 

were affected by changes in the database structure in 1997. Appendix F includes detailed 

data and analysis of each factor. 

The team used the analysis of factors by percent impact and percent reduction to select 

the top factors for further study. Factors with the percent impact and the percent 

reduction below 8 percent were not pursued. Traffic Signal Preemption was the only 

factor not pursued. The RAIRS Grade Crossing database analysis may have attributed 

grade crossing incidents to multiple factors. This overlap of incidents may have resulted 

in inflated impact and reduction percentages for the factors. The research team 

conducted factor isolation in Phase III. 
23

Table 5. Phase II Percent Impact and Percent Reduction 

Factor % Impact % Reduction 

Commercial Driver Safety 30.04/26.25 52.99/51.75 

Crossing Closure and Grade 4.73 16.22 

Separation 

Grade Crossing 2.23/3.41 3.07/13.92 

Maintenance Rule 

Locomotive Conspicuity 24.7 30.3 

More Reliable Motor 7.54 11.19 

Vehicles 

Sight Lines Clearance 5.13 8.81 

Traffic Signal Preemption 1.82 0 

Warning Device Upgrades 3.01 8.25 
24

5.0 Phase III 

5.1 Isolating Success Factors 

In Phase II, concern was that the method of data analysis resulted in an overestimation of 

the effect of the success factors analyzed. This was due to multiple counting of incidents 

and overlaps of incidents among factors. This situation arose because data fields from 

the highway-rail crossing accident reports are used to categorize the incidents by success 

factor and these reports could cite more than one factor per incident. For example, an 

accident report could cite a commercial vehicle involved, the vehicle stalled on the 

tracks, and an obstruction blocked the sight line. This would mean that this incident 

would be attributed to all three of the following factors: Commercial Driver Safety, 

More Reliable Motor Vehicles, and Sight Lines Clearance. In reality, either one factor 

alone or some combination of the three factors was responsible for the incident. Thus, 

Phase III began by trying to isolate the effect of each success factor. It was necessary to 

find a methodology that would take one incident and assign it to a particular success 

factor, a combination of two or more success factors, or a single category of all other 

factors. 

The researchers used inferential extrapolation was used in Phase II to determine how 

many incidents were impacted and reduced from the two success factors: Crossing 

Closure and Grade Separation and Warning Device Upgrades. Because this number of 

incidents was an estimate and could not be obtained directly from the RAIRS Grade 

Crossing database, these two success factors and their interactions were unable to be 

isolated. The Section 130 Program and Operation Lifesaver were two success factors that 

could also not be isolated because they encompassed too many other factors. Additional 

research conducted in Phase III revealed a potential success factor in Railroad Mergers. 

Railroad Mergers data was not obtained directly from the RAIRS Grade Crossing 

database and therefore could not be isolated. The remaining five success factors and their 

combined interactions could be isolated because the number of incidents assigned to 

these success factors came from the RAIRS Grade Crossing database. These five success 

factors were Commercial Driver Safety, Locomotive Conspicuity, Sight Lines Clearance, 

Grade Crossing Maintenance Rule, and More Reliable Motor Vehicles. 

To imagine how they were isolated, the team used a Venn diagram of five factors. The 

Venn diagram showed the five factors and every intersection (or overlap) possible 

between them. Incidents could then be isolated to one factor or some intersection of 

factors. To better understand this concept, an example shown with three factors will be 

described next. 

Imagine three factors exist: A, B, and C. Initially, A includes incidents that intersect 

with B, incidents that intersect with C, and incidents that intersect with B and C. The 

goal is to find the number of incidents, A’, that does not include these intersections. It is 

also desired to find the number of incidents for the intersection of A and B (A∩B), as 

well as A and C (A∩C), that does not include the intersection of A, B, and C (A∩B∩C). 

The same process applies to factors B and C. These new values, which represent the 

isolated factors and their interactions, will be labeled: A’, B’, C’, A’∩B’, A’∩C’, B’∩C’, 
25

and A’∩B’∩C’. (A’∩B’ equals B’∩A’ and the same goes for the other intersections.) 

Figure 9 shows a Venn diagram of A, B, and C. A, B, and C are the circles in their 

entirety, including all intersections. Figure 10 shows the Venn diagram with the isolated 

factors and their isolated interactions. This means that A’ would be the number of 

incidents that can be attributed only to Factor A and no intersection with the other factors. 

Table 6 shows the equations to solve for A’, B’, C’, and their isolated interactions. 

 

 Figure 9. Venn Diagram of Factors A,B,C (pre-isolation) 

Figure 10. Venn Diagram of Isolated Factors and Isolated Interactions 

A’

B’

C’

A’∩B’ 

A’∩C’ 

B’∩C’ 

A’∩B’∩C’ 

A

B C
26

Table 6. Equations for Isolated Factors and Their Isolated Interactions 

Isolated Factor and Interactions 

Factor A’: 

 A’∩B’∩C’ = A∩B∩C 

 A’∩B’ = (A∩B) – (A’∩B’∩C’) 

 A’∩C’ = (A∩C) - (A’∩B’∩C’) 

 A’ = A – [(A’∩B’) + (A’∩C’) + (A’∩B’∩C’)] 

Factor B’: 

 A’∩B’∩C’ = A∩B∩C 

 B’∩A’ = (B∩A) - (A’∩B’∩C’) 

 B’∩C’ = (B∩C) - (A’∩B’∩C’) 

 B’ = B – [(B’∩A’) + (B’∩C’) + (A’∩B∩C’)] 

Factor C’: 

 A’∩B’∩C’ = A∩B∩C 

 C’∩A’ = (C∩A) - (A’∩B’∩C’) 

 C’∩B’ = (C∩B) - (A’∩B’∩C’) 

 C’ = C – [(C’∩A’) + (C’∩B’) + (A’∩B’∩C’)] 

The above methodology was performed on the five success factors mentioned above: 

Commercial Driver Safety, Locomotive Conspicuity, Sight Lines Clearance, Grade 

Crossing Maintenance Rule, and More Reliable Motor Vehicles. (See Appendix G to 

see a detailed breakdown of how these success factors were isolated.) The figure below 

graphs the number of incidents over the 10 years (1994-2003) that can be attributed to a 

particular isolated success factor. This is shown by the green dashed line and it is 

associated with the right vertical axis. The pink solid line shows the overall number of 

incidents nationwide over the 10 years. This line is associated with the left vertical axis. 

5.1.9 Commercial Driver Safety 

The first graph, Figure 11, shows incidents that can be attributed to Commercial Driver 

Safety for the years 1994 through 2003. In 1997, pickup trucks and vans received their 

own category on the accident/incident data forms (represented with a yellow triangle on 

the graph); in 1999, the Motor Carrier Safety Improvement Act was introduced 

(represented by an X on the graph). Nationally, incidents were reduced from 4,979 to 

2,924, a reduction of approximately 2,000 incidents. The reduction for incidents that 

were attributed to commercial vehicles was 712 from 1,246 to 534. The reduction in 

commercial vehicle incidents accounts for about a third of the national reduction. This 

factor follows the national trend or possibly influences it. 
27

2000

2500

3000

3500

4000

4500

5000

5500

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Overall Incidents

500

700

900

1100

1300

1500

1700

1900

Commercial Driver Incidents

Overall

Definition Change

Motor Carrier Safety Act

Commercial Driver Safety without Overlap

Figure 11. Incidents Attributed to Commercial Drivers for 1994-2003 
28

5.1.10 Locomotive Conspicuity 

Figure 12 shows incidents that can be attributed to Locomotive Conspicuity for the years 

1994 through 2003. A final rule (49 CFR 229) published in March 1996, effective in 

December 1997, stated that all locomotives exceeding 20 mph over the crossing must 

have auxiliary alerting lights in addition to the headlight (this date is represented with a 

yellow triangle on the graph). Incidents attributed to Locomotive Conspicuity were 

reduced from 745 to 465 over the study period. Locomotive Conspicuity incidents mimic 

the national trend. The cause of the spike in incidents in 2000 is unidentified. 

2000

2500

3000

3500

4000

4500

5000

5500

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Overall Incidents

350

450

550

650

750

850

Locomotive Conspicuity Incidents

Overall Alerting Lights Final Rule Locomotive Conspicuity without Overlap

Figure 12. Incidents Attributed to Locomotive Conspicuity for 1994-2003 
29

5.1.11 Sight Lines Clearance 

Figure 13 shows incidents that can be attributed to Sight Lines Clearance for the years 

1994 through 2003. During the study period, sight line obstructions at crossings and 

along the right of way drew increased attention. In 1995, FRA included a provision 

addressing the need to maintain rail rights-of-way adjacent to crossings free of sight 

obstructing vegetation in its “Notice of Proposed Rulemaking on Track Standards,” and 

in 1998, the final rule on Track Safety Standards (49 CFR 213.37) stated that 

“vegetation...shall be controlled so that it does not obstruct visibility...at highway-rail 

crossings.” The Technical Working Group report, Guidance on Traffic Control Devices 

at Highway-Rail Grade Crossings [14] contained guidance on appropriate sight distances 

at crossings and removing obstructions. In 1997, data fields on accident/incident reports 

were reconfigured. Incidents attributed to obstructed sight lines were reduced from 137 

to 63. Although these numbers are small and some variability occurs, they still mimic the 

national trend. 

2000

2500

3000

3500

4000

4500

5000

5500

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Overall Incidents

50

70

90

110

130

150

170

190

210

230

250

Sight Lines Incidents

Overall Sight Lines without Overlap

Figure 13. Incidents Attributed to Sight Lines Clearance for 1994-2003 
30

5.1.12 Grade Crossing Maintenance Rule 

Figure 14 shows incidents that can be attributed to the Grade Crossing Maintenance Rule 

for the years 1994 through 2003. In 1995, the Final Rule on Grade Crossing Signal 

Systems Safety was issued (this is represented by a yellow triangle on the graph). This 

rule states that railroads must implement specific maintenance, inspection, and warning 

testing requirements for active crossing systems. The graph shows a peak in 1997 

because in that year new categories were added to the signal field in the RAIRS Grade 

Crossing database. Before 1997, detailed data was not collected for this field. Incidents 

related to warning device failures were reduced from 116 (in 1997) to 52. The largest 

decline occurred from 1997 to 2000. After 2000, the reduction leveled off. The benefits 

of this factor could have been maximized by the year 2000. 

2000

2500

3000

3500

4000

4500

5000

5500

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Overall Incidents

0

50

100

150

200

250

300

Grade Crossing Maintenance Rule Incidents

Overall Grade Crossing Maintenance Rule

Definition Change Grade Crossing Maintenance without Overlap

Figure 14. Incidents Attributed to the Grade Crossing Maintenance Rule for 1994-

2003 
31

5.1.13 More Reliable Motor Vehicles 

Figure 15 shows incidents that can be attributed to More Reliable Motor Vehicles for the 

years 1994 through 2003. In 1997, a new category, trapped, was added to the position 

field on the accident/incident form. Incidents attributed to unreliable motor vehicles were 

reduced from 116 to 52. Similar to Sight Lines Clearance, the numbers are small but still 

mimic the national trend. 

2000

2500

3000

3500

4000

4500

5000

5500

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Overall Incidents

40

90

140

190

240

Reliable Car Incidents

Overall Reliable Cars without Overlap

Figure 15. Incidents Attributed to More Reliable Motor Vehicles for 1994-2003 

5.2 Final Percent Impact and Percent Reduction 

To obtain the new impact and reduction percentages, the incidents were assigned to one 

of seven categories: the five isolated success factors, a combined interaction of two or 

more of the five isolated success factors, and a seventh category labeled unidentified 

factors. The other factors category contained all other incidents that were not assigned to 

an isolated success factor or some combined interaction of isolated success factors. 

Once the success factors were isolated, the team used the same method as in Phase II to 

find new percent impacts and percent reductions for the five isolated factors and their 

combined interactions. The numbers were a more realistic representation of the effects of 

each factor. This method validated that the factors identified were indeed success factors 

for incident reduction. Table 7 shows these new percent impacts and percent reductions 

for the five isolated success factors, combined interactions, and unidentified factors. The 

numbers in this table are based on incidents nationwide from 1994 to 2003. 
32

Table 7. Final Percents Impact and Percents Reduction 

Factor Percent Impact Percent Reduction

Commercial Driver Safety 21.8% 34.6% 

Locomotive Conspicuity 15.0% 13.6% 

Sight Lines Clearance 2.6% 3.6% 

Grade Crossing Maintenance Rule 1.1% 3.1% 

More Reliable Motor Vehicles 1.9% 3.1% 

Combined Interactions 12.8% 21.9% 

Other Factors 44.7% 20.0% 

Pareto diagrams were found to be an effective tool to display the new results. The Pareto 

principle states that the majority of wealth is held by a disproportionately small segment 

of the population. This principle applies to quality improvement. Out of all possible 

problems, only some occur frequently [15]. For the success factors problem, this 

translates to mean that a small number of success factors are responsible for the majority 

of the incident reduction from 1994 to 2003. The five factors and the interactions 

between them impacted 55 percent of the incidents during these ten years. And, 80 

percent of the reduction in incidents, from 1994 to 2003, can be attributed to the five 

selected factors or the interaction of those factors. (See Table 8, Figure 16, and Error! 

Reference source not found..) The two isolated factors with the largest effects on 

incident reduction were Commercial Driver Safety and Locomotive Conspicuity. 

Table 8. Factor Key for Pareto Charts 

Factor Type Description 

A Unidentified factors 

B Commercial Driver Safety 

C Locomotive Conspicuity 

D Sight Lines Clearance 

E Grade Crossing Maintenance Rule 

F More Reliable Motor Vehicles 

G Combined interactions 
33

100%

52% 54% 55%

22%

37%

50%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

B CGD F E A

Factor

Percent of Total

Figure 16. Pareto Chart of Factors Involved in Incidents (Percent Impact) 

57%

100%

80%

74% 77%

70%

35%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

BGC DE FA

Factors

Percent of Total

Figure 17. Pareto Chart of Factors that Contributed to a Reduction of Incidents 
34

5.3 Kendall Partial Rank Correlation Coefficient 

When analyzing the factors by percent impact and percent reduction, it was not possible 

to isolate the following two success factors: Crossing Closure and Grade Separation and 

Warning Device Upgrades. A different methodology was needed to analyze the effects 

of these two factors. The correlation between each of these two factors and the number 

of incidents for each year between 1994 and 2003 was examined. The Kendall Partial 

Rank Correlation Coefficient was used. It is a nonparametric method of partial 

correlation that ranks the data for each variable on an ordinal scale. This means that no 

assumptions about the distribution of the population need be made. It determines the 

correlation between variable X and variable Y, when variable Z is held constant. The 

correlation coefficient values can range from one to negative one. A correlation 

coefficient of one indicates that variables X and Y are positively correlated, a coefficient 

of zero indicates that no correlation exists, and a coefficient of negative one indicates that 

there is a negative correlation [16]. 

Table 9 and Table 10 show the Kendall Partial Rank Correlation Coefficient for Crossing 

Closure and Warning Device Upgrades for each year in the study. The complete Kendall 

Partial Rank Correlation Coefficient analysis, as well as more detail on the statistical 

method, is available in Appendix H and Appendix I. 

5.3.14 Crossing Closure 

Crossing closure and grade separation yield many of the same safety benefits. They 

eliminate the risk of an incident by preventing highway and rail traffic from intersecting. 

Because of the limited data available for grade separations and the small number 

constructed in the Nation, the analysis focused on crossing closures. For the analysis of 

Crossing Closure, variable X is the number of closed crossings, variable Y is the number 

of incidents reduced from one particular year to the next, and variable Z is the number of 

crossings in that year. The data was separated into the eight FRA regions (see Appendix 

H). For each year of study, the research team ranked the eight regions on the three 

variables. The rankings were used to obtain Kendall rank correlation coefficients for 

variables X and Y, Z and Y, and X and Z. With those values, the correlation coefficient 

of X and Y when Z is held constant was computed. 

 

The Kendall Partial Rank Correlation Coefficients for crossing closures (see Table 9) 

indicate that the correlation between the number of crossings closed and the number of 

incidents reduced, is strong for some years and weak in others. The graph in Figure 17 

shows that the correlation may be cyclical; however, the span of this study is too short to 

draw any conclusions. The closure activity may take years to complete and see the 

benefits. The type of crossings closed also would impact incident reduction. Closing 

abandoned crossings with little traffic would yield fewer safety benefits than closing a 

crossing with high exposure. 
35

Table 9. Kendall Partial Rank Correlation Coefficient for Crossing Closure 

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Years of study

τ

Year τxy,z

1994 0.16 

1995 0.47 

1996 -0.29

1997 0.09 

1998 -0.45

1999 -0.02

2000 0.73 

2001 0.32 

2002 0.30 

2003 0.00 

Figure 17. Kendall Partial Rank Correlation Coefficients for Crossing Closure

5.3.15 Warning Device Upgrades 

For the analysis of Warning Device Upgrades, variable X is the number of crossing 

upgrades, variable Y is the number of incidents reduced from one particular year to the 

next, and variable Z is the number of crossings in that year. The data was separated into 

the eight FRA regions. For each year of study, the research team ranked the eight regions 

on the three variables. The rankings were used to obtain Kendall rank correlation 

coefficients for variables X and Y, Z and Y, and X and Z. With those values, the 

correlation coefficient of X and Y when Z is held constant was computed. 
36

The Kendall Partial Rank Correlation Coefficients for warning device upgrades also 

appears to be cyclical. Some years have high correlation values while others are low. 

The factor isolation conducted in Phase III attributed some incidents to particular factors. 

These incidents were removed for this correlation analysis. It was assumed that the 

incident reduction that was not attributed to the five isolated factors could have been 

attributed to warning device upgrades. Because different types of upgrades (e.g. flashing 

lights to gates, or crossbucks to gates) exist, a proxy number was used for the total 

number of upgrades. It was determined by the summation of the number of each type of 

upgrade multiplied by the effectiveness value of that upgrade, as defined by the RailHighway Crossing Resource Allocation Procedure [13]. Correlation values were found, 

as shown in Table 10. Figure 18 graphs the values in Table 10, and a cyclical pattern 

can be seen. This indicates that the benefits of upgrading the crossing warning devices 

may take time to be realized, or delays could occur in entering the data into the inventory 

database. 

Table 10. Kendall Partial Rank Correlation Coe

Upgrades 

fficient for Warning Device 

Year τxy,z

1994 0.21 

1995 0.42 

1996 -0.17

1997 0.46 

1998 0.17 

1999 -0.28

2000 0.57 

2001 0.18 

2002 0.52 

2003 0.09 
37

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Years of study

τ

Figure 18. Kendall Partial Rank Correlation Coefficients for Warning Device 

Upgrades 

Although the results varied among years, the tests for correlation between crossing 

closures or warning device upgrades and the reduction in highway-rail grade crossing 

incidents suggest that these two factors do have effects on crossing safety. The reduction 

in incidents from 1994 through 2003 can be related to efforts to close and consolidate 

crossings and upgrade the crossing warning devices. 

5.4 The Section 130 Program 

The Section 130 Program is a Federal program under US Code Title 23, Section 130 that 

provides funds for highway-rail grade crossing safety improvements. Under the Section 

130 Program, approximately $220,000,000 is available to states annually. Over the last 

15 years, $3.8 billion has been obligated for grade crossing improvements. The crossing 

safety improvement programs are unique to each state. They can include signage, 

pavement markings, warning devices, illumination, crossing surface repairs, crossing 

closures, and grade separations. Because this program encompasses many options to 

improve crossing safety, it was difficult to isolate its effect on incident reduction. The 

researchers conducted a qualitative analysis that revealed that the Section 130 Program is 

beneficial to crossing safety. Table 11 lists literature relevant to the Section 130 

Program. 
38

Table 11: Literature Relevant to the Section 130 Program 

Article Author(s) Date

Accidents That Shouldn’t Happen; A Report of United States Department March 

the Grade Crossing Safety Task force to of Transportation 1996 

Secretary Federico Pena 

U.S. Railroad Safety Statistics and Trends P. French January 

2006 

Accidents That Shouldn't Happen; A Report of the Grade Crossing Safety Task Force to 

Secretary Federico Pena [17] states that FHWA estimates that since the inception of the 

Section 130 Program, 40,000 injuries have been prevented and 9,000 lives saved. A 

presentation provided by the Association of American Railroads in January 2006, U.S. 

Railroad Safety Statistics and Trends [18], stated that the installation of gates at a 

crossing reduced the accident and fatality rates by 93 percent. This statistic indicates that

using the funds available from the Section 130 Program to upgrade crossing warning 

devices has a significant effect on crossing safety. The effects from this factor are 

conjoined with the effects from Crossing Closure and Grade Separation and Warning 

Device Upgrades. 

5.5 Operation Lifesaver 

Operation Lifesaver was also considered a possible success factor. However, proper 

evaluation of this program’s impact would require time and labor intensive data 

collection. Therefore, the research team chose a qualitative literature review evaluation. 

Table 12 lists the four most relevant articles. 

Table 12: Literature Relevant to Operation Lifesaver 

Article Author(s) Date

Does Public Education Improve Rail- I. Savage March 2005 

Highway Crossing Safety? 

Evaluation of Transport Canada’s Departmental Evaluation November 

Contribution to Operation Lifesaver Final Services Transport Canada 2003 

Report 

Public Education and Enforcement S. Sposato, P. Bien-Aime, June 2006 

Research Study – Draft Report and M. Chaudhary 

Driver's Behavior at Railroad Grade K.A. Brewer March 1992 

Crossings: Before and After Safety 

Campaign. Final Report 

In the paper, “Does Public Education Improve Rail-Highway Crossing Safety?” [19],

negative binomial regression was used to determine a relationship between Operation 

Lifesaver and grade crossing incidents and fatalities. While no conclusion could be made 

about the impact of Operation Lifesaver on fatalities, it was determined that the level of 

Operation Lifesaver educational activity had a significant effect on the number of 
39

incidents. One of the reasons the analysis was done at the State level was because 

Operation Lifesaver activity is measured on a State level. 

Transport Canada took a different approach in evaluating Operation Lifesaver in its 

November 2003 final report titled Evaluation of Transport Canada’s Contribution to 

Operation Lifesaver [20]. Transport Canada looked at three data sources: file and 

document reviews; interviews of Transport Canada railway safety staff, Operation 

Lifesaver National Office staff, and Operation Lifesaver Stakeholders; and two case 

studies. 

The report indicated that the Operation Lifesaver activity improved the safety behavior 

and awareness of its target audience. However, the positive results were not conclusive 

because of a lack of quantitative data. The case studies added additional anecdotal 

evidence. For example, the mock disaster showed that by improving the awareness about 

the logic of the events that lead to a grade crossing incident, people’s willingness to 

change their behavior can be influenced. 

The Public Education and Enforcement Research Study (PEERS) [21] dealt with the 

impact of education on violations of the grade crossing warning devices. Various types 

of public education were applied, including crossing safety blitzes and an increase in 

Operation Lifesaver presentations throughout the community. These activities resulted in 

the violation rates being reduced by 30.92 percent from the pre-test to the post-test 

period. In addition, the violation rate when a highway-user enters the grade crossing 

while warning flashers are active and the gates are fully deployed (horizontal position), 

showed a 71.4 percent decrease from the pre-test to the post-test period. 

Driver's Behavior at Railroad Grade Crossings: Before and After Safety Campaign 

Final Report [22] covered a new implementation of Operation Lifesaver in Iowa. This 

study reviewed traffic characteristics at 22 grade crossings before and after the 

implementation of Operation Lifesaver. Two observations were made before and after 

the implementation of Operation Lifesaver and then compared. The results showed that 

Operation Lifesaver was effective in three ways. At low speed crossings, the driver’s 

approach and crossing speeds were slowed. A lower percentage of drivers exceeded the 

speed limit while approaching the crossings. A higher percentage of drivers made sure 

the grade crossing was clear of trains before crossing over the tracks. 

In summary, while none of the articles contain any strong quantitative data that document 

the success of Operation Lifesaver on a nationwide or even state level, ample qualitative 

data does seem to suggest Operation Lifesaver is successful in its mission. 

5.6 Railroad Mergers–A Potential Success Factor 

In addition to the success factors analyzed in Phases I and II, changes in the railroad 

industry operations and organization, through mergers and consolidations, could also 

have influenced the reduction in highway-rail incidents. The mergers and consolidations 

could have affected the reduction in incidents in any of the following ways: the railroads 

were operating more efficiently, the success factors were reinforced in the new more 
40

efficient culture, or unidentified independent factors arose from the mergers and 

consolidations. 

The number and size of railroads during the 10-year study period, 1994 through 2003, 

changed. In 1994, there were 15 Class I railroads (as defined by the Surface 

Transportation Bureau) in the United States; in 2003, there were 9. This merging and 

consolidation likely resulted from a number of economic forces: modal competition, 

opportunities for economic efficiencies, changes in the manufacturing sector, and shifting 

traffic patterns. There could be a safety benefit to merging and an economy of scope or 

scale. These mergers may have reinforced the effects of the success factors (particularly 

factors within the railroad industry control, e.g., sight line improvements, crossing 

maintenance, warning device upgrades, crossing closures, and grade separations), as well 

as created new independent effects. To fully study this issue is a sizable undertaking; 

therefore, this study did not determine any independent effects. 

In 1994, 23 railroads had 85 percent of the incidents when ranked in descending order of 

frequency; in 2003, 18 railroads had 85 percent of these incidents. (See Appendix J.) As 

mentioned, the number of Class I railroads in the United States decreased from 15 in 

1994 to 9 by the end of the study period in 2003. (See Table 13.) Currently, Class I 

status is defined as operating revenues in excess of $289.4 million. In 2004, Class I 

railroads operated 97,496 of the 140,806 miles (70 percent) of the U.S. railroad network 

[23]. 

Table 13. Class I Railroads at the Beginning and End of the Study Period 

Class I Railroads at Beginning of Study Class I Railroads at End of Study 

Period, 1994 Period, 2003

Amtrak (National Railroad Passenger Amtrak (National Railroad Passenger 

Corp.) Corp.) 

Atchison, Topeka & Santa Fe Railway Burlington Northern and Santa Fe Railway 

Co. 

Burlington Northern Railroad Co. Canadian National/Grand Trunk Western 

Railroad Co. 

Chicago and North Western Railway Co. CSX Transportation, Inc. 

Consolidated Rail Corp. Kansas City Southern Railway Co. 

CSX Transportation, Inc. Norfolk Southern Combined Railroad 

Subs. 

Denver & Rio Grande Western Railroad Soo Line Railroad Co. 

Grand Trunk Western Railroad Inc. Union Pacific Railroad Co. 

Illinois Central Railroad Co. 

Kansas City Southern Railway Co. 

Norfolk Southern Corp. 

Soo Line Railroad Co. 

Southern Pacific Transportation Co. 

St. Louis Southwestern Railway Co. 

Union Pacific Railroad Co. 
41

While mergers occurred among Class I and other railroads, some Class II and III 

railroads also merged and/or consolidated. Additionally, Class II and III railroads may 

respond competitively to Class I operating changes. However, because they operate only 

a small percentage of U.S. tracks, these mergers are not of much interest. 

Highway-rail incidents declined 41.2 percent during the 10-year study period. Over the 

first 5 years of the study period, the rate of decline was 8.4 percent per year versus 4.4 

percent for the final five years. (See Table 14.) In the first period, Union Pacific 

Railroad Co. (UP) merged with Southern Pacific Transportation Co. (SP) and Chicago 

Northwestern Railway Co. (CNW) in 1995; Burlington Northern Railroad Co. (BN) and 

Atchison Topeka Santa Fe Railway (ATSF) merged in 1996. Whether these mergers in 

any way impacted the incident decline, particularly during the first 5 years, would be of 

interest. 

 

Table 14. Yearly Rates of Decline in Public and Private Crossing Incidents 

Year Incidents Percent 

Decline 

5 Year 

Compound

Rate 

of 

Decline 

 

1994 

1995 

1996 

1997 

1998 

4,979 

8.4% 

4,633 -7.0% 

4,257 -8.1% 

3,865 -9.2% 

3,508 -9.2% 

 

1999 

2000 

2001 

2002 

2003 

3,489 -0.5% 

4.4% 

3,502 0.4% 

3,237 -7.6% 

3,077 -4.9% 

2,920 -5.1% 

Highway-rail incidents for UP and its merger partners, SP and CNW, declined yearly, 

with the exception of 1999 throughout the study period. The overall reduction, from 

1,029 in 1994 to 427 in 2003, was 58.5 percent. (See Table 15.) UP acquired more track 

miles following the merger in 1995. As UP operations grew as a result of the merger, the 

incidents decreased. This suggests a safety benefit from railroad mergers that may not 

have previously been identified. 

Table 15. Public Crossing Incidents Reported for UP and Major Merger Partners

 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 

UP 591 468 583 679 590 656 602 555 479 427 

SP 246 201 202 

CNW 192 153 0 

 

TOTAL 1029 822 785 679 590 656 602 555 479 427 
42

 Change -20.1% -4.5% -13.5% -13.1% 11.2% -8.2% -7.8% -13.7% -10.9% 

Highway-rail incidents for the Burlington Northern and Santa Fe Railway Co. (BNSF) 

and two of its major merger partners, BN and ATSF, declined yearly with the exception 

of 1995, throughout the study period. (See Table 16.) The overall reduction from 625 in 

1994 to 343 in 2003 was 45.1 percent. A large incident reduction (20.8 percent) occurred 

in 1996 and again in 1998 (14.0 percent), bracketing the time of the merger. The trend in 

incidents involving BNSF after the merger suggests a relationship between consolidation 

and incident declines. 

Table 16. Public Crossing Incidents Reported for BNSF and Major Merger 

Partners 

 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 

BNSF 0 0 243 537 462 446 437 410 408 343 

BN 453 474 192 0 0 0 0 0 0 0 

ATSF 172 193 93 0 0 0 0 0 0 0 

 

TOTAL 625 667 528 537 462 446 437 410 408 343 

Change 6.7% -20.8% 1.7% -14.0% -3.5% -2.0% -6.2% -0.5% -15.9% 

 

5.7 Summary of Results 

The five success factors that were isolated in Phase III accounted for nearly 80 percent of 

the reduction in incidents from 1994 to 2003. These five factors are Commercial Driver 

Safety, Locomotive Conspicuity, Grade Crossing Maintenance Rule, Sight Lines 

Clearance, and More Reliable Motor Vehicles. Figure 19 shows the number of grade 

crossing incidents by year. The columns are divided into incidents that identified one of 

the five success factors as a cause and those where the cause was unknown. The 

incidents attributed to a success factor see a dramatic decline over the decade, whereas 

the other incidents remain constant. This indicates that the success factors that were 

instrumental in the reduction of incidents from 1994 to 2003 have been identified. 
43

Highway Rail Grade Crossing Incidents 1994-2003

0

1000

2000

3000

4000

5000

6000

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Years of Study

Highway Rail Grade Crossing Incidents

All Other Incidents 5 Success Factors

Figure 19. Highway-Rail Grade Crossing Incidents 1994-2003 

The two factors, Crossing Closure and Grade Separation and Warning Device Upgrades, 

were analyzed using the Kendall Partial Rank Correlation Coefficient. This analysis 

showed positive correlations between the factors and the reduction in incidents. This 

supports the belief that these two factors contributed to incident decline. The researchers 

performed qualitative analysis on the two factors, the Section 130 Program and Operation 

Lifesaver. Previously conducted research suggests these factors are success factors. 

Additional analysis during the course of this project revealed a potential 10th success 

factor, Railroad Mergers. 
44

6.0 Findings 

6.1 Lessons Learned 

Before an analysis can be conducted, it is important to determine which data to use, how 

to obtain that data, and how to maintain the integrity of that data. In this study, FRA 

databases were chosen as the data sources. Data mining tools were then needed to extract 

the data from the databases. ADAPT_X and SAS were chosen as two data mining tools. 

However, when the incident data generated from ADAPT_X and SAS were compared, 

the results showed that SAS produced a slightly higher number of incidents. The 

comparison of the two data sets revealed that in the SAS set, some incidents were 

counted multiple times. This may occur when more than one railroad is involved in an 

incident and each railroad files a report with FRA. Instead of only one incident record, 

two incidents are recorded. Because of the multiple counting generated by SAS, 

ADAPT_X was chosen as the primary data mining tool. The multiple entries of a single 

incident in the RAIRS Grade Crossing database could affect results, and measures should 

be taken to remedy these inconsistencies. 

When the data fields from the highway-rail crossing accident/incident reports are used to 

categorize the incidents by success factor, some incidents are counted multiple times. 

The overlap of incidents between factors results in an overestimation of the factor’s 

effect. It is important to recognize this, as well as to isolate the factors and any 

interaction between them. This yields more realistic numbers and a more accurate 

representation of each factor’s effect on incident reduction. 

In this study, the researchers examined data from multiple years; therefore, it was 

important to be aware of database changes over time. Some modifications were the result 

of the following: changes in definitions and codes used within the database, new fields 

and categories being added to the database, and new lawmaking rules and regulations. 

The Crossing Inventory database is divided into a current and a historical section. It is 

necessary to be aware that changes will be made in the current section of the database 

without updating the historical section. In addition, it was difficult to discern when a 

particular change was made to the database. Because the Crossing Inventory is split 

between the historical and current data, it is important to realize that changes made to one 

set of data would not necessarily be made to the other. 

During the course of the analysis, it became apparent that not enough data fields are 

collected to determine the effects of all the identified success factors. A limited number 

of fields are in the accident reports and the grade crossing inventory. Creative and 

qualitative methods were necessary to examine the effects of some factors. The 

quantitative methods attributed some portion of the reduction in incidents to the success 

factors. The qualitative analysis does not rely on mathematical validity, but instead on 

expert conjecture and prior studies. Some safety programs, such as Operation Lifesaver 

and the Section 130 Program, encompassed multiple factors. This made it difficult to 

identify the impact and reduction on incidents due to these safety programs. The 

differences in analytical methods made it difficult to compare the effects of all factors 

and presented challenges in determining the top success factors. 
45

The success factors were analyzed using two metrics. These two metrics, percent impact 

and percent reduction, provided a more accurate picture of the effects of the factors. A 

factor that reduced a large percentage of incidents could only be considered a top success 

factor if it impacted a large percentage of the incidents from 1994-2003. For example, a 

factor that reduced incidents by 50 percent, but only impacted 1 percent of incidents 

would not be a success factor. But a factor that reduced incidents by 25 percent and 

impacted 30 percent of incidents would. It was important to consider both metrics when 

determining the success factors. 

A Pareto diagram is a tool that is useful to quickly identify critical areas. In this analysis, 

the critical areas were those factors that were responsible for the largest reduction in 

incidents from 1994 to 2003. Pareto diagrams arrange factors in order of importance. In 

this analysis, the factors were arranged by the number of incidents impacted and the 

number of incidents reduced. The Pareto diagrams for this project provided evidence that 

the major success factors in incident reduction, from 1994 to 2003, were responsible for 

nearly 80 percent of the reduction during this period. 

In addition to the sources explicitly referenced in this paper, general knowledge and 

understanding were obtained from a variety of sources. These papers, reports, and 

legislative documents offered guidance and insight as the analysis moved forward. 

Appendix K lists additional sources. 

6.2 Recommendations for Next Steps 

Proceeding with the following next steps could enhance the results of this analysis. They 

could provide a better understanding of grade crossing success factors. 

6.2.1 Case Studies 

Case studies can help validate the results and understand complexities by taking a micro 

approach. Some regions and States have more pronounced declines. The States and 

regions selected to study should be larger entities because generally less variation occurs 

in counts among years. A comparison case study could be conducted between a State 

with a large reduction in incidents due to a particular success factor and a State without a 

significant reduction. Another case study approach would be to focus on the incident 

data and crossing improvement programs for a particular State. The availability of better 

data could produce a more thorough analysis. Some of the States, like Illinois, collect 

more detailed grade crossing data than is available from the FRA. 

Two potential States for case studies are Florida and Texas. Rail-Highway Crossing 

Safety— Fatal Crash and Demographic Descriptors [25] identified Texas as the State 

most representative of the Nation as a whole. Florida had a large number of incidents 

during the study period. Unlike other States with a large number of incidents, the number 

of crossings was relatively unchanged and the number of gated crossings only increased 

slightly. This disparity makes it an interesting case study. Examination of the highwayrail grade crossing incident data by FRA region is another possible case study. Variation 
46

in incident rates was detected among regions. Regions 4, 5, and 6 showed stronger 

declines in incidents during the study period (see Appendix J). 

The number and size of railroads during the study period changed. A safety benefit to 

merging may exist. A full study on the effects of railroad mergers would be a large 

endeavor. A case study of a large railroad State may provide a manageable beginning. 

Two potential States are Texas or California, where mergers and operational 

consolidation occurred during the study period. Incidents at public crossings in 

California declined from 183 in 1994 to 120 in 2003 (see Appendix J). UP and BNSF, 

two railroads with substantial merger activity during the study period, operate in 

California. Incidents at public crossings in Texas declined from 502 in 1994 to 258 in 

2003 (see Appendix J). The number of railroads reporting incidents also declined (30 to 

16). Two major railroad consolidations involving BNSF and UP occurred. These case 

studies would help reveal the effects of railroad mergers on incidents. 

6.2.2 Data Analysis 

Collection of additional data may improve understanding of future trends in highway-rail 

grade crossing incidents. The goal in any additional data collection is to remove the need 

to make assumptions about incidents. Some areas of data collection that could be 

improved are: mandatory updates to the data files for current information, more driver 

behavior data fields, more consistent guidelines for filling out the accident/incident 

reports and better record keeping for database changes. Information value should be 

weighed against stakeholder collection costs. 

It is important to look at the likelihood that each success factor will continue to provide a 

reduction in grade crossing incidents. Diminishing returns is a concern because after a 

certain point, additional spending in a given area will yield fewer benefits than it did in 

the past. For example, Locomotive Conspicuity had the largest reduction in incidents in 

the years immediately following the implementation of the alerting lights regulation. 

Afterwards, the benefit of this safety measure was constant. No expectation exists that 

another large benefit will be realized from this safety measure because all locomotives 

are already equipped with alerting lights. However, a safety measure such as Crossing 

Closure and Grade Separation may continue to yield benefits at the same rate. Every 

time a crossing is closed or grade separated, an impact on grade crossing incidents 

occurs. Crossings still need to be closed or grade separated. Diminishing returns are 

something to consider when investigating the success factors of the next decade. 

Analyzing data from grade crossing fatalities could reveal the success factors that would 

help reduce the most severe incidents. Safety initiatives that save human lives provide 

invaluable benefits. Two sources of grade crossing fatality data are the RAIRS Grade 

Crossing database and the FARS database. The FARS database collects more fields than 

the RAIRS Grade Crossing database, which could be helpful when looking for root 

causes. Identifying the factors that reduce the most severe incidents can help maximize 

the benefits of implementing safety countermeasures. 
47

For each of the factors identified in this paper, further analysis should be conducted on 

the incident data for 2004, 2005, and 2006. The methodology should be similar to the 

one used in this study. The same data sources and methods of extracting the data from 

the sources should be used for consistency. The goal of the analysis is to see if the trends 

identified in this study continue. One benefit of this analysis would be additional 

validation of the conclusions of this study. It would also help predict where additional 

safety benefits can be gained in the coming years. 

6.2.3 Future Success Factors 

Future analysis could explore the impact of enforcement to help reduce grade crossing 

incidents. Enforcement is an increasingly popular low cost option to enhance safety at 

highway-rail grade crossings. Research is already being conducted that indicates that 

enforcement has the potential to decrease grade crossing incidents. Different types of 

enforcement include photo enforcement or the physical presence of law enforcement at 

the crossing. 

Driver behavior is usually the cause of grade crossing incidents. The Office of the 

Inspector General report attributed 94 percent of highway-rail grade crossing incidents 

and 87 percent of fatalities to “risky driver behavior or poor judgment” [6]. For example, 

an incident can occur when a motorist decides to violate the crossing warning devices and 

drive around the gates. Enforcement activity can influence drivers to behave safely. The 

Use of Photo Enforcement at Highway-Rail Grade Crossings in the U.S. July 2000—July 

2001 [24] looked at the use of photo enforcement at six locations. The result was a 

reduction of violations at all locations. Public Education and Enforcement Research 

Study [21] looked at the effect of law enforcement blitzes (i.e., when a police officer 

issues a citation to violators of the crossing warning devices). The violation rates were 

lower on days when law enforcement was present. This indicates that highway-user 

behavior was changed for the safer during the law enforcement blitzes. Enforcement is a 

safety tool that was not widely used from 1994 to 2003, but could be a success factor in 

incident reduction in future decades.

On November 28, 2005, FRA enacted a new rule with the intent to reduce the number of 

highway-rail grade crossing incidents. The rule, Reflectorization of Rail Freight Rolling 

Stock (49 CFR 224), states that retro-reflective sheeting shall be applied to every railcar 

and locomotive. Retro-reflectivity improves train visibility at night or during other low 

visibility situations. This rule impacts safety at highway-rail grade crossings after the 10-

year period studied in this report. It is a potential success factor for future decades. 

Another rule that potentially impacts crossing safety after 2003 was the locomotive horn 

rule. The locomotive horn helps highway users avoid grade crossing incidents. 

However, communities who are frequently subjected to this train horn may feel that the 

level of noise the horn creates is affecting their quality of life. On June 24, 2005, FRA’s 

Final Rule on the Use of Locomotive Horns at Highway-Rail Grade Crossings (49 CFR 

222-229) went into effect. This new rule requires that train horns be sounded at all 

highway-rail grade crossings in the United States. It also allows localities to establish 

areas where locomotive horns are not sounded, provided that alternative safety measures 
48

are implemented to eliminate any increase in risk. The changes implemented by this rule 

will influence highway-rail grade crossing safety. This is a potential success factor in the 

next decade. 

In addition to the mergers and consolidations, other railroad-industry specific factors 

(e.g., traffic type or commodity mix) might have an effect on incident reduction and its 

variation among States and FRA regions. Shipments of major commodity groups (e.g., 

grain, coal, forest products) vary among years. This variation may differentially impact 

regions. In addition to the overall downward decline in incidents, a railroad activity cycle 

may be superimposed on the trend. Historically, the decline has occurred as an 

oscillating process, possibly influenced by fluctuations in railroad business activity. 
49

7.0 Conclusions 

At the conclusion of this analysis, 10 factors were identified as successful highway-rail 

grade crossing safety initiatives and programs. Five factors were isolated and analyzed 

based on numeric data, and the other five were analyzed inferentially and qualitatively. 

The five factors that were isolated are: 

• Commercial Driver Safety 

• Locomotive Conspicuity 

• Sight Lines Clearance 

• Grade Crossing Maintenance Rule 

• More Reliable Motor Vehicles. 

These factors were analyzed using data from the RAIRS Grade Crossing database. These 

five factors impacted 55 percent of the incidents during the ten years. And 80 percent of 

the reduction in incidents, from 1994-2003, can be attributed to these five selected factors 

or the interaction of these factors. The two factors with the greatest success in reducing 

incidents were Commercial Driver Safety (34.6 percent) and Locomotive Conspicuity 

(13.6 percent). 

The other five factors that were analyzed alternatively are: 

• Crossing Closure and Grade Separation 

• Warning Device Upgrades 

• Operation Lifesaver 

• Section 130 Program 

• Railroad Mergers 

The team examined Crossing Closure and Grade Separation and Warning Device 

Upgrades in Phase II of this study. The analysis used a probable number of incidents to 

determine the percent impact and percent reduction. These factors were unable to be 

isolated in Phase III. Therefore, a measure of correlation was used to establish a 

relationship between the factor and the reduction in incidents. Although a percent impact 

and reduction could not be isolated, the data did show a positive correlation. 

Operation Lifesaver and the Section 130 Program were multifaceted programs and 

addressed safety in a variety of ways. Because of their broad reaching nature, a solid 

quantitative analysis was difficult. There was not data collected that could be associated 

with many aspects of these programs. Therefore, a qualitative analysis was undertaken, 

revealing positive results. Although this analysis did not involve numerical data, it 

indicated that Operation Lifesaver and the Section 130 Program contributed to safer 

behavior at highway-rail grade crossings. Funds from the Section 130 Program can be 

used to close, separate, or upgrade crossings. Therefore, the effects of Crossing Closure 

and Grade Separation and Warning Device Upgrades also reflect on the effectiveness of 

the Section 130 Program. 
50

Investigation into alternative methodologies for analysis yielded supporting evidence of 

the nine success factors and another potential success factor. Railroad Mergers show 

evidence of dramatic incident reductions, even though the railroads grew in size and their 

operations expanded. Mergers and consolidation could have reinforced the other success 

factors identified in this paper, or it could have created independent effects. 

The analysis conducted during this study revealed 10 factors that can account for the 

majority of the reduction in grade crossing incidents from 1994 to 2003. 
51

Appendix A. 

 FRA Accident/Incident Report
52
53

Appendix B. 

 Comprehensive List of Success Factors 
54

Table Key • Description–Text description of the item • Evaluation Method–Description in text of how to evaluate the factor • Data Needs–Description of the ideal data needed to evaluate the factor • Interviews–‘Y’ = interviews would be a good data source, ‘N’ = it would not be helpful. • Data Analysis–‘Y’ = data analysis would be a good data source, ‘N’ = it would not be helpful. • Literature Review–‘Y’ = literature review would be a good data source, ‘N’ = it would not be helpful. • Projected Impact–‘L’ = Low Impact; ‘M’ = Medium Impact; ‘H’ = High Impact • Difficulty to Evaluate–‘E’ = Easy to evaluate; ‘M’ = Medium Difficulty to evaluate; ‘V’ = Very Difficult to Evaluate 
55

Crossing 

Safety 

Improvement 

Items

Description Evaluation 

Method

Data 

Needs

Interview

s 

Data 

Analysis

Literature 

Review

Projecte

Impac

Highway Traffic 

Signal 

Interconnection 

and Preemption

Traffic signal preemption is 

recommended for crossings 

with a highway-highway 

intersection within 200 feet. 

The normal sequence of 

traffic control signal 

indication is preempted upon 

the approach of trains to 

avoid entrapment of vehicles 

on the highway-rail crossing 

by conflicting aspects of the 

traffic control signals and the 

highway-rail grade crossing 

active warning devices. 

Look at “before and 

after” incident rates 

at crossings that have 

traffic signal 

preemption at nearby 

intersections. And 

conduct a trend 

analysis versus 

overall incident 

reduction. 

Look at incidents 

where the motor 

vehicle was trapped 

in the grade crossing 

by highway traffic 

Identify 

crossing with 

preemption 

and the date 

it was 

installed 

Identify 

crossings 

that are 

within 200 

yards of a 

traffic 

signaled 

intersection 

N Y N L 

Motor Carrier 

Safety 

Improvement and 

Commercial Driver 

Disqualification 

During the period 1994-2003, 

a greater emphasis was put on 

commercial driver safety. 

 The Motor Carrier Safety 

Improvement Act established 

the Federal Motor Carrier 

Review the Act for 

details on crossing 

safety. 

Evaluate by 

commercial driver 

Annual truck 

traffic Grade 

crossing 

incidents 

N Y N H 

Safety Administration and 

emphasized truck safety. In 

October of 1999, a law stated 

incidents/time for the 

10 years 1994-2003. 

involving 

commercial 

vehicles 

that commercial drivers Find number of 

convicted of violating the 

grade crossing warning 

devices would lose their 

commercial drivers who lost their CDL 

as a result of the law. 

CDL. 

Crossing 

Improvement 

Programs/Section 

130 Funding 

Highway funds are 

appropriated by Congress 

under Section 130, Title 23 of 

the United States Code for 

improvements to the safety of 

highway-rail grade crossings. 

The funds are appropriated by 

state and each state has its 

own crossing improvement 

plan. 

Investigate the 

programs and 

funding of States that 

have had the greatest 

improvements in 

crossing safety. 

Rank States by 

amount spent on 

crossing safety and 

then rank states by 

number of incidents. 

Grade 

crossing 

incident data 

by State 

Crossing 

improvement 

plans 

Levels of 

funding for 

CIP 

Y Y N M 
56

and number of 

crossings. 

 Try to see if any 

pattern between 

amount spent and 

number of incidents. 

Locomotive Making locomotives more Review literature on Incident data N Y Y M 

Conspicuity conspicuous aids drivers in evaluations done in 

not only seeing an oncoming the mid-1990s. Dates when 

train, but judging its distance lights were 

and speed. Look for decrease in installed on 

 A final rule published in incidents at night or locomotives 

March 1996, effective in passive crossings 

December 1997, stated that where locomotive is 

all locomotives exceeding 20 less visible. 

mph over the crossing must 

have auxiliary alerting lights 

in addition to the headlight. 

Urbanization The migration of the public Compare the number Census data N Y N M 

from rural areas of the Nation and types of urban 

to more urban areas. and rural crossings. Incident data 

 Urban centers have fewer 

grade crossings, especially Look at the exposure 

passive protective crossings. rates at crossings. 

Use GIS. 

More Reliable During the period of study, Look for decrease in Incident data N Y N M 

Motor Vehicles automobiles were improved stalled cars on the 

to be safer and more reliable. tracks that led to Automobile 

A more reliable vehicle incidents. reliability 

reduces the likelihood of ratings 

breaking down or stalling on 

the tracks and subsequently 

being hit by an oncoming 

train. 

Grade Separation The creation of an Look at incident rates Incident data N Y Y H 

over/underpass at highway- specifically in areas 

rail grade crossings. Grade with a lot of Identify the 

separations reduce the risk of separated crossings. separated 

a collision to zero because the crossings 

vehicle and train paths no Analyze the 

longer at that location. relationship between 

separated crossings 

and incidents. 

Crossing Closure Crossing closures may have Look at incident rates Incident data N Y Y H 

impacted the safety of specifically in areas 

highway-rail grade crossings with a lot of crossing Identify the 

over the Action Plan period closures. closed 

(1994 to 2003), In 1991, the crossings 
57

FRA Administrator Analyze the 

recommended closing 25% of relationship between 

all crossings. closed crossings and 

 Closures reduce the risk of a incidents. 

collision to almost zero 

because the vehicle and train Consider what types 

paths no longer intercept at of crossings are 

that location. being closed e.g. Are 

they closing the 

highest risk crossing? 

Or redundant unused 

crossings? 

Upgrade of Physical warning devices at Look at upgraded Incident data N Y Y M 

Warning Devices the crossings are upgraded for crossings incident 

safety. rates. Records of 

 When crossing warning crossing 

devices are upgraded to Analyze incident data upgrades 

devices with a higher by type of warning 

effectiveness value, the risk device. 

of a collision at the crossing 

is reduced. Review literature on 

the effectiveness of 

warning devices. 

Grade Crossing In 1995, the Final Rule on Look at incident data Incident N Y N H/L 

Maintenance Rule Grade Crossing Signal before and after the data 

And Constant System Safety was issued. rule to see if a 

Warning Time This rule stated that railroads reduction in incidents Database 

must implement specific occurred due to cause 

maintenance, inspection, and warning device codes 

testing requirements for malfunction 

active crossing warning 

systems. Look for a significant 

 The regular maintenance decrease in incidents 

and inspection would reduce with long or short 

the risk of warning device warning times 

malfunction. 

Review Literature on 

driver impatience, 

faulty gate 

activations 

Sight Lines The clearing of vegetation Review literature on Incident data N Y Y L 

and removal of obstructions sight line visibility 

surrounding a grade crossing and driver behavior 

enables highway-users to at crossings. 

observe an oncoming train 

from greater distances from Look at incidents that 

the crossing. occurred because the 

 Highway-users have a better driver was not aware 

chance to stop safely and the of the crossing and/or 

risk of being surprised by a train. 
58

train is reduced.
59

Driver Behavior The majority of incidents at Review the Incident data N Y Y M 

highway-rail grade crossings compendium 

are caused by driver error. reference list on Driver behavior 

 The change in behaviors over driver behavior at literature 

time could result in fewer grade grade crossings. 

crossing incidents. 

Conduct a data 

analysis of the 

incident data to 

identify causal 

factors and changes 

in driver behavior 

characteristics (time 

series over 10 years). 

Changes in Physical changes in the Do a time series Incident data N Y Y M 

Crossings characteristics of a crossing analysis of crossing 

Characteristics could result in a safer characteristics to Records of 

environment. identify changes in crossing 

the incident rates upgrades 

over 10 years. 

Literature on 

effectiveness of 

warning devices 

Accident A rule on accident reporting Identify the changes Reporting errors ? ? ? L 

Reporting mandated in 1996 required the in reporting. 

railroads to report incidents Rule changes 

 Changes in the accident 

reporting requirements result in 

different data being collected. 

 It also holds the railroads 

accountable for incidents and 

encourages a safety culture. 

On Board Train Improved communications Identify technology List of Y Y Y L 

Technology between locomotive and available and when it technologies 

wayside computers alert the was implemented. available and 

engineer to potential dangers at implementation 

upcoming crossings. records 

Training & New manuals and training for Create survey Railroad Safety Y N N L 

Manuals for locomotive engineers have questions for Data 

Locomotive increased safety as a priority. locomotive 

Engineers engineers. 

Investigate what is 

included in the 

manuals/training. 

AADT Changes in AADT affect Compare changes in AADT N Y N L 

exposure rates at grade AADT (or exposure) Incident data 

crossings. with changes in grade 

crossing incidents 
60

Trains Per Day Changes in trains per day affect Compare changes in TPD N Y N L 

(TPD) exposure rates at grade train traffic (or Incident data 

crossings. exposure) with 

changes in grade 

crossing incidents. 

Operation Education and awareness Interview Operation State Operation Y Y Y M 

Lifesaver program dedicated to ending Lifesaver staff and Lifesaver 

tragic collisions, fatalities, and presenters. Programs 

injuries at highway-rail grade 

crossings and on railroad rights Use the most active Incident data 

of way. state Operation 

Lifesaver programs 

to see if there is any 

correlation between 

rail safety and 

Operation Lifesaver. 

Drunk Driver Increased awareness of the Look for a decrease Incident data N Y N L 

Programs dangers of drunk driving result in incidents that 

in a safer environment for all identified the 

highway users. highway user as 

 In 1998, all states had zero impaired by alcohol 

tolerance laws. over the 10 years. 

 The TEA-21 provides grants 

to States for DUI programs. 

Enforcement Law and photo enforcement of Look at tickets and Violation data N Y Y L 

traffic laws related to the fines for crossing 

crossing discourage risky violations over 10 Tickets and 

behavior at highway-rail grade years. fines 

crossings. 

Review existing Incident data 

studies involving 

enforcement 

(PEERS, Naperville, 

IL). 

Corridor Decisions about crossing Compare crossings Corridor N Y Y L 

Approach improvements are made by on a corridor with information 

considering a stretch of other similar 

crossings together rather than crossings. Incident data 

each crossing individually. 

Review literature on 

existing corridors. 
61

Appendix C. 

 Percent Impact and Percent Reduction
62

The research team quantitatively analyzed eight success factors. The objective was to 

rank their effectiveness based on two metrics, percent impact and percent reduction. The 

percent impact is the percentage of incidents, from 1994 to 2003, that can be attributed to 

behaviors that the factor was attempting to change. The percent reduction is the 

percentage of incidents reduced, from 1994 to 2003, that can be attributed to the safety 

countermeasures for a factor. Table C-1 shows the analysis of the eight factors. 

Table C-1. Percent Impact and Reduction Table 

FACTOR % IMPACT % REDUCTION 

Traffic Signal Preemption 1.82 0 

Commercial Driver Safety 30.04/26.25 52.99/51.75 

Locomotive Conspicuity 24.7 30.3 

More Reliable Motor 7.54 11.19

Vehicles 

Sight Lines Clearance 5.13 8.81 

Grade Separation 0.34 1.02 

Crossing Closure 4.39 15.20 

Warning Device Upgrades 3.01 8.25 

Grade Crossing 2.23/3.41 3.07/13.92

Maintenance Rule 

A. Percent of incidents impacted (caused) that can be attributed to behaviors that the factor 

was attempting to change. 

B. Percent of incidents reduced that can be attributed to safety countermeasures for the factor

Traffic Signal Preemption 

The Factor: Traffic signal preemption is available for crossings with a highway-highway 

intersection within 200 feet. The normal sequence of traffic control signal indication is 

preempted upon the approach of trains to avoid entrapment of vehicles on the highwayrail crossing by conflicting aspects of the traffic control signals and the highway-rail 

grade crossing active warning devices. 

Data Source: FRA Inventory (Highway-Rail Crossing Inventory by State); RAIRS 

Method: Limited data was available for this field. The team was able to determine the 

number of incidents that occurred at crossings interconnected to highway traffic signals 

for each year. However, an interconnection is not necessarily indicative of preemption. 

The team was also able to identify the total number of crossings for all 10 years that had 

preemption. This data could not be reduced further to determine the number that existed 

each year. Other approaches were taken, including looking at interconnected and not 

interconnected crossings that had incidents where the vehicle was trapped on the 

crossing. This also proved fruitless because no way existed to determine the reason that 

the vehicle became trapped or if preemption was a possibility in preventing it. The total 
63

number of preempted crossings was less than 2 percent of all crossings; the percentage of 

incidents occurring at preempted crossings did not change over the ten year period. 

Crossings Total 

with Crossings 

Preemption 

Total 4514 248564 1.82%

Success Factor: No 

Next Steps: This factor will not be pursued any further. 

Commercial Driver Safety 

The Factor: Highway-rail grade crossing incidents involving trucks have the potential to 

be more severe than passenger automobiles. During the period 1994-2003, a greater 

emphasis was put on commercial driver safety. The Motor Carrier Safety Improvement 

Act of 1999 established the Federal Motor Carrier Safety Administration and emphasized 

truck safety. In October 1999, commercial drivers convicted of violating the grade 

crossing warning devices would lose their CDL. 

Data Source: RAIRS 

Method: The team used incidents from vehicles that would require a CDL. The percent 

impact was determined by using the percentage of the total number of incidents from 

1994 to 2003 that involved commercial drivers. The percent reduction was determined 

by finding the number of fewer incidents attributed to commercial drivers that occurred 

in 2003 versus 1994. The data structure changed in 1997. Pickup trucks and vans were 

added as separate categories. These counts could have been included in the category of 

trucks pre-1997. Therefore, the team also looked at the number of fewer incidents 

attributed to commercial drivers that occurred in 2003 versus 1997. 

Total 

Truck- School Commercial 

 Truck trailer Bus Bus Driver Total 

1994 1235 543 3 3 1784 4979 

1995 1189 504 3 3 1699 4633 

1996 1097 471 8 4 1580 4257 

1997 681 490 10 1 1182 3863 

1998 460 477 3 4 944 3508 

1999 408 475 6 1 890 3489 

2000 407 446 4 4 861 3502 

2001 350 465 7 3 825 3237 

2002 338 452 4 3 797 3077 

2003 313 375 7 0 695 2924 

TOTAL 6478 4698 55 26 11257 37471 

 

A. 30.04% = 11257/37471 

B. 52.99% = (695-1784)/(2924-4979) 
64

However, in 1997 pick-up trucks were separated out 

A. 26.25% = 6194/23600 

B. 51.75% = (695-1182)/(2924-3863) 

Success Factor: Yes 

Next Steps: This category could be confounded with other factors and therefore appear to 

have a much larger effect than actual. Some areas to pursue are: to incorporate the 

number of CDLs by year, or truck miles by year, investigate how many drivers lost their 

CDL as a result of the disqualification rule, and combine a subset of commercial drivers 

and the other factors to determine the overlap. 

Locomotive Conspicuity 

The Factor: A final rule published in March 1996, effective in December 1997 stated 

that all locomotives exceeding 20 mph over the crossing must have auxiliary alerting 

lights in addition to the headlight. 

Data Source: RAIRS 

Method: The FRA published report Safety of Highway-Railroad Crossings: Use of 

Auxiliary External Alerting Devices to Improve Locomotive Conspicuity [11], which 

stated that a significant difference existed in the effectiveness of the alerting lights during 

the day and night. The lights provided more benefit in the night. The team used 

incidents in which the rail equipment struck the highway user at dark, dawn and dusk as 

the measure of locomotive conspicuity. The percent impact was determined by using the 

percentage of the total number of incidents from 1994 to 2003 in which the rail 

equipment struck the highway user at dark or dusk. The percent reduction was 

determined by finding the number of fewer incidents that occurred in 2003 versus 1994. 

Rail Equip. struck highway user at dark, dawn 

and dusk Total 

Year Pulling Pushing Incidents

1994 1220 100 4,979 

1995 1107 107 4,633 

1996 981 88 4,257 

1997 834 67 3,865 

1998 763 79 3,508 

1999 728 70 3,489 

2000 804 81 3,502 

2001 694 78 3,237 

2002 644 95 3,077 

2003 616 80 2,920 

TOTAL 8391 845 37,467 

 A. 24.7% = (8391+845)/37,467 

 B. 30.3% = ((616+80)-(1220+100))/(2920-4979) 

 

Success Factor: Yes 
65

Next Steps: Use data to rank crossings by darkness. The FRA published report Use of 

Auxiliary External Alerting Devices to Improve Locomotive Conspicuity [11] states a 

statistically significant difference in the effectiveness of alerting lights in daylight and 

darkness. Consider the railroad equipment accident database as a source, which lists a 

cause of “Highway user misjudgment under normal weather and traffic conditions”. The 

narratives could provide more detailed information. 

More Reliable Motor Vehicles 

The Factor: During the period 1994 to 2003 automobiles have been improved to be safer 

and more reliable. A more reliable vehicle reduces the likelihood of breaking down 

(stalling) on the tracks and subsequently being hit by an oncoming train. 

Data Source: RAIRS 

Method: The database identifies incidents in which the highway vehicle was stalled on 

the tracks at the time of impact. The percent impact was determined by using the 

percentage of the total number of incidents from 1994 to 2003 in which the highway 

vehicle was stalled on the crossing. The percent reduction was determined by finding the 

number of fewer incidents attributed to stalled vehicles that occurred in 2003 versus 

1994. 

Stalled 

on 

Year Crossing Total 

1994 412 4,979

1995 438 4,633

1996 370 4,257

1997 252 3,864

1998 227 3,508

1999 243 3,489

2000 231 3,502

2001 228 3,236

2002 241 3,077

2003 182 2,924

TOTAL 2,824 37,471

A. 7.54% = 2824/37471 

B. 11.19% = (182-412) / (2924-4979) 

Success Factor: Yes 

Next Steps: Use JD Power Associates or Consumer Reports as a source on automobile 

reliability by years. The National Highway Traffic Safety Administration database FARS 

contains driver information and vehicle types. 
66

Sight Lines Clearance 

The Factor: The clearing of vegetation and removal of obstructions surrounding a grade 

crossing enables highway-users to observe an oncoming train from greater distances from 

the crossing. Highway-users have a better chance to stop safely, and the risk of being 

surprised by a train is reduced. 

Data Source: RAIRS 

Method: The percent impact was determined by using the percentage of the total number 

of incidents from 1994 to 2003 that described some visual obstruction present at the 

grade crossing at the time of impact. The percent reduction was determined by finding 

the number of fewer incidents in which an obstruction was present that occurred in 2003 

versus 1994. 

Year Permanent Standing Passing Topography Vegetation Highway Other 

Structure Railroad Train Vehicles (Specify in 

Equipment Narrative) 

1994 94 39 18 37 84 22 40

1995 88 27 19 39 58 14 38

1996 76 20 14 48 63 7 35

1997 73 25 8 38 61 13 46

1998 59 27 16 24 74 11 32

1999 55 20 14 14 40 12 17

2000 41 22 10 21 50 7 36

2001 34 19 6 13 39 11 28

2002 31 9 14 23 40 18 13

2003 36 11 12 22 21 9 23

TOTAL 587 219 131 279 530 124 308

A. 5.13% = 1923/37471 

B. 8.81% = (113-294)/(2924-4979) 

Success Factor: Uncertain 

Next Steps: Examine the proportion of obstructed sightlines that are attributed to ones 

that could be influenced by countermeasures (e.g., vegetation) and those that are not (e.g., 

topography). Consider the railroad equipment accident database as a source, which lists a 

cause of “Highway user unaware due to environmental factors.” The narratives could 

provide more detailed information. 
67

Crossing Closure 

The Factor: When a crossing is little used or redundant, eliminating the crossing 

becomes an option. A crossing closure physically removes all access to the grade 

crossing, eventually eliminating it completely. When a crossing is closed, the risk 

associated with a collision between a vehicle and a train is reduced to nearly zero. 

Data Source: FRA Inventory (Hwy-Rail Crossing Inventory by State); RAIRS 

Method: 

Probable incident rate is xi incident per year, which is (total incidents over 9+i years)/(9+i 

years), where i is 1 to 10. 

1984 was used as the base year to compute the probable incident rate and incidents at 

closed crossing since 1984 were examined. 

The total probable incident rate (298.98) is the number of incidents that may have 

occurred in 2003 if all the closed crossings stayed open. 

Probable incidents for year i = the sum from 1994 to year i of (probable incident rate * i). 

Probable incidents for year i is the number of incidents that may have occurred for year i 

if the crossings closed between 1994 and year i stayed open. 

Probable incidents between year i and 2003 = (xi incidents per year)*(11-i). 

Probable incidents between year i and 2003 is the number of incidents that may have 

occurred between year i and 2003 if the crossings closed in year i stayed open. 

# of 

incidents 

from 

closed 

Number crossings # of Probable 

of since Crossings Probable Probable incidents Actual Year incidents 1984 Closed (FRA incident incidents from year plus 

(i) (RAIRS) (RAIRS) INVENTORY) Rate for year i i to 2003 Probable 

1994 1 4979 278 4010 27.80 27.80 278.00 5006.80 

1995 2 4633 327 4249 29.73 57.53 267.55 

1996 3 4257 609 6020 50.75 108.28 406.00 

1997 4 3865 411 4734 31.62 139.89 221.31 

1998 5 3508 348 3582 24.86 164.75 149.14 

1999 6 3489 335 3404 22.33 187.08 111.67 

2000 7 3502 413 4442 25.81 212.90 103.25 

2001 8 3237 646 4543 38.00 250.90 114.00 

2002 9 3077 389 3070 21.61 272.51 43.22 

2003 10 2924 503 3948 26.47 298.98 26.47 3222.98 

Total 37471 4259 42002 298.98 1720.61 

Percent Impact = 4.39% 

Percent Impact = (the sum of probable incidents between 1994 and 2003)/(the sum of 

actual and probable incidents between 1994 and 2003)*100 

Percent Impact = 1720.61/(1720.61+37471)*100 

Percent Reduction = 15.20% 
68

Percent Reduction = the absolute value of the (change in probable incidents from 1994 to 

2003)/(change in actual incidents plus probable incidents from 1994 to 2003)*100 

Percent Reduction = ABS(27.80-298.98)/(5006.80-3222.98)*100 

Success Factor: Yes 

Next Steps: Re-examine the predicted number of incidents. Find a better approximation 

than linear extrapolation. Cite case studies as supporting evidence, such as North 

Carolina Sealed Corridor. Consider the impact of rerouted traffic on adjacent crossings. 

Grade Separation 

The Factor: Grade Separation is offsetting the rail tracks from the roadway by either an 

underpass or an overpass. The risk of a collision is reduced because the vehicle and train 

paths no longer intercept. 

Data Source: FRA Inventory (History & Current File); RAIRS 

Method: For detailed explanation see crossing closures above. 

Number of Number of 

Number of Incidents from Crossing Probable Probable Probable 

Incident Separated Xing Separated (FRA Incident Incident for Incidents from Actual Plus 

Year (i) (RAIRS)Since 1984 (RAIRS) INVENTORY) Rate Year i Year i to 03 Probable

1994 1 4979 11 25 1.10 1.1 11.00 4980.1

1995 2 4633 3 29 0.27 1.37 2.45 

1996 3 4257 83 28 6.92 8.29 55.33 

1997 4 3865 25 18 1.92 10.21 13.46 

1998 5 3508 31 23 2.21 12.43 13.29 

1999 6 3489 24 19 1.60 14.03 8.00 

2000 7 3502 43 24 2.69 16.71 10.75 

2001 8 3237 55 27 3.24 19.95 9.71 

2002 9 3077 16 30 0.89 20.84 1.78 

2003 10 2924 18 27 0.95 21.79 0.95 2945.79

 37471 309 250 21.79 126.72

 

Percent Impact 0.34% 

 

Percent Reduction 1.02% 

Success Factor: No 

Next Steps: Grade Separation is a useful tool for increasing safety at highway-rail grade 

crossings. By removing the intersection for cars and trains to meet, the risk of a vehicletrain incident at that crossing is eliminated. However, during the period of study 1994-

2003, only 274 crossings were separated. This is not a large enough number to produce a 

significant effect on a national scale. 
69

Warning Device Upgrades 

The Factor: Each type of warning device has an effectiveness value associated with it. 

A passive warning device is less effective than flashing lights and gates. When crossing 

warning devices are upgraded to ones with a higher effectiveness value the risk of 

collision at the crossing is reduced. 

Data Source: FRA Inventory (History & Current File); RAIRS 

Method: Due to the time consuming nature of evaluating the warning device upgrades 

for each state, the team looked only at the State of Illinois. Illinois has a large number of 

crossings and highway-rail grade crossing incidents. To determine the number of 

incidents that were saved by upgrading warning devices, the team used an effectiveness 

rate for each type of upgrade: 

• Passive to Flashing Lights 0.7 

• Passive to Gates 0.83 

• Passive to Stop Signs 0.35 

• Flashing Lights to Gates 0.69 

For each year 1994 to 2003, the team also developed an accident rate for each type of 

warning device. This formula was accident rate = number of incidents at crossing with 

warning device/number of total crossings with warning device. 

The probable incident rate for year i was determined by 

(accident rate) x (number of upgraded crossings) x (upgrade effectiveness). 

The probable incident year i column is the number of additional incidents that would 

have occurred that year if no crossings were upgraded (either in that year or any previous 

years). 

The probable incidents from year i to 2003 is the number of additional incidents that 

would have occurred over the ten years from the crossings upgraded in year i. 
70

Upgrade of Warning Devices for Illinois 

Guesstimate 

Incidents 

Guesstimate Guesstimate from Year i Actual plus 

Year (i) # Incidents Rate Year i to 03 Guesstimate 

1994 1 337 1.960560782 1.961 19.6056078 338.961

1995 2 295 1.085918267 3.04691827 9.77326441 

1996 3 232 1.115561317 4.16247958 8.92449054 

1997 4 213 0.753095961 4.91557555 5.27167173 

1998 5 199 0.942962012 5.85853756 5.65777207 

1999 6 202 1.370298279 7.22883584 6.8514914 

2000 7 217 0.377446653 7.60628249 1.50978661 

2001 8 212 0.823870976 8.43015347 2.47161293 

2002 9 172 1.028719964 9.45887343 2.05743993 

2003 10 157 0.235026135 9.69389957 0.23502613 166.6939

 2236 9.693460348 62.3581636 

Percent Impact 2.71% 

 

Percent Reduction 4.49% 

Upgrade of Warning Devices 

Probable 

Probable Probable Incidents 

Number of Incident Incident from Year i Actual Plus 

Year (i) Incidents Rate Year i to 03 Probable 

1994 1 4979 27.627 1.961 276.268 4980.961

1995 2 4633 24.318 26.279 218.858 

1996 3 4257 20.442 46.721 163.540 

1997 4 3865 23.640 70.361 165.479 

1998 5 3508 14.905 85.266 89.428 

1999 6 3489 19.164 104.429 95.818 

2000 7 3502 17.642 122.071 70.566 

2001 8 3237 17.459 139.529 52.376 

2002 9 3077 10.810 150.339 21.619 

2003 10 2924 8.302 158.641 8.302 3082.641

 37471 184.307 1162.255

Percent Impact 3.01%

 

Percent Reduction 8.25%

Success Factor: Uncertain 
71

Next Steps: A deeper look into inventory data provided by the State of Illinois should 

reveal whether the majority of upgrades were done prior to the study period. The FRA 

inventory yielded inconclusive results. 

Grade Crossing Maintenance Rule 

The Factor: In 1995, the Final Rule on Grade Crossing Signal System Safety was issued. 

This rule stated that railroads must implement specific maintenance, inspection, and 

warning testing requirements for active crossing systems. The regular maintenance and 

inspection would reduce the risk of warning device malfunction. 

Data Source: RAIRS 

Method: For this factor, the team assumed that the ways in which a warning device could 

malfunction would be too long a warning, too short a warning, or no warning at all. The 

database identifies incidents that have both alleged and confirmed warning device 

malfunction. The percent impact was determined by using the percentage of the total 

number of incidents from 1994 to 2003 that had either a confirmed or alleged warning 

device malfunction. The percent reduction was determined by finding the number of 

fewer incidents with warning device malfunction that occurred in 2003 versus 1994. 

However, it appeared in 1997 that better data was collected for this field. Therefore, the 

team also looked at the number of fewer incidents with a warning device malfunction that 

occurred in 2003 versus 1997. 

Alleged Alleged Alleged Confirmed Confirmed Confirmed Empty Total 

Warning Warning No Warning Warning No Warning Malfunction

Time Time Warning Time Time Less 

Greater Less Greater Than 20 

Than 60 Than 20 Than 60 Seconds 

Seconds Seconds Seconds 

1994 0 0 10 0 0 0 0 10

1995 0 0 7 0 0 0 0 7

1996 0 0 10 0 0 0 0 10

1997 159 8 4 12 4 17 0 204

1998 145 6 4 13 4 19 0 191

1999 46 11 7 8 1 21 0 94

2000 50 7 4 2 0 22 0 85

2001 38 16 12 2 2 13 0 83

2002 32 7 9 8 3 18 0 77

2003 35 8 9 3 1 17 0 73

TOTAL 505 63 76 48 15 127 0 823

A. 2.23% = 823/37471 

B. 3.07% = (73-10)/(2924-4979) 

However, better data was collected in 1997 

A. 3.41% = 807/23600 

B. 13.92% = (73-204)/(2924-3865) 
72

Success Factor: Yes 

Next Steps: The FRA collects data on activation failures that are available on its Web site. 

The data exists for the years 2000 through 2003. A time series or trend analysis of this 

data might be useful. General inspection reports are available from 1995 to the present. A 

concern exists because of the inclusion of alleged malfunctions, should these be removed 

the percentages would be much lower. Consider the railroad equipment accident database 

as a source, which lists a cause of “Malfunction, improper operation of train activated 

warning devices.” 

Other Factors Considered 

From the inception of this project the team considered other possible factors in addition to 

those listed above. Some were found to be too difficult to analyze with the available data, 

and others were simply not determined to have a large impact. 

Difficult to Analyze 

• Section 130 funding – FHWA ceased collecting the state crossing improvements 

plans prior to the period in study. Section 130 funds cover upgrades, closures, and 

separations. There is overlap between this factor and many of the other factors in 

study. 

• Urbanization – The distribution of grade crossings that are urban versus rural 

ceased to be collected after 1997. Population censuses are taken only every ten 

years. The team was unable to conclude anything about population migration 

toward urban climates, and/or the location of the highway-rail grade crossings. 

• Driver behavior – This factor is extremely difficult to analyze, there are few 

human behavior studies conducted on highway-rail intersections. Driver behavior 

is involved in many of the other factors so there is overlap. It may be useful for 

supporting data. 

• Operation Lifesaver – Little to no evaluations have been done on OL. There is no 

evaluation on a national scale. Without conducting a controlled experiment, it is 

extremely difficult to analyze. 

Low Impact 

• Changes in crossing characteristics 

• Changes in accident reporting 

• Onboard train technology 

• New training and manuals available for train crews 

• AADT 

• Trains per day 

• Drunk driver programs 

• Enforcement 

• Corridor approach 
73

Appendix D. 

 Distribution of Incidents by Type of Vehicles
74

Table D-1. Total Incidents by Type of Vehicles 

Other 

Truck Pickup- School Motor 

Auto Truck Trailer Truck Van Bus bus Motorcycle Vehicle Pedestrian Other Total 

1994 2940 1235 543 0 0 3 3 22 0 77 156 4979 

1995 2703 1189 504 0 0 3 3 14 0 74 143 4633 

1996 2463 1097 471 0 0 8 4 11 0 95 108 4257 

1997 2078 681 490 335 96 10 1 7 49 73 43 3863 

1998 1810 460 477 444 114 3 4 7 56 87 46 3508 

1999 1763 408 475 513 129 6 1 7 47 81 59 3489 

2000 1697 407 446 554 161 4 4 12 67 88 62 3502 

2001 1516 350 465 523 129 7 3 6 65 92 81 3237 

2002 1449 338 452 490 141 4 3 8 45 71 76 3077 

2003 1401 313 375 470 137 7 0 12 47 79 83 2924 

TOTAL 19820 6478 4698 3329 907 55 26 106 376 817 857 37469 

Table D-2. Percent of Incident by Type of Vehicles 

Other 

Truck Pickup- School Motor 

Auto Truck Trailer Truck Van Bus bus Motorcycle Vehicle Pedestrian Other Total 

1994 59.05% 24.80% 10.91% 0.00% 0.00% 0.06% 0.06% 0.44% 0.00% 1.55% 3.13% 100.00% 

1995 58.34% 25.66% 10.88% 0.00% 0.00% 0.06% 0.06% 0.30% 0.00% 1.60% 3.09% 100.00% 

1996 57.86% 25.77% 11.06% 0.00% 0.00% 0.19% 0.09% 0.26% 0.00% 2.23% 2.54% 100.00% 

1997 53.79% 17.63% 12.68% 8.67% 2.49% 0.26% 0.03% 0.18% 1.27% 1.89% 1.11% 100.00% 

1998 51.60% 13.11% 13.60% 12.66% 3.25% 0.09% 0.11% 0.20% 1.60% 2.48% 1.31% 100.00% 

1999 50.53% 11.69% 13.61% 14.70% 3.70% 0.17% 0.03% 0.20% 1.35% 2.32% 1.69% 100.00% 

2000 48.46% 11.62% 12.74% 15.82% 4.60% 0.11% 0.11% 0.34% 1.91% 2.51% 1.77% 100.00% 

2001 46.83% 10.81% 14.37% 16.16% 3.99% 0.22% 0.09% 0.19% 2.01% 2.84% 2.50% 100.00% 

2002 47.09% 10.98% 14.69% 15.92% 4.58% 0.13% 0.10% 0.26% 1.46% 2.31% 2.47% 100.00% 

2003 47.91% 10.70% 12.82% 16.07% 4.69% 0.24% 0.00% 0.41% 1.61% 2.70% 2.84% 100.00% 

Total 52.90% 17.29% 12.54% 8.88% 2.42% 0.15% 0.07% 0.28% 1.00% 2.18% 2.29% 100.00% 
75

52.90%

12.54%

17.29%

8.88%

Auto Truck Truck-trailer

Pickup-truck Van Bus

School bus Motorcycle Other Motor vehicle

Pedestrain Other

Distribution of Incidents by Type of Vehicle 
76

Appendix E. 

 An Analysis of Severity 
77

Table E-1. Fatalities Involving Auto, Truck, and Truck-Trailer 

Auto Truck Truck-Trailer

Incident Vehicle Fat Train Fat Incident Vehicle Fat Train Fat Incident Vehicle Fat Train Fat

1994 2940 381 0 1235 136 0 543 15 1 

1995 2703 324 0 1189 144 0 504 22 1 

1996 2463 269 0 1097 121 0 471 21 1 

1997 2078 246 0 681 89 0 490 21 0 

1998 1810 205 0 460 57 0 477 13 0 

1999 1763 185 0 408 33 0 475 12 11 

2000 1697 177 1 407 42 1 446 18 1 

2001 1516 174 0 350 40 0 465 20 0 

2002 1449 176 0 338 33 0 452 10 0 

2003 1401 138 0 313 26 1 375 11 0 

Total 19820 2275 1 6478 721 2 4698 163 15 

Table E-2. Fatality Rate (Fatality/Incident) for Auto, Truck and Truck-Trailer 

Auto Truck Truck-Trailer

Vehicle Train Vehicle Train Vehicle Train 

1994 0.13 0.00 0.11 0.00 0.03 0.00 

1995 0.12 0.00 0.12 0.00 0.04 0.00 

1996 0.11 0.00 0.11 0.00 0.04 0.00 

1997 0.12 0.00 0.13 0.00 0.04 0.00 

1998 0.11 0.00 0.12 0.00 0.03 0.00 

1999 0.10 0.00 0.08 0.00 0.03 0.02 

2000 0.10 0.00 0.10 0.00 0.04 0.00 

2001 0.11 0.00 0.11 0.00 0.04 0.00 

2002 0.12 0.00 0.10 0.00 0.02 0.00 

2003 0.10 0.00 0.08 0.00 0.03 0.00 
78

Severity to Highway User (Fatalities)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Year

Severity (Fatalities/Incident)

Auto Truck Truck-Trailer

Fatality Rate for Highway Users for Auto, Truck, Truck-Trailer 

Severity to Train Occupants (Fatalities)

0.00

0.01

0.01

0.02

0.02

0.03

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Year

Severity (Fatalities/Incident)

Au to Truck Truck-Tra iler

Fatality Rate for Train Occupants for Auto, Truck, and Truck-Trailer 
79

From the figure below, the severity to highway users of incidents that involved 

automobiles and trucks is higher than incidents that involved truck trailers. This could be 

because on average, automobiles and trucks have more passengers than truck trailers. 

Thus, automobiles and trucks have the possibility of more injuries during an incident. 

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Year

Severity (Injuries/I ncident)

Auto Truck Truck-Trailer

Incident Severity to Highway User from Automobile, Truck, or Truck-Trailer 
80

Appendix F. 

 Activation Failure
81

Table F-1. Failure Rates for Active Grade Crossing Warning Devices 

Active Crossing Activation 

Year Failure Gates Lights Othera

 Total Failure Rateb

1992 2279 27507 29949 1726 59182 3.851 

1993 1672 28139 29645 1672 59456 2.812 

1994 29050 29325 1661 60036 

1995 29912 28910 1583 60405 

1996 30813 28614 1557 60984 

1997 31696 28354 1515 61565 

1998 & 1999 1269 65641 55972 2984 124597 1.018 

2000 595 34296 27100 1417 62813 0.947 

2001 429 35422 26558 1342 63322 0.677 

2002 472 36403 25841 1188 63432 0.744 

2003 472 36440 25656 1269 63365 0.745 

Source FRA FRA Publication 

a

Other includes WigWag, Highway Signal, and Bells. 

b

Rate equals activation failure times 100 divided by total active crossing 

Activation Failure Rate

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

1992 1993 1994 1995 1996 1997 1998 &

1999

2000 2001 2002 2003

Year

Per 100 Crossing

Failure Rate

Table F-1. Failure Rate for Active Grade Crossing Warning Devices 
82

Appendix G. 

 Breakdown of Isolated Success Factors
83

Success Factors Keys 

• C = Commercial Driver Safety 

• L = Locomotive Conspicuity 

• M = Grade Crossing Maintenance Rule 

• R = More Reliable Motor Vehicles 

• S = Sight Lines Clearance 
84

Table G-1. Breakdown of Commercial Driver Safety Overlap by Year 

Year C' CL' CR' CM' CS' CLR' CLM' CLS' CRM' CRS' CMS' CLRM CLRS CLMS CRMS CLRMS

1994 1246 295 62 2 98 62 0 15 0 3 0 0 1 0 0 0

1995 1223 240 66 2 85 66 0 10 0 5 0 0 2 0 0 0

1996 1150 224 63 2 80 40 0 15 0 1 1 1 3 0 0 0

1997 834 157 30 42 70 28 8 4 1 2 5 0 1 0 0 0

1998 676 106 24 36 54 29 5 3 0 2 5 2 1 1 0 0

1999 638 123 36 16 48 20 3 3 1 0 1 1 0 0 0 0

2000 638 110 30 14 35 22 4 5 1 1 1 0 0 0 0 0

2001 623 98 27 10 37 21 2 1 0 0 3 3 0 0 0 0

2002 602 99 38 8 24 21 1 3 0 0 0 1 0 0 0 0

2003 534 93 18 12 23 6 0 4 0 2 2 0 0 1 0 0

C CL CR CM CS CLR CLM CLS CRM CRS CMS CLRM CLRS CLMS CRMS CLRM

11257 1964 744 198 661 331 33 73 11 24 20 8 8 2 0 0 

 315 23 63 3 16 18 

 1545 394 144 554 

8164 

C'= 8164 

C'=C-(CL'+CR'+CM'+CS'+CLR'+CLM'+CLS'+CRM'+CRS'+CMS'+CLRM+CLRS+CLMS+CRMS+CLRMS) 

 

CLR'= 315 

CLR'=CLR-(CLRM+CLRS+CLRMS) 

 

CLM'= 23 

CLM'=CLM-(CLRM+CLMS+CLRMS) 

 

CLS'= 63 

CLS'=CLS-(CLRS+CLMS+CLRMS) 

 

CRM'= 3 

CRM'=CRM-(CLRM+CRMS+CLRMS) 

 

CRS'= 16 

CRS'=CRS-(CLRS+CRMS+CLRMS) 

 

CMS'= 18 

CMS'=CMS-(CLMS+CRMS+CLRMS) 

 

CL'= 1545 

CL'=CL-(CLR'+CLM'+CLS'+CLRM+CLRS+CLMS+CLRMS) 

 

CR'= 394 

CR'=CR-(CLR'+CRM'+CRS'+CLRM+CLRS+CRMS+CLRMS) 

 

CM'= 144 

CM'=CM-(CLM'+CRM'+CMS'+CLRM+CLMS+CRMS+CLRMS) 

 

CS'= 544 

S
85

CS'=CS-(CLS'+CRS'+CMS'+CLRS+CLMS+CRMS+CLRMS) 

Table G-2. Breakdown of Locomotive Conspicuity Overlap by Year 

Year L' LC' LS' LR' LM' LCS' LCR' LCM' LSR' LSM' LRM' LCSR LSRM LRMC LMCS LCSRM

1994 745 295 34 162 3 15 62 0 3 0 0 1 0 0 0 0 

1995 678 240 26 182 1 10 66 0 8 1 0 2 0 0 0 0 

1996 589 224 23 166 2 15 40 0 6 0 0 3 0 1 0 0 

1997 530 157 31 105 24 4 28 8 4 0 9 1 0 0 0 0 

1998 527 106 21 104 32 3 29 5 3 0 8 1 0 2 1 0 

1999 525 123 10 101 8 3 20 3 2 0 2 0 0 1 0 0 

2000 609 110 16 103 10 5 22 4 2 0 4 0 0 0 0 0 

2001 512 98 13 104 13 1 21 2 1 0 4 0 0 3 0 0 

2002 459 99 20 117 12 3 21 1 2 0 4 0 0 1 0 0 

2003 465 93 12 99 12 4 6 0 3 0 1 0 0 0 1 0 

L LC LS LR LM LCS LCR LCM LSR LSM LRM LCSR LSRM LRMC LMCS LCSRM

9236 1964 314 1640 183 73 331 33 42 3 40 8 0 8 2 0 

 63 315 23 34 1 32 

 1545 206 1243 117 

5639 

L'= 5639 

L'=L-(LC'+LS'+LR'+LM'+LCS'+LCR'+LCM'+LSR'+LSM'+LRM'+LCSR+LSRM+LRMC+LMCS+LCSRM) 

 

LCS'= 63 

LCS'=LCS-(LCSR+LMCS+LCSRM) 

 

LCR'= 315 

LCR'=LCR-(LCSR+LRMC+LCSRM) 

 

LCM'= 23 

LCM'=LCM-(LRMC+LMCS+LCSRM) 

 

LSR'= 34 

LSR'=LSR-(LCSR+LSRM+LCSRM) 

 

LSM'= 1 

LSM'=LSM-(LSRM+LMCS+LCSRM) 

 

LRM'= 32 

LRM'=LRM-(LSRM+LRMC+LCSRM) 

 

LC'= 1545 

LC'=LC-(LCS'+LCR'+LCM'+LCSR+LRMC+LMCS+LCSRM) 

 

LS'= 206 

LS'=LS-(LCS'+LSR'+LSM'+LCSR+LSRM+LMCS+LCSRM) 

 

LR'= 1243 

LR'=LR-(LCR'+LSR'+LRM'+LCSR+LSRM+LRMC+LCSRM) 

 

LM'= 117 
86

LM'=LM-(LCM'+LSM'+LRM'+LSRM+LRMC+LMCS+LCSRM) 

Table G-3. Breakdown of Grade Crossing Maintenance Rule Overlap by Year 

Year M' MS' MR' MC' ML' MSR' MSC' MSL' MRC' MRL' MCL' MSRC MSCL MRCL MSRL MSRCL

1994 5 0 0 2 3 0 0 0 0 0 0 0 0 0 0 0 

1995 3 0 0 2 1 0 0 1 0 0 0 0 0 0 0 0 

1996 2 1 1 2 3 0 1 0 0 -1 0 0 0 1 0 0 

1997 104 6 5 42 25 0 5 0 1 8 8 0 0 0 0 0 

1998 89 8 5 36 34 0 5 0 0 6 5 0 1 2 0 0 

1999 56 4 2 16 8 0 1 0 1 2 3 0 0 1 0 0 

2000 43 7 1 14 10 0 1 0 1 4 4 0 0 0 0 0 

2001 44 2 2 10 14 0 3 0 0 3 2 0 0 3 0 0 

2002 43 3 5 8 13 0 0 0 0 3 1 0 0 1 0 0 

2003 41 3 1 12 12 0 2 0 0 1 0 0 1 0 0 0 

M MS MR MC ML MSR MSC MSL MRC MRL MCL MSRC MSCL MRCL MSRL MSRCL

834 55 59 198 183 0 20 3 11 34 33 0 2 8 0 0 

 0 18 1 3 26 23 

 34 22 144 123 

430 

M'= 430 

M'=M-(MS'+MR'+MC'+ML'+MSR'+MSC'+MSL'+MRC'+MRL'+MCL'+MSRC+MSCL+MRCL+MSRL+MSRCL) 

 

MSR'= 0 

MSR'=MSR-(MSRC+MSRL+MSRCL) 

 

MSC'= 18 

MSC'=MSC-(MSRC+MSCL+MSRCL) 

 

MSL'= 1 

MSL'=MSL-(MSCL+MSRL+MSRCL) 

 

MRC'= 3 

MRC'=MRC-(MSRC+MRCL+MSRCL) 

 

MRL'= 26 

MRL'=MRL-(MRCL+MSRL+MSRCL) 

 

MCL'= 23 

MCL'=MCL-(MSCL+MRCL+MSRCL) 

 

MS'= 34 

MS'=MS-(MSR'+MSC'+MSL'+MSRC+MSCL+MSRL+MSRCL) 

 

MR'= 22 

MR'=MR-(MSR'+MRC'+MRL'+MSRC+MRCL+MSRL+MSRCL) 

 

MC'= 144 

MC'=MC-(MSC'+MRC'+MCL'+MSRC+MSCL+MRCL+MSRCL) 

 

ML'= 123 
87

ML'=ML-(MSL'+MRL'+MCL'+MSCL+MRCL+MSRL+MSRCL) 

Table G-4. Breakdown of More Reliable Motor Vehicles Overlap by Year 

Year R' RM' RC' RL' RS' RCL' RCM' RCS' RLM' RLS' RSM' RCLS RCLM RCMS RMSL RCLMS

1994 116 0 62 162 3 62 0 3 0 3 0 1 0 0 0 0 

1995 105 0 66 182 4 66 0 5 0 8 0 2 0 0 0 0 

1996 85 0 63 166 5 40 0 1 0 6 0 3 1 0 0 0 

1997 64 4 30 105 4 28 1 2 9 4 0 1 0 0 0 0 

1998 50 3 24 104 1 29 0 2 8 3 0 1 2 0 0 0 

1999 76 2 36 101 2 20 1 0 2 2 0 0 1 0 0 0 

2000 63 1 30 103 4 22 1 1 4 2 0 0 0 0 0 0 

2001 65 1 27 104 2 21 0 0 4 1 0 0 3 0 0 0 

2002 51 4 38 117 3 21 0 0 4 2 0 0 1 0 0 0 

2003 52 1 18 99 0 6 0 2 1 3 0 0 0 0 0 0 

R RM RC RL RS RCL RCM RCS RLM RLS RSM RCLS RCLM RCMS RMSL RCLMS

2824 59 744 1640 86 331 11 24 40 42 0 8 8 0 0 0 

 315 3 16 32 34 0 

 16 394 1243 28 

727 

R'= 727 

R'=R-(RM'+RC'+RL'+RS'+RCL'+RCM'+RCS'+RLM'+RLS'+RSM'+RCLS+RCLM+RCMS+RMSL+RCLMS) 

 

RCL'= 315 

RCL'=RCL-(RCLS+RCLM+RCLMS) 

 

RCM'= 3 

RCM'=RCM-(RCLM+RCMS+RCLMS) 

 

RCS'= 16 

RCS'=RCS-(RCLS+RCMS+RCLMS) 

 

RLM'= 32 

RLM'=RLM-(RCLM+RMSL+RCLMS) 

 

RLS'= 34 

RLS'=RLS-(RCLS+RMSL+RCLMS) 

 

RSM'= 0 

RSM'=RSM-(RCMS+RMSL+RCLMS) 

 

RM'= 16 

RM'=RM-(RCM'+RLM'+RSM'+RCLM+RCMS+RMSL+RCLMS) 

 

RC'= 394 

RC'=RC-(RCL'+RCM'+RCS'+RCLS+RCLM+RCMS+RCLMS) 

 

RL'= 1243 

RL'=RL-(RCL'+RLM'+RLS'+RCLS+RCLM+RMSL+RCLMS) 

 

RS'= 28 
88

RS'=RS-(RCS'+RLS'+RSM'+RCLS+RCMS+RMSL+RCLMS) 

Table G-5. Breakdown of Sight Lines Clearance Overlap by Year 

Year S' SM' SR' SC' SL' SRM' SMC' SML' SRC' SRL' SCL' SMRC SMCL SRCL SLMR SMRCL

1994 137 0 3 98 34 0 0 0 3 3 15 0 0 1 0 0 

1995 109 0 4 85 26 0 0 1 5 8 10 0 0 2 0 0 

1996 107 1 5 80 23 0 1 0 1 6 15 0 0 3 0 0 

1997 116 6 4 70 31 0 5 0 2 4 4 0 0 1 0 0 

1998 117 8 1 54 21 0 5 0 2 3 3 0 1 1 0 0 

1999 76 4 2 48 10 0 1 0 0 2 3 0 0 0 0 0 

2000 99 7 4 35 16 0 1 0 1 2 5 0 0 0 0 0 

2001 74 2 2 37 13 0 3 0 0 1 1 0 0 0 0 0 

2002 61 3 3 24 20 0 0 0 0 2 3 0 0 0 0 0 

2003 63 3 0 23 12 0 2 0 2 3 4 0 1 0 0 0 

S SM SR SC SL SRM SMC SML SRC SRL SCL SMRC SMCL SRCL SLMR SMRCL

1923 55 86 661 314 0 20 3 24 42 73 0 2 8 0 0 

 0 18 1 16 34 63 

 34 28 554 206 

959 

S'= 959 

S'=S-(SM'+SR'+SC'+SL'+SRM'+SMC'+SML'+SRC'+SRL'+SCL'+SMRC+SMCL+SRCL+SLMR+SMRCL) 

 

SRM'= 0 

SRM'=SRM-(SMRC+SLMR+SMRCL) 

 

SMC'= 18 

SMC'=SMC-(SMRC+SMCL+SMRCL) 

 

SML'= 1 

SML'=SML-(SMCL+SLMR+SMRCL) 

 

SRC'= 16 

SRC'=SRC-(SMRC+SRCL+SMRCL) 

 

SRL'= 34 

SRL'=SRL-(SRCL+SLMR+SMRCL) 

 

SCL'= 63 

SCL'=SCL-(SMCL+SRCL+SMRCL) 

 

SM'= 34 

SM'=SM-(SRM'+SMC'+SML'+SMRC+SMCL+SLMR+SMRCL) 

 

SR'= 28 

SR'=SR-(SRM'+SRC'+SRL'+SMRC+SRCL+SLMR+SMRCL) 

 

SC'= 554 

SC'=SC-(SMC'+SRC'+SCL'+SMRC+SMCL+SRCL+SMRCL) 

 

SL'= 206 
89

SL'=SL-(SML'+SRL'+SCL'+SMCL+SRCL+SLMR+SMRCL) 
90

Appendix H. 

 Kendall Partial Rank Correlation for Crossing Closure
91

Kendall Partial Rank Correlation Coefficient Methodology 

Subject a b c d 

Rank on Z 1 2 3 4 

Rank on X 3 1 2 4 

Rank on Y 2 1 3 4 

A + is assigned to the pairs in which the lower rank precedes the higher. A - is assigned 

to the pairs in which the higher rank precedes the lower. 

Pair (a,b) (a,c) (a,d) (b,c) (b,d) (c,d) 

Z + + + + + + 

X - - + + + + 

Y - + + + + + 

If Z is + and X is +, then X’s sign agrees with Z’s sign. If Z is + and Y is -, then Y’s sign 

disagrees with Z’s sign. 

Y pairs whose sign Y pairs whose sign Total 

agrees with Z’s sign disagrees with Z’s 

sign 

X pairs whose sign A B A+B 

agrees with Z’s sign 

X pairs whose sign C D C+D 

disagrees with Z’s 

sign 

Total A+C B+D ⎛ N ⎞ ⎜ ⎟ ⎝ 2 ⎠

AD − BC

τxy,z = 

(A + B)(C + D)(A + C)(B + D)
92

Table H-1. The Eight FRA Regions 

Region # Region Name States in the Region 

1 Northeast ME, NH, VT, MA, NY, RI, CT, NJ 

2 Middle Atlantic PA, OH, WV, VA, MD, DE 

3 Southeast KY, TN, NC, SC, GA, AL, MS, FL 

4 North Central MN, WI, IL, IN, MI 

5 South NM, TX, OK, AR, LA 

6 Central CO, NE, KS, IA, MO 

7 Southwest CA, NV, UT, AZ 

8 Northwest AK, WA, OR, ID, MT, WY, ND, SD 
93

Table H-2. Kendall Partial Ranking for Year 1994 

1994 1994-95 

Crossings Incident 1994 Number Rank Rank Rank 

Region Closed Reduction Crossings Closed Reduction Crossings

R3 1249 63 52928 1 3 1 

R4 538 122 50352 4 1 2 

R6 628 28 40334 3 5 3 

R5 450 84 38998 5 2 4 

R2 679 35 31239 2 4 5 

R8 322 -6 30127 6 8 6 

R7 89 4 16856 8 7 7 

R1 153 16 15283 7 6 8 

τxy,z 0.1615146

Table H-3. Kendall Partial Ranking for Year 1995 

1995 1995-96 1995 

Crossings Incident Number Rank Rank Rank 

Region Closed Reduction Crossings Closed Reduction Crossings

R3 1123 86 52078 1 1 1 

R4 544 86 49656 4 1 2 

R6 258 38 40128 6 5 3 

R5 529 73 38213 5 4 4 

R2 1015 84 30263 2 3 5 

R8 627 3 29589 3 7 6 

R7 156 6 16805 7 6 7 

R1 15 0 15279 8 8 8 

τxy,z 0.4714045
94

Table H-4. Kendall Partial Ranking for Year 1996 

1996 1996-97 1996 

Crossings Incident Number Rank Rank Rank 

Region Closed Reduction Crossings Closed Reduction Crossings

R3 1287 105 51573 3 1 1 

R4 1470 80 49341 2 2 2 

R6 605 36 39662 4 6 3 

R5 439 37 37777 5 5 4 

R2 1858 20 29477 1 7 5 

R8 301 49 29377 6 4 6 

R7 88 59 16749 7 3 7 

R1 73 6 15279 8 8 8 

τxy,z -0.2948839

Table H-5. Kendall Partial Ranking for Year 1997 

1997 1997-98 1997 

Crossings Incident Number Rank Rank Rank 

Region Closed Reduction Crossings Closed Reduction Crossings

R3 2099 -6 50851 1 7 1 

R4 833 107 48788 2 2 2 

R6 392 70 38710 4 3 3 

R5 331 144 37537 5 1 4 

R8 285 10 29374 6 6 5 

R2 596 36 28966 3 4 6 

R7 105 -39 16682 8 8 7 

R1 169 35 14883 7 5 8 

τxy,z 0.0895622
95

H-6. Kendall Partial Ranking for Year 1998 

1998 1998-99 1998 

Crossings Incident Number Rank Rank Rank 

Region Closed Reduction Crossings Closed Reduction Crossings

R3 775 53 49330 2 1 1

R4 593 -10 48208 3 5 2

R6 823 -19 38558 1 8 3

R5 484 -18 37196 4 7 4

R8 155 45 29189 7 2 5

R2 408 -7 28661 6 3 6

R7 6 -9 16661 8 4 7

R1 421 -16 14897 5 6 8

τxy,z -0.452267

H-7. Kendall Partial Ranking for Year 1999 

1999 1999-2000 1999 

Crossings Incident Number Rank Rank Rank 

Region Closed Reduction Crossings Closed Reduction Crossings

R3 557 30 49475 3 2 1

R4 649 -30 48081 2 7 2

R6 759 17 37704 1 3 3

R5 100 -37 37092 8 8 4

R2 329 -3 28800 6 4 5

R8 405 -11 28644 5 5 6

R7 110 39 16556 7 1 7

R1 526 -18 14767 4 6 8

τxy,z -0.0157103
96

H-8. Kendall Partial Ranking for Year 2000 

2000 2000-01 2000 

Crossings Incident Number Rank Rank Rank 

Region Closed Reduction Crossings Closed Reduction Crossings

R3 252 13 49091 6 5 1 

R4 2002 99 45777 1 1 2 

R6 429 34 37738 3 3 3 

R5 853 87 36843 2 2 4 

R2 301 26 28659 5 4 5 

R8 368 4 28236 4 7 6 

R7 53 -9 16557 8 8 7 

R1 197 11 14507 7 6 8 

τxy,z 0.7302967

H-9. Kendall Partial Ranking for Year 2001 

2001 2001-02 2001 

Crossings Incident Number Rank Rank Rank 

Region Closed Reduction Crossings Closed Reduction Crossings

R3 252 -6 49091 6 8 1 

R4 2002 56 45777 1 2 2 

R6 429 12 37738 3 4 3 

R5 853 59 36843 2 1 4 

R2 301 -4 28659 5 6 5 

R8 368 -5 28236 4 7 6 

R7 53 50 16557 8 3 7 

R1 197 -2 14507 7 5 8 

τxy,z 0.3162278
97

H-10. Kendall Partial Ranking for Year 2002 

2002 2002-03 2002 

Crossings Incident Number Rank Rank Rank 

Region Closed Reduction Crossings Closed Reduction Crossings

R3 287 61 48946 4 1 1 

R4 1176 52 44409 1 4 2 

R6 544 54 37241 2 3 3 

R5 407 57 36258 3 2 4 

R2 111 -35 28156 8 8 5 

R8 281 5 28081 5 5 6 

R7 134 -15 16493 7 6 7 

R1 278 -26 13814 6 7 8 

τxy,z 0.3015113

H-11. Kendall Partial Ranking for Year 2003 

2003 2003-04 2003 

Crossings Incident Number Rank Rank Rank 

Region Closed Reduction Crossings Closed Reduction Crossings

R3 335 -56 48733 5 8 1 

R4 206 -43 43177 7 6 2 

R6 954 -51 35231 2 7 3 

R5 401 -23 34610 4 5 4 

R2 1017 3 27908 1 3 5 

R8 275 12 27164 6 1 6 

R7 61 0 16029 8 4 7 

R1 727 11 13430 3 2 8 

τxy,z 0 
98

Appendix I. 

 Kendall Partial Rank Correlation for Warning Device Upgrades
99

I-1. Kendall Partial Ranking for Year 1994 

1994 1994-95 1994 

Crossings Incident Number Rank Rank Rank 

Region Upgraded Reduction Crossings Closed Reduction Crossings

R3 358.28 48 52928 1 2 1 

R4 173.56 71 50352 2 1 2 

R6 150.63 12 40334 5 5 3 

R5 160.95 27 38998 4 3 4 

R2 169.64 4 31239 3 6 5 

R8 16.62 -5 30127 8 7 6 

R7 22.96 20 16856 7 4 7 

R1 23.86 -5 15283 6 7 8 

τxy,z 0.2110267

I-2. Kendall Partial Ranking for Year 1995 

1995 1995-96 1995 

Crossings Incident Number Rank Rank Rank 

Region Upgraded Reduction Crossings Closed Reduction Crossings

R3 287.87 24 52078 1 3 1 

R4 137.37 6 49656 3 6 2 

R6 99.47 28 40128 5 2 3 

R5 127.42 20 38213 4 4 4 

R2 220.8 46 30263 2 1 5 

R8 64.52 12 29589 6 5 6 

R7 52.85 -6 16805 7 7 7 

R1 22.74 -9 15279 8 8 8 

τxy,z 0.4195732
100

I-3. Kendall Partial Ranking for Year 1996 

1996 1996-97 1996 

Crossings Incident Number Rank Rank Rank 

Region Upgraded Reduction Crossings Closed Reduction Crossings

R3 156.32 37 51573 2 1 1 

R4 136.23 19 49341 4 2 2 

R6 113.09 -26 39662 5 7 3 

R5 151.5 -4 37777 3 5 4 

R2 294 -34 29477 1 8 5 

R8 36.17 15 29377 6 3 6 

R7 12.87 14 16749 7 4 7 

R1 9.26 -4 15279 8 5 8 

τxy,z -0.1730567

I-4. Kendall Partial Ranking for Year 1997 

1997 1997-98 1997 

Crossings Incident Number Rank Rank Rank 

Region Upgraded Reduction Crossings Upgraded Reduction Crossings

R3 472.94 7 50851 1 5 1 

R4 141.62 40 48788 3 1 2 

R6 78.8 13 38710 6 4 3 

R5 118.16 -5 37537 4 6 4 

R8 16.78 -5 29374 7 6 5 

R2 192.05 30 28966 2 2 6 

R7 13.95 -18 16682 8 8 7 

R1 94.4 14 14883 5 3 8 

τxy,z 0.4624973
101

I-5. Kendall Partial Ranking for Year 1998 

1998 1998-99 1998 

Crossings Incident Number Rank Rank Rank 

Region Upgraded Reduction Crossings Upgraded Reduction Crossings

R3 199.12 -12 49330 1 4 1 

R4 188.97 -23 48208 2 6 2 

R6 40.38 -26 38558 7 7 3 

R5 98.42 -45 37196 4 8 4 

R8 63.92 -1 29189 5 1 5 

R2 143.25 -3 28661 3 2 6 

R7 3.06 -15 16661 8 5 7 

R1 62.12 -3 14897 6 2 8 

τxy,z 0.1666667

I-6. Kendall Partial Ranking for Year 1999 

1999 1999-2000 1999 

Crossings Incident Number Rank Rank Rank 

Region Upgraded Reduction Crossings Upgraded Reduction Crossings

R3 239.55 49 49475 1 1 1 

R4 231.85 -20 48081 2 8 2 

R6 111.93 32 37704 5 2 3 

R5 153.18 -9 37092 3 6 4 

R2 146.8 -6 28800 4 4 5 

R8 94.06 -8 28644 6 5 6 

R7 14.53 9 16556 8 3 7 

R1 39.15 -9 14767 7 6 8 

τxy,z -0.2786522
102

I-7. Kendall Partial Ranking for Year 2000 

2000 2000-01 2000 

Crossings Incident Number Rank Rank Rank 

Region Upgraded Reduction Crossings Upgraded Reduction Crossings

R3 122.54 -28 49091 3 8 1 

R4 170.07 48 45777 2 2 2 

R6 98.55 25 37738 5 3 3 

R5 271.13 58 36843 1 1 4 

R2 120.78 13 28659 4 4 5 

R8 37.24 -3 28236 8 7 6 

R7 39.77 1 16557 7 6 7 

R1 64.64 4 14507 6 5 8 

τxy,z 0.5705443

I-8. Kendall Partial Ranking for Year 2001 

2001 2001-02 2001 

Crossings Incident Number Rank Rank Rank 

Region Upgraded Reduction Crossings Upgraded Reduction Crossings

R3 182.87 -28 49091 3 8 1 

R4 218.18 48 45777 2 2 2 

R6 63.63 25 37738 6 3 3 

R5 282.15 58 36843 1 1 4 

R2 57.9 13 28659 7 4 5 

R8 113.79 -3 28236 4 7 6 

R7 18.38 1 16557 8 6 7 

R1 97.54 4 14507 5 5 8 

τxy,z 0.1766043
103

I-9. Kendall Partial Ranking for Year 2002 

2002 2002-03 2002 

Crossings Incident Number Rank Rank Rank 

Region Upgraded Reduction Crossings Upgraded Reduction Crossings

R3 31.1 11 48946 6 4 1 

R4 199.35 23 44409 2 2 2 

R6 67.2 21 37241 3 3 3 

R5 202.48 33 36258 1 1 4 

R2 58.32 -39 28156 4 8 5 

R8 33.66 -6 28081 5 6 6 

R7 4.45 -17 16493 8 7 7 

R1 28.11 -4 13814 7 5 8 

τxy,z 0.518545

I-10. Kendall Partial Ranking for Year 2003 

2003 2003-04 2003 

Crossings Incident Number Rank Rank Rank 

Region Upgraded Reduction Crossings Upgraded Reduction Crossings

R3 30.29 -42 48733 5 8 1 

R4 83.14 -11 43177 4 6 2 

R6 86.08 -12 35231 3 7 3 

R5 204.1 -4 34610 1 4 4 

R2 88.6 1 27908 2 2 5 

R8 27.11 11 27164 6 1 6 

R7 5.53 -2 16029 8 3 7 

R1 14.84 -7 13430 7 5 8 

τxy,z 0.0860663
104

Appendix J. 

 Railroad Mergers
105

J-1. Incidents at Public Crossings by Railroad, 1994 and 2003 

 1994 2003

 Railroad Incidents Cumulative Railroad Incidents Cumulative

Percent Percent 

 

1 UP 896 19.8 CSX 496 19.0

2 NS 649 34.2 UP 462 36.7

3 CSX 568 46.7 NS 418 52.7

4 BNSF 354 54.5 BNSF 348 66.1

5 KCS 171 58.3 KCS 98 69.8

6 BN 166 62.0 ATK 83 73.0

7 CR 133 64.9 IC 83 76.2

8 IC 110 67.4 WC 45 77.9

9 NW 102 69.6 GTW 31 79.1

10 SP 97 71.8 SOO 27 80.2

11 WC 90 73.8 CRSH 22 81.0

12 CNW 83 75.6 FEC 21 81.8

13 ATSF 74 77.2 ICE 17 82.5

14 ATK 67 78.7 NIRC 17 83.1

15 SOO 56 80.0 SCAX 17 83.8

16 GTW 46 81.0 WE 17 84.4

17 CC 35 81.7 CC 12 84.9

18 SSW 32 82.5 AM 9 85.2

19 FEC 28 83.0 

20 WE 28 83.7 

21 WSOR 24 84.2 

22 NIRC 22 84.7 

23 IHB 18 85.1 

 Sub total 

 Other 3,849 85.1% 2,223 85.2%

RRs 

 674 14.9% 386 14.8%

 Total 

 4,523 100.0% 2,609 100.0%
106

J-2. Public Crossings Incidents in California by Railroad 

Railroad 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

UP 86 74 86 71 90 94 60 63 49 47 

BNSF 27 27 31 34 38 35 47 44 38 24 

SP 21 13 17 

ATSF 14 11 5 

ATK 9 15 4 11 14 22 20 27 10 20 

SJVR 8 4 8 3 1 4 1 3 1 5 

SCAX 7 15 14 8 12 10 12 13 14 16 

TVRR 3 3 1 1 1 

CCT 2 1 1 

CFNR 2 2 2 2 2 2 3 

ARZC 1 1 1 

OTR 1 1 

SCBG 1 1 

SDNX 1 1 2 1 2 2 2 

AL 1 1 

CORP 1 1 2 

CWR 1 

LAJ 1 

MET 1 1 1 

NVWT 2 1 

PCMZ 1 1 1 1 

PHL 1 1 

RSIX 2 1 

SDTI 1 

SERA 1 

SMV 2 

STE 1 1 

VCY 2 

YSLR 1 2 

 

Total 183 170 172 135 160 176 141 163 117 120 

Railroads 

reporting 14 14 13 11 9 13 6 14 8 11 

incidents 
107

J-3. Incidents for Texas by Railroad, 1994 through 2003 

Railroad 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 

 

UP 293 226 217 214 144 191 186 182 155 148 

BNSF 55 52 44 70 67 64 76 70 58 55 

SP 34 39 32 

ATSF 22 13 13 

KCS 22 26 24 38 34 30 19 28 11 16 

BN 11 10 5 

RVSC 6 5 3 6 3 2 1 2 6 7 

SSW 6 11 9 

TM 6 2 4 11 11 9 9 5 16 5 

TNER 6 1 1 2 1 

DGNO 5 5 4 3 3 9 7 4 5 6 

AUAR 4 1 1 1 3 2 2 

PTRA 4 5 4 4 3 3 6 4 6 

TRE 4 1 1 1 1 1 2 

ANR 3 1 1 2 1 

FWWR 3 8 8 4 1 9 6 3 6 3 

TIBR 3 7 1 2 6 2 6 4 4 2 

SWGR 2 1 

WTLR 2 1 4 4 2 

ATK 1 2 4 3 3 3 5 3 3 

CHRC 1 

CRLX 1 1 

CYCY 1 

DART 1 1 1 1 1 1 

PVS 1 

SRN 1 1 1 2 1 

SSC 1 1 1 

TCT 1 1 1 1 2 

TELX 1 

TXPF 1 3 1 1 1 

ANR 1 

ATCX 1 

BRG 1 1 2 

GCSR 1 1 1 

GVSR 2 1 1 

HBT 2 1 

KRR 1 1 

LHRR 1 

PCN 1 

PNR 1 1 1 1 

SAW 1 

SO 1 1 1 

SW 2 

TN 1 1 

TNMR 1 1 

TREX 1 2 

TXNW 1 

TXTX 1 

WATX 1 

WI 1 

 

Total 502 428 391 368 289 324 329 313 277 258 
108

J-4. Public Crossing Incidents by State, Ranked by Percent Change 

 from 1994 to 2003 

State % 1994 2003 State % 1994 2003 

Change Incidents Incidents Change Incidents Incidents

 

 

CT -100.0% 4 0 LA -34.7% 202 132

NV -85.7% 7 1 CA -34.4% 183 120

AK -66.7% 3 1 VT -33.3% 3 2

WI -60.8% 176 69 CO -32.6% 43 29

IA -60.5% 157 62 UT -30.8% 26 18

WY -57.1% 7 3 SC -29.9% 87 61

NC -55.9% 145 64 VA -28.6% 56 40

NM -55.6% 18 8 GA -28.2% 149 107

NE -54.7% 86 39 WV -26.8% 41 30

ID -53.8% 39 18 TN -25.7% 101 75

MO -53.4% 116 54 FL -19.8% 116 93

AL -53.1% 177 83 MT -11.1% 18 16

MN -52.2% 138 66 KY -10.0% 80 72

SD -51.7% 29 14 MA -6.3% 16 15

IN -50.9% 275 135 NY -2.7% 37 36

IL -50.8% 309 152 PA -2.7% 73 71

NH -50.0% 2 1 DC 0.0% 0 0

OH -49.8% 229 115 HI 0.0% 0 0

MS -49.7% 167 84 ND 10.0% 20 22

AR -48.9% 139 71 NJ 11.4% 35 39

TX -48.6% 502 258 DE 20.0% 5 6

WA -48.3% 60 31 AZ 29.2% 24 31

KS -46.8% 94 50 MD 50.0% 12 18

OK -44.8% 116 64 ME 60.0% 5 8

OR -42.1% 38 22 RI +100.0% 0 2

MI -36.1% 158 101 

 

 Total -42.3% 4,523 2,609
109

J-5. Public Crossings by State, Ranked by Percent Change from 1994 to 2003 

State % 1994 2003 State % 1994 2003 

Change Crossings Crossings Change Crossings Crossings

 

 

DC -362.5% 37 8 NE -8.4% 4,058 3,744

MA -45.7% 1,192 818 MI -7.8% 5,791 5,374

WY -33.5% 530 397 VA -7.3% 2,201 2,051

KS -27.0% 7,912 6,231 GA -6.9% 6,197 5,796

WV -25.1% 1,972 1,576 SC -6.6% 3,111 2,918

IL -25.0% 10,265 8,213 KY -6.4% 2,639 2,480

NH -24.8% 503 403 TN -5.9% 3,371 3,183

NJ -22.2% 1,862 1,524 OH -5.3% 6,713 6,374

AL -19.2% 4,008 3,362 AR -5.2% 3,325 3,162

ID -19.1% 1,556 1,307 MO -4.9% 4,872 4,643

WI -17.6% 4,899 4,167 ME -4.9% 882 841

PA -16.9% 5,599 4,788 FL -4.4% 4,077 3,905

IA -14.2% 5,290 4,632 MT -4.2% 1,533 1,471

WA -13.3% 3,018 2,664 AZ -4.2% 941 903

NY -12.0% 3,279 2,928 MN -3.9% 5,218 5,024

MS -11.4% 3,028 2,717 OR -3.3% 2,343 2,269

OK -11.4% 4,627 4,155 CA -2.9% 7,988 7,761

TX -11.2% 12,706 11,431 MD -0.3% 693 691

ND -11.1% 4,631 4,167 SD -0.3% 2,137 2,131

NC -10.7% 4,875 4,405 AK 0.0% 227 227

NM -10.3% 815 739 CT 0.0% 370 370

CO -10.1% 2,076 1,885 VT 0.0% 496 496

IN -10.0% 6,678 6,071 NV 3.7% 288 299

UT -9.0% 1,009 926 DE 12.1% 269 306

LA -8.5% 3,770 3,475 HI 25.0% 6 8

RI -8.5% 128 118 

 

 Totals -9.9% 166,011 149,534
110

J-6. Changes in Public Incidents and Public Crossings by State 

State % % State % % 

Change Change Change Change 

Incidents Crossings Incidents Crossings 

 

CT -100.0% 0.0% LA -34.7% -8.5% 

NV -85.7% 3.7% CA -34.4% -2.9% 

AK -66.7% 0.0% VT -33.3% 0.0% 

WI -60.8% -17.6% CO -32.6% -10.1% 

IA -60.5% -14.2% UT -30.8% -9.0% 

WY -57.1% -33.5% SC -29.9% -6.6% 

NC -55.9% -10.7% VA -28.6% -7.3% 

NM -55.6% -10.3% GA -28.2% -6.9% 

NE -54.7% -8.4% WV -26.8% -25.1% 

ID -53.8% -19.1% TN -25.7% -5.9% 

MO -53.4% -4.9% FL -19.8% -4.4% 

AL -53.1% -19.2% MT -11.1% -4.2% 

MN -52.2% -3.9% KY -10.0% -6.4% 

SD -51.7% -0.3% MA -6.3% -45.7% 

IN -50.9% -10.0% NY -2.7% -12.0% 

IL -50.8% -25.0% PA -2.7% -16.9% 

NH -50.0% -24.8% DC 0.0% -362.5% 

OH -49.8% -5.3% HI 0.0% 25.0% 

MS -49.7% -11.4% ND 10.0% -11.1% 

AR -48.9% -5.2% NJ 11.4% -22.2% 

TX -48.6% -11.2% DE 20.0% 12.1% 

WA -48.3% -13.3% AZ 29.2% -4.2% 

KS -46.8% -27.0% MD 50.0% -0.3% 

OK -44.8% -11.4% ME 60.0% -4.9% 

OR -42.1% -3.3% RI +100.0% -8.5% 

MI -36.1% -7.8%
111

Table J-7. The Eight FRA Regions 

Region # Region Name States in the Region 

1 Northeast ME, NH, VT, MA, NY, RI, CT, NJ 

2 Middle Atlantic PA, OH, WV, VA, MD, DE 

3 Southeast KY, TN, NC, SC, GA, AL, MS, FL 

4 North Central MN, WI, IL, IN, MI 

5 South NM, TX, OK, AR, LA 

6 Central CO, NE, KS, IA, MO 

7 Southwest CA, NV, UT, AZ 

8 Northwest AK, WA, OR, ID, MT, WY, ND, SD 
112

 J-8. Percent Decline in Public and Private Incidents by FRA Region 

FRA 1994 2003 Percent 

Region Incidents Incidents Change 

 

1 134 117 -12.7% 

7 283 197 -30.4% 

2 487 330 -32.2% 

3 1,119 759 -32.2% 

8 260 154 -40.8% 

6 526 306 -41.8% 

5 1,074 610 -43.2% 

4 1,116 590 -47.1% 

 

total 4,999 3,063 -38.7%

J-9. Public and Private Incidents for First Quarter (January, February, March) of 

Year, 1994 through 2006 

Year Incidents Percent

Decline 

 

1994 1,437 

1995 1,245 -13.4%

1996 1,200 -3.6%

1997 1,060 -11.7%

1998 918 -13.4%

1999 895 -2.5%

2000 899 0.4%

2001 865 -3.8%

2002 777 -10.2%

2003 756 -2.7%

2004 766 1.3%

2005 724 -5.5%

2006 696 -3.9%

J-10. Incidents as Public and Private Crossings, 1980 through 2005 

Year Incidents Year Incidents Year Incidents Year Incidents 

 

1980 10,796 1987 6,426 1994 4,979 2001 3,237 

1981 9,461 1988 6,617 1995 4,633 2002 3,077 

1982 7,932 1989 6,526 1996 4,257 2003 2,977 

1983 7,305 1990 5,715 1997 3,865 2004 3,072 

1984 7,456 1991 5,388 1998 3,508 2005 3,035 

1985 7,073 1992 4,910 1999 3,489 2006 696* 

1986 6,513 1993 4,892 2000 3,502 

* 2006 incident counts are for months January, February, and March 
113

Appendix K. 

 Additional Sources 
114

Additional Sources 

Bowman, B.L. and Colson C., “Current State Practices and Recommendations for 

Improving the Rail-Highway Grade Crossing Program,” Alabama Highway 

Department, 1994. 

Burgess, M., “Contrasting Rural and Urban Fatal Crashes 1994-2003,” 

Washington, DC: National Highway Transportation Safety Administration, 

National Center for Statistics and Analysis, December 2005. 

Carroll, A.A. and Warren, J.D., “Closure of U.S. Highway-Rail Grade Crossings: 

Status Report (03-3800)”, prepared for the Transportation Research Board, 

Washington, DC, August 2001. 

Federal Railroad Administration, “Federal Railroad Administration Guide for 

Preparing Accident/Incident Reports,” Washington, DC: U.S. DOT/FRA, May 

2003. 

Gou, M. and Bellavigne-Ladoux, O., “Impact of Heavy Vehicles on Crossing 

Safety–Development of an Adapted Design Tool,” Ottawa, ON: Transport 

Canada, May 2003. 

Mead, K.M., “Highway-Railroad Grade Crossing Safety Issues; Statement of The 

Honorable Kenneth M. Mead, Inspector General,” Washington, DC: U.S. DOT, 

July 2005. 

National Highway Transportation Safety Administration, “National Automotive 

Sampling System (NASS), General Estimates System (GES), Analytical User’s 

Manual 1988-2004,” Washington, DC: National Highway Transporation Safety 

Administration, 2004. 

National Transportation Safety Board, “Safety at Passive Grade Crossings”, 

Washington, DC: National Transportation Safety Board, July 1998. 

Office of Safety, Federal Railroad Administration, “Report on High Risk 

Crossings and Mitigation Efforts by State,” Washington, DC: U.S. DOT/FRA, 

February 2002. 

Park, Y.J. and Saccomanno, F.F., “Evaluating Factors Affecting Safety at 

Highway-Railway Grade Crossings,” prepared for the Transportation Research 

Board, Washington, DC, November 2004. 

Saks, J. and Carroll, A., “North Carolina DOT Traffic Separation Studies Volume 

I–Assessment,” prepared for the Federal Railroad Administration, Cambridge, 

MA: U.S. DOT/FRA, September 2004. 
115

Stackhouse, S., “Effectiveness of Marketing Campaigns for Grade Crossing 

Safety,” Minneapolis, MN: University of Minnesota, Minneapolis and Minnesota 

Department of Transportation, September 1996. 

United States General Accounting Office, “Railroad Safety, Status of Efforts to 

Improve Railroad Crossing Safety,” Washington, DC: U.S. General Accounting 

Office, August 1995. 
116

References 

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Crossing Safety Action Plan,” Washington, DC: U.S. DOT, June 1994. 

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Crossings?,” Risk Analysis 25.4, November 2005, pages 867-881. 

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Use in Analyzing Highway-Rail Grade Crossing,”prepared for the Federal Railroad 

Administration, Cambridge, MA: U.S. DOT/FRA, March 2004. 

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Crossing and Grade Crossing Accident Inventories, an Analysis of the Potential 

Impacts from the Horn Sounding Requirement of the Swift Rail Development Act, 

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Statistics (1994-2003),” Washington, DC: U.S. DOT/FHWA, 2004. 

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Program, Office of the Inspector General”, Washington, DC: OIG, June 2004. 

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Crossings on the Empire Corridor”, prepared for the Federal Railroad Administration, 

Cambridge, MA: US DOT/FRA, August 2000. 

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Administration, Cambridge, MA: U.S. DOT/FRA, July 1995. 

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prepared for the Federal Railroad Administration, Reston, VA: U.S. DOT/FRA, 

November 1996. 
117

13. Farr, E.H., “Rail-Highway Crossing Resource Allocation Procedure–User's Guide, 

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U.S. DOT/FRA, August 1987. 

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119

Acronyms 

AADT average annual daily traffic 

ADAPT_X Accident Data Analytical Prospective Tool for HighwayGrade Crossing Incidents 

ATSF Atchison Topeka Santa Fe Railway 

BN Burlington Northern Railroad Co. 

BNSF Burlington Northern Santa Fe Railway Co. 

CDL Commercial Driver’s License 

CNW Chicago Northwestern Railway Co. 

Crossing Inventory National Highway-Rail Crossing Inventory 

FARS Fatality Analysis Reporting System 

FHWA Federal Highway Administration 

FMCSA Federal Motor Carrier Safety Administration 

FRA Federal Railroad Administration 

RAIRS Grade Crossing Rail Accident Incident Reporting System–Highway-Rail 

Grade Crossing 

RAIRS Rail Equipment Railroad Accident Incident Reporting System–Rail 

Equipment 

SP Southern Pacific Transportation Co. 

TMT train miles traveled 

TRB Transportation Research Board 

UP Union Pacific Railroad Co. 

USDOT U.S. Department of Transportation 

VMT vehicle miles traveled 

Volpe Center John A. Volpe National Transportation Systems Center 
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Glossary 

Exposure–A measure of grade crossing traffic that includes vehicle miles traveled, Class 

I train miles traveled, and the number of at-grade crossings 

Fatality Rate–The number of grade crossing fatalities per vehicle miles traveled 

Incident-Fatality Rate–The number of fatalities per grade crossing incident 

Incident-Injury Rate–The number of injuries per grade crossing incident 

Incident Rate–The number of grade crossing incidents per vehicle miles traveled 

Kendall Partial Rank Correlation Coefficient–A nonparametric method of partial 

correlation using ranks, in which the correlation between two variables is found with the 

third variable held constant 

Overlap–The number of incidents that can be attributed to behaviors associated with 

more than one of the success factors 

Pareto Diagram–Graphical tool used to summarize and display the relative importance of 

the differences between groups of data 

Percent Impact–Percentage of incidents, from 1994 to 2003, that can be attributed to 

behaviors that the factor was attempting to change 

Percent Reduction–Percentage of incidents reduced, from 1994 to 2003, which can be 

attributed to the safety countermeasures for a factor 

Success Factors–The safety initiatives that were the most successful in reducing incidents 

at highway-rail grade crossings during the years 1994 through 2003