DOT/FRA/ORD-21/31 Final Report | September 2021

Railroad Information Sharing Environment (RISE)
i 
i 

REPORT DOCUMENTATION 

PAGEREPORT DOCUMENTATION 

PAGE

Form Approved 

OMB No. 0704-0188 

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data 

sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other 

aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information 

Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision 

of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.

PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS.

1. REPORT DATE (17-06-2021) 2. REPORT TYPE

Technical Report

3. DATES COVERED (Sept 2019 TO June 

2021) 

4. TITLE AND SUBTITLE

RAILROAD INFORMATION SHARING ENVIRONMENT (RISE) PILOT PROJECT 

5a. CONTRACT NUMBER

693JJ619P000030 

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

Franz, Mark L. 

Tous, Ignacio 

Pack, Michael L.

5d. PROJECT NUMBER

5e. TASK NUMBER

5f. WORK UNIT NUMBER

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

University of Maryland – Center for Advanced Transportation Technology 

Technology Ventures Building 

5000 College Ave, Suite 2206 

College Park, MD 20742 

8. PERFORMING ORGANIZATION 

REPORT NUMBER 

N/A 

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

U.S. Department of Transportation 

Federal Railroad Administration

Office of Railroad Policy and Development

Office of Research, Development, and Technology

Washington, DC 20590

10. SPONSOR/MONITOR'S ACRONYM(S)

FRA

11. SPONSOR/MONITOR'S REPORT 

NUMBER(S) 

DOT/FRA/ORD-21/31

12. DISTRIBUTION/AVAILABILITY STATEMENT

This document is available to the public through the FRA website.

13. SUPPLEMENTARY NOTES

COR: Starr Kidda, Ph.D. 

14. ABSTRACT

Despite tremendous progress in railroad safety over the last 30 years, many safety statistics have plateaued. Thus, additional insights on the complex 

interaction between people, trains, infrastructure, and the operating environment are needed. While the Federal Railroad Administration (FRA) requires 

railroad agencies to regularly report on key safety metrics, there is additional data that agencies collect, but rarely share. Following similar successful data 

sharing projects in the aviation and motor vehicle industries, this FRA project established a secure, data-sharing and analysis platform titled the Railroad 

Information Sharing Environment (RISE). The RISE pilot project demonstrated the ability for FRA, railroads, and a trusted third-party data steward to share 

and analyze safety-sensitive data to improve railroad safety. This report documents the effort, challenges, lessons learned, and recommendations for 

building future phases of RISE as a data trust for the railroad industry. 

15. SUBJECT TERMS

Railroad safety, data trust, data analysis, data visualization 

16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF 

ABSTRACT 

UU 

18. NUMBER 

OF 

PAGES

42 

19a. NAME OF RESPONSIBLE PERSON

Mark L. Franz a. REPORT

U 

b. ABSTRACT

SAR

c. THIS PAGE

SAR

19b. TELEPHONE NUMBER (Include area code)

301-314-0422
ii

METRIC/ENGLISH CONVERSION FACTORS

ENGLISH TO METRIC METRIC TO ENGLISH

LENGTH (APPROXIMATE) LENGTH (APPROXIMATE)

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

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

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

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

1 kilometer (km) = 0.6 mile (mi)

AREA (APPROXIMATE) AREA (APPROXIMATE)

1 square inch (sq in, in2) = 6.5 square centimeters (cm2) 1 square centimeter (cm2) = 0.16 square inch (sq in, in2) 

1 square foot (sq ft, ft2) = 0.09 square meter (m2) 1 square meter (m2) = 1.2 square yards (sq yd, yd2)

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

1 square mile (sq mi, mi2) = 2.6 square kilometers (km2) 10,000 square meters (m2) = 1 hectare (ha) = 2.5 acres 

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

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

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

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

1 short ton = 2,000 pounds (lb) = 0.9 tonne (t) 1 tonne (t) =

=

1,000 kilograms (kg) 

1.1 short tons

VOLUME (APPROXIMATE) VOLUME (APPROXIMATE)

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

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

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

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

1 pint (pt) = 0.47 liter (l)

1 quart (qt) = 0.96 liter (l)

1 gallon (gal) = 3.8 liters (l) 

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

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

TEMPERATURE (EXACT) TEMPERATURE (EXACT)

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

QUICK INCH - CENTIMETER LENGTH CONVERSION

0 1 2 3 4 5

Inches

Centimeters 0 1 2 3 4 5 6 987 1110 1312

QUICK FAHRENHEIT - CELSIUS TEMPERATURE CONVERSIO

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

 

°F

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

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

Measures. Price $2.50 SD Catalog No. C13 10286 Updated 6/17/98
iii

Acknowledgements 

Special thanks to Federal Railroad Administration technical contributors Starr Kidda, Shala 

Blue, Robert Castiglione, Mike Long, Mark Patterson, and Robert Siegfried, as well as Jordan 

Multer and Scott Gabree, for their technical insights and guidance. 
iv

Contents

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

1. Introduction ................................................................................................................. 2

1.1 Background ................................................................................................................. 2

1.2 Objectives.................................................................................................................... 3

1.3 Overall Approach ........................................................................................................ 4

1.4 Scope ........................................................................................................................... 5

1.5 Organization of the Report.......................................................................................... 5

2. Pilot Safety Topic Selection Process........................................................................... 6

2.1 Pre-project Stakeholder Engagement .......................................................................... 6

2.2 Data Assessment and Final Safety Topic Selection .................................................... 6

3. Data Security, Acquisition, and Pre-Processing.......................................................... 8

3.1 Data Security Plan ....................................................................................................... 8

3.2 Data Requests and Stakeholder Discussions ............................................................. 10

3.3 Rail Safety Data Collection State-of-Practice Snapshot ........................................... 11

3.4 Data Cleaning and Fusion ......................................................................................... 11

4. Data Analysis and Methodology ............................................................................... 13

4.1 Exploratory Data Analysis ........................................................................................ 13

4.2 Dashboard Development ........................................................................................... 13

4.3 Statistical Tests .......................................................................................................... 18

4.4 C3RS and Inspection Data ........................................................................................ 20

5. Analysis Findings ...................................................................................................... 21

5.1 Use Cases with Dashboards ...................................................................................... 21

5.2 Overall Findings and Trends ..................................................................................... 22

5.3 Statistical Analysis Findings ..................................................................................... 24

5.4 C3RS and Inspection Findings .................................................................................. 27

6. Lessons Learned and Recommendations ................................................................... 28

7. Conclusion ................................................................................................................. 30

8. Appendix ................................................................................................................... 31

8.1 Appendix A: Use Case 1 Representative Sample of Visualizations ......................... 31

8.2 Appendix B: Stakeholder Project Feedback Form .................................................... 34
v 

Illustrations

Figure 1. Railroad Employee Injury Rate by Year (1990–2020) .................................................... 2

Figure 2. Railroad Accident Rate by Year (1990–2020) ................................................................ 3

Table 1. Summary of Safety Topic Assessment ............................................................................. 7

Figure 3: Tableau Online Account Login Page .............................................................................. 9

Figure 4: RISE Data Protection Framework ................................................................................. 10

Figure 5. Hypothetical Data Matrix .............................................................................................. 10

Figure 6. PI Univariate Dashboard ............................................................................................... 14

Figure 7. PI Univariate Normalized Dashboard ........................................................................... 15

Figure 8. PI Multivariate Continuous Dashboard ......................................................................... 16

Figure 9. PI Multivariate Categorical Dashboard ......................................................................... 16

Figure 10. PI Risk Matrix Dashboard ........................................................................................... 17

Figure 11. RV Univariate Dashboard ........................................................................................... 17

Figure A1. FRA Reportable Versus Nature of Injury ................................................................... 31

Figure A2. FRA Reportable Versus Occupation Title .................................................................. 31

Figure A3. FRA Reportable Versus Injured Body Region ........................................................... 32

Figure A4. FRA Reportable Versus Years of Service .................................................................. 32

Figure A5. FRA Reportable Versus Cause or Reason for Incident .............................................. 33
vi 

Tables

Table 1. Summary of Safety Topic Assessment ............................................................................. 7 
1 

Executive Summary

Despite tremendous progress in railroad safety over the last 30 years, many safety statistics have 

plateaued. Thus, additional insights on the complex interaction between people, trains, 

infrastructure, and the operating environment are needed. While the Federal Railroad 

Administration (FRA) requires railroads to regularly report on key safety metrics, there is 

additional data that railroads collect, but rarely share. 

Following similar successful data sharing projects in the aviation and motor vehicle industries, 

this FRA project established a pilot, secure, data sharing and analysis platform titled the Railroad 

Information Sharing Environment (RISE).

The RISE team included partners from FRA, the Volpe National Transportation Systems Center 

(Volpe), five passenger railroads, and the University of Maryland’s (UMD) Center for Advanced 

Transportation Technology (CATT Lab). The identities of the participating passenger railroads 

are kept anonymous as part of this project’s data trust.1

 The team worked together to achieve the 

following project objectives:

• Promote trust in the rail community. 

• Develop secure data capture processes. 

• Define and produce key analytic outputs. 

The team worked collaboratively to accomplish these objectives by:

• Identifying critical railroad safety topics

• Beginning to develop a non-punitive data trust 

• Establishing a data security plan

• Creating a secure, online, data sharing and analysis platform

• Demonstrating the platform on real-world safety issues of personal injuries and rule 

violations 

• Documenting the lessons learned

The RISE pilot project demonstrated the ability for FRA, Volpe, railroads, and a trusted thirdparty vendor, UMD’s CATT Lab, to share and analyze safety-sensitive data to improve railroad 

safety. This report documents the effort, challenges, lessons learned, and recommendations for 

building future phases of RISE as a data trust for the railroad industry. 

1 https://www.transportation.gov/orders/data-trust-policy-guidelines-and-principles
2 

1. Introduction

The Federal Railroad Administration (FRA), in collaboration with the Volpe National 

Transportation Systems Center (Volpe), the University of Maryland’s Center for Advanced 

Transportation Technology Laboratory (CATT Lab), and five passenger railroad stakeholders, 

conducted a proof-of-concept pilot project called the Railroad Information Sharing Environment 

(RISE) to test the feasibility of developing a data trust for the railroad industry. The first task in 

developing a data trust was to develop a secure railroad safety data sharing and analysis 

platform. This report documents the effort, lessons learned from this first task, and 

recommendations for future paths related to developing the RISE data trust. 

1.1 Background

Safety is the most important performance measure of any transportation system. To monitor and 

understand safety performance, the U.S. Department of Transportation and its modal 

administrations collect data on the movement of people and goods. While this data has enabled 

tremendous improvement in U.S. transportation system safety, many modes have observed 

plateaus in safety performance. For railroad safety, FRA has noted that both accident rates 

(Figure 1) and injury rates (Figure 2) have flatlined.

Figure 1. Railroad Employee Injury Rate by Year (1990–2020) 
3 

Figure 2. Railroad Accident Rate by Year (1990–2020) 

To address the challenge of further improving transportation safety, several modes have 

established data sharing initiatives, called data trusts, to securely gather additional safety data

from stakeholders. Examples of successful transportation safety data trusts include the Federal 

Aviation Administration’s Aviation Safety Information Analysis and Sharing, and the National 

Highway Transportation Safety Administration’s Partnership for Analytics Research in Traffic 

Safety. FRA has created several data sharing programs to break down the barriers preventing 

secure data sharing. Examples of successful FRA data trusts include its Confidential Close Call 

Reporting System (C3

RS) and an ongoing project with the Short Line Safety Institute. 

In a similar fashion, RISE was established to assess the feasibility of a stakeholder-driven, 

secure, safety data sharing and analysis platform. In the RISE pilot project, FRA worked with 

Volpe to engage five passenger railroad stakeholders and an impartial, third-party vendor to 

provide a secure data sharing and analysis environment. The University of Maryland’s CATT 

Lab was selected to serve as the data vendor that led the development of the RISE data sharing 

and analysis platform. 

1.2 Objectives

The RISE pilot project was established to assess the challenges, lessons learned, and overall 

feasibility of a stakeholder-driven secure safety data sharing and analysis environment for the 

railroad industry. The objectives of this research were to:

• Promote trust in the rail community. 

• Develop secure data capture processes. 

• Define and produce key analytic outputs. 
4 

1.3 Overall Approach

1.3.1 RISE Team Members and Roles

The RISE pilot team was comprised of members from FRA’s Office of Railroad Safety (RRS) 

and FRA’s Office of Research, Development, and Technology (RD&T) teams, Volpe, five 

passenger railroad stakeholders, and the CATT Lab. Each had specific roles to fulfill: 

FRA: The RRS and RD&T teams assisted in project management, provided formatted data and 

data reporting documentation, assisted in the design of the analysis dashboards, and provided 

general technical assistance on rail safety data analysis. 

Volpe: The Volpe team assisted in stakeholder engagement and data acquisition, provided 

technical assistance on rail safety data analysis, and assisted in the design of the data protection 

plan and design of the analysis dashboards. 

Railroad stakeholders: The five participating passenger railroads provided valuable industry 

perspectives by identifying and ranking safety issues of interest, sharing data to support the 

down-selected issues, providing information on how they collect and analyze safety data, and 

sharing feedback on the analysis dashboards and overall project experience. 

CATT Lab: The CATT Lab served as the third-party vendor that led the overall development of 

RISE, including project management and documentation, stakeholder engagement, secure data 

acquisition and storage, data cleaning and analysis, and dashboard development. 

1.3.2 RISE Development Process

A brief summary of the RISE development activities are provided below and are discussed in 

further detail throughout this report. 

Pre-project stakeholder engagement: Prior to CATT Lab’s involvement, Volpe and FRA worked 

with five passenger railroad stakeholders to identify and prioritize potential safety topics. The 

FRA report on that task, Lessons from the Railroad Information Sharing Environment (RISE) 

Pilot Project, is now available on the FRA eLibrary. 

CATT Lab joins RISE team: CATT Lab joined the team and revisited the short list of topics with 

the RISE team. 

Data assessment and determination of final safety topics: CATT Lab worked with FRA, Volpe, 

and railroad stakeholders to determine final safety topics by independently evaluating the data 

available to support the analysis of each topic. 

Data acquisition: CATT Lab, with Volpe’s assistance, held several meetings with the railroad 

stakeholders to discuss the data available to support this project and how it was collected. 

Options for sharing the data were also discussed to ensure stakeholders were comfortable with at 

least one of the sharing mechanisms. The secure data sharing mechanism was one component of 

the overall data security plan. During this process, the team learned about the state of railroad 

safety data collection. 

Data cleaning and fusion: Upon receiving data from the railroad stakeholders, CATT Lab began 

developing the anonymized database that removed any personally identifiable information (PII) 

as well as the exact time and physical location of each observation. The anonymization of the 

data was another component of the data security plan. During this process, CATT Lab had 
5 

several follow-up meetings with the railroad stakeholders to resolve data formatting issues and 

erroneous data entries.

Develop secure analytical dashboards: Once the anonymized database was created, the team 

developed a secure data analysis platform. The team used Tableau online software to create 

interactive dashboards using the anonymized database

Share dashboards with RISE team: The completed secure analyses were shared with RISE 

stakeholders via Tableau online licenses. Within the dashboards, each participating railroad user 

could switch between the aggregate dataset of all participating railroads and the data provided by 

the user’s railroad. This functionality enabled peer benchmarking assessments. For data security 

purposes, FRA and Volpe users had access to only the aggregate dataset.

Gather feedback: After each RISE team member explored the dashboards, follow-up meetings 

were conducted to document end user experiences, suggestions, and next steps for RISE. The 

railroad stakeholders were also asked to complete a brief questionnaire that is included in the 

appendix of this report. 

1.4 Scope

This report describes the effort to develop the pilot version of RISE. The report also includes 

documentation on the state-of-the-practice of railroad safety data collection, lessons learned in 

establishing a secure data sharing and analysis platform, end-user feedback on the data sharing 

and analysis platform, and recommendations for the next steps. This project included data 

provided by five commuter railroad stakeholders, from January 2018 through June 2019. 

1.5 Organization of the Report

Section 2 presents the process of selecting the topics for the pilot RISE demonstration. Section 3

describes the data acquisition and cleaning process. Section 4 discusses the background on the 

safety data analysis. Section 5 presents the findings of the safety analysis. Section 6 documents 

the lessons learned in developing the pilot version of RISE. And Section 7 provides concluding 

remarks and recommendations for next steps.
6 

2. Pilot Safety Topic Selection Process

2.1 Pre-project Stakeholder Engagement

Before CATT Lab joined the project, Volpe and FRA worked with five passenger railroad 

stakeholders to identify and prioritize potential safety topics. During this process, 35 safety 

topics and 34 supporting data types were identified.2

 To focus the discussion, the topics were 

consolidated to nine topics: 

1. Personal Injuries

2. Operating Rules Violations 

3. Passenger Station Misses/Off Platform

4. Technology Overreliance

5. PTC System Status

6. Equipment Safety

7. Employee Training

8. Operator Errors

9. Post-Incident Actions

To further down-select these topics, each railroad stakeholder was asked to rank each topic for its 

value, feasibility, and duration on a scale of from 1 to 5 (with 1 indicating low interest and 5 

indicating highest interest). The result of this ranking created a shortlist of three potential topics:

1. Personal Injuries (PI)

2. Operating Rule Violations (RV)

3. Passenger Station Misses/Off Platform

2.2 Data Assessment and Final Safety Topic Selection

CATT Lab joined the RISE team in September 2019 and revisited the shortlist of topics with the 

RISE team. CATT lab recommended revisiting at-grade rail crossings as a potential topic for 

consideration, and the team agreed to replace the Passenger Station Misses/Off Platform topic 

with At-Grade Rail Crossings. 

CATT Lab worked with FRA, Volpe, and railroad stakeholders to determine final safety topics 

by independently evaluating the data available to support the analysis of each topic. The team 

decided to use data covering January 2018 through June 2019 in this pilot study. CATT Lab’s 

evaluation considered: 

1. Data availability and accessibility

2. Data consistency

3. Pre-analysis data cleaning/processing

2 See the FRA Technical Report, Lessons from the Railroad Information Sharing Environment (RISE) Pilot Project. 
7 

4. Ability to anonymize data. 

5. Perceived ability to support deeper safety insights for all stakeholders. 

These considerations were scored 1, 2, or 3 (with 3 being the highest score) for each of the 

potential topics. CATT Lab recommended the inclusion of PI as well as RV as the topics for the 

pilot study. Each railroad stakeholder agreed with this recommendation. 

Table 1. Summary of Safety Topic Assessment

3 = Highest score for RISE implementation

1 = Lowest score for RISE implementation

* Indicates tie for highest score
8 

3. Data Security, Acquisition, and Pre-Processing

3.1 Data Security Plan

To ensure that the data and associated analysis tool were made available only to the appropriate 

parties, data security and anonymization aimed to: 

1. Block unauthorized users from RISE data and analysis tool. 

2. Mitigate PII concerns by removing the exact date and physical location of injuries and 

violations. 

3. Protect stakeholder data within the aggregate dataset. 

4. Protect stakeholder specific datasets. 

3.1.1 Secure Data Sharing and Storage 

Stakeholder-provided data in the original and/or redacted format was sent to CATT Lab via 

email, secure CATT Lab FTP, or directly to an encrypted data storage environment where the 

data cleaning, storage, and analysis took place. The Controlled Unclassified Information 

Environment (CUIE) provided by the University of Maryland’s Division of Information 

Technology is NIST 800-171-compliant.3 

3.1.2 Data Anonymization 

The data anonymization strategy removed all obvious PII such as employee name, ID number, 

and date of birth. Additional data scrubbing removed temporal (date and time) and spatial 

(physical location) details for each injury and violation record. Temporal data resolution was 

restricted to hour of day within a given day. Next, the spatial details were transformed and only 

FRA location codes and county information were retained. Injuries could not be geographically 

mapped to a physical location. 

3.1.3 User Permissions

A multi-tiered permissions structure was employed to restrict access and control what data was 

available to specific registered users. Only approved RISE participants could access the RISE 

platform. Accounts were requested and approved by the account administrator for each 

stakeholder. In this phase of the RISE project, a representative from each participating 

stakeholder acted as the account administrator. Each administrator coordinated directly with 

CATT Lab to identify personnel with access to the RISE dashboards provided via Tableau online 

licenses. Once approved, the users accessed the RISE platform using a user ID and password 

within the Tableau login page, as shown in Figure 3. 

3 University of Maryland, Division of Information Technology. CUI Environment. 
9 

Figure 3: Tableau Online Account Login Page

Once a user is logged in, the platform controls access to specific datasets based on railroad 

affiliation. All RISE partners have access to the anonymized aggregate datasets. However, access 

to railroad-specific data and analytical tools are controlled by each railroad. For example, a user 

from railroad A can see and analyze the aggregate data and the data from railroad A. No other 

data is available to this user. 

Lastly, to mitigate the unapproved sharing of RISE data, only aggregate dataset visualizations 

were available for download. The individual-level observations were not available for download. 

In addition, no individual stakeholder downloads were available – as this data was provided by 

the stakeholders, and it was assumed that such functionality was not necessary for this project. 

A summary of the proposed data security and anonymization plan is described in Figure 4. 
10 

Figure 4: RISE Data Protection Framework 

3.2 Data Requests and Stakeholder Discussions

With the data security plan in place, the team began the process of making formal data requests 

from the railroad stakeholders. CATT Lab and Volpe held several virtual meetings with each 

participating railroad stakeholder to discuss the details of the available data and how the data was 

collected. To document the availability of data across all participating railroad stakeholders, the 

team developed data matrices for both the PI and RV topics. These matrices were ultimately used 

to determine what data items were consistently collected and available to share for this study for 

the PI and RV analysis, respectively. To protect the actual data shared in this project, a 

hypothetical data matrix is provided in Figure 5.

Figure 5. Hypothetical Data Matrix
11 

3.3 Rail Safety Data Collection State-of-Practice Snapshot

In the data acquisition discussions with the railroad stakeholders, CATT Lab and Volpe also 

asked about safety data collection practices. These discussions shed light on the differences and 

similarities between railroad safety data collection processes. To honor the anonymity of the 

participating railroad stakeholders, this section will only discuss generalized findings. 

Though each participating railroad stakeholder had unique protocols on how safety data was 

collected and archived, all had recently or are in the process of transitioning to electronic-based 

data collection via commercial off-the-shelf software. Before this transition, a majority of data 

collection was performed using manual processes, such as manual entry into a spreadsheet or by 

calling in events to record observations, that increased the possibility of human error. 

Furthermore, these data entry methods often created segregated databases that prevented efficient 

and comprehensive safety analysis. For example, a single employee may be responsible for 

documenting RV observations and if that person leaves the railroad, that data may not be easily 

discoverable to conduct trend analysis. The commercial software solutions have tremendous 

potential to improve the efficiency and quality of data collection. In addition, commercial 

software solutions make archiving and sharing data easier by storing data in a centralized 

database that can be populated by multiple authorized users. 

Other challenges in rail safety data collection and sharing stem from siloed departments within a 

given railroad. For example, detailed employee work logs are needed to conduct fatigue analysis. 

However, such data may be difficult for the safety department to acquire if the human resources 

department is unwilling to share this valuable data. This challenge can only be overcome if 

departmental siloes are broken down to allow for comprehensive safety analysis. Most railroads 

are now building centralized databases so that all relevant data can be made available to 

authorized personnel across all departments. 

3.4 Data Cleaning and Fusion

CATT Lab performed data cleaning and fusion across the PI and RV datasets using a 

standardized process for storage, aggregation, normalization, and analysis. Railroad stakeholder 

feedback and an understanding of the potential uses of the data was essential to standardizing 

and processing. The RV dataset was more limited than the PI dataset, as it currently is not 

submitted to FRA. This resulted in different levels of data maturity among the stakeholders, and 

a smaller analysis that could be conducted on the RV dataset.

As discussed in the previous section, the first step of this process was to extract and obtain the 

MS Excel files with the required data elements from the railroad stakeholders. Next, the research 

team identified incomplete data submissions. Once the submission was deemed complete, the 

team standardized and cleaned the dataset using automated scripts for each stakeholder. The 

scripts anonymized the railroad stakeholder data by removing PII and enriched the dataset by 

deriving new data fields, such as binning continuous variables, creating grouped categorical 

variables that combined lower frequency levels, and creating date part elements. The scripts also 

integrated employee hours worked and abbreviated injury scale (AIS)/value of statistical life 

(VSL) data into the PI dataset. Due to the varying of employee hours worked provided by the 

railroad stakeholders, the day of week distribution from two stakeholders was applied to the 

other stakeholders that only had monthly granularity for this analysis. The overall level of effort 

for cleaning and standardizing the datasets was much higher than originally anticipated due to 
12 

the challenges associated with the manual compilation process described below. In future 

analyses, the team recommends automating the data ingestion process and using data already 

provided to FRA.

• Variations in the data format: 

o Discrepancies caused by category description abbreviations and special character 

usage.

o Inconsistent usage of category codes versus category descriptions across railroad 

stakeholders

• Differences in the data fields provided: 

o Different interpretation of the data requests, such as stakeholders providing FRA 

job title as opposed to FRA occupation. 

o Inconsistent data fields provided by railroad stakeholders when there was a

combined category code for a data field, such as Location, that was equivalent to 

multiple sub-attributes, such as Location1, Location2 and Location3. 

• Invalid and incomplete data: 

o Missing data fields in non-reportable incidents

o Category levels that did not exist.

o Invalid values, such as years of service values greater than an individual’s age and 

non-reportable incidents with lost days greater than 0 

• Duplicate entries: 

o Some PI incidents were recorded as both non-reportable4

 and reportable. 

• Multiple files that were not intuitively linked:

o Multiple iterations and requests needed to obtain all originally requested fields. 

o New data elements, such as weather, AIS, and inspection locations, were 

requested from the railroad stakeholders as the project progressed.

Once each dataset was cleaned, the team fused them into a master database that was used for the 

data analysis.

4 Non-reportable injuries refer to injuries below FRA minimum reporting thresholds and are also called accountable 

injuries. 
13 

4. Data Analysis and Methodology

This section provides an overview of the data analysis and methodology, including: 

• Exploratory Data Analysis

• Dashboard Development

• Statistical Tests

• C3RS and Inspections Data 

4.1 Exploratory Data Analysis

CATT Lab performed an exploratory data analysis once the initial master database was created. 

This was an iterative process, as data discrepancies were identified through the exploratory data 

analysis which resulted in minor updates to the master database. The process involved the 

development of descriptive statistics, word clouds, charts, and graphs to understand the data 

fields within the PI and RV datasets. The exploratory data analysis helped test the master 

database by identifying, collecting, aggregating, and analyzing the PI and RV data. This 

exploratory analysis was essential to the development of the dashboards and provided 

information such as:

• Completeness of data fields

• Removal of data fields with insufficient data

• Distribution of data fields

• Differences between PI reportable and non-reportable incidents

• Outliers in data that required action

• Initial potential relationships between the variables 

• Appropriate types of visualizations

• Identification of new data fields of interest

4.2 Dashboard Development

CATT Lab designed and implemented the RISE platform once the exploratory data analysis was 

complete. The research team recognized the need for dashboards with different objectives and 

developed the RISE platform to include six dashboards: five focused on PI and one focused on 

RV. The development approach involved building and vetting a set of mock-ups to select the 

types of charts, views, and pivots that best accomplish the purpose and objectives of each 

dashboard. These mock-ups were vetted in both internal meetings with FRA as well as external 

meetings with railroad stakeholders. In addition, once the dashboards were built, pilot access to 

the dashboards was provided to further obtain feedback and changes. 

The RISE platform was developed using Tableau Online and allowed users to navigate and 

explore the datasets using deep-dive, trend, and sensitivity techniques. The dashboards were built 

to be interactive and easy to navigate, with users able to click on charts and drop-downs to zoom 

and filter across the data fields. The dashboards were developed with data protection and railroad 

stakeholder anonymity at the forefront, which was a key emphasis of the of the data protection 

plan with the railroad stakeholders. Data protection was accomplished through establishing user 

roles and permissions within Tableau Online to control the access of users and protect the 

underlying dataset. Furthermore, the visualizations focused on providing general trends, 
14 

relationships, and distributions. To protect the data, the visualizations did not allow users to drill 

-down to individual records. Lastly, the dashboards permitted railroad stakeholders to toggle 

between an aggregate view of the data that included all stakeholder data as well as a specific 

view for each respective stakeholder. This allowed individual stakeholders to benchmark their 

data against their railroad peers while protecting the identity of specific railroad stakeholder 

groups. A high-level overview of each of the dashboards is provided below: 

PI – Univariate 

The PI Univariate dashboard (Figure 6) enabled users to explore the differences between FRA 

reportable and non-reportable incidents. Users could investigate temporal relationships across 

month-year, hour of day, and day of week. In addition, within the dashboard users could select a 

categorical variable and explore the injury distribution as well as the average lost days, age, and 

years of service associated with that categorical variable. Lastly, the dashboard allowed users to 

select a desired reportable versus non-reportable view including stacked, side-by-side, 

ungrouped, reportable incidents only, and non-reportable incidents only.

Figure 6. PI Univariate Dashboard 

PI – Univariate Normalized 

The PI Univariate Normalized dashboard (Figure 7) had a similar focus, look, and feel as the PI 

Univariate dashboard. The main difference was that it allowed users to normalize the reportable 

and non-reportable injury count by employee hours worked for the day of week and month-year 

categorical variables. Both the day of week and month-year charts allowed users to see injury 

counts and employee hours worked to understand how the normalization works. As with the PI 

Univariate dashboard, it also allowed users to select a desired reportable versus non-reportable 

view.
15 

Figure 7. PI Univariate Normalized Dashboard 

PI – Multivariate Numeric

The focus of the PI Multivariate Continuous dashboard (Figure 8) was to enable users to explore 

relationships between the continuous variables. This dashboard consisted of the following three 

charts and allowed users to select two continuous and one categorical variable:5

• Bubble plot: allowed users to explore two continuous variables on the x and y axis and 

one categorical variable that sized the bubble. 

• Scatter plot: allowed users to explore two continuous variables on the x and y axis. 

• Box and whisker plot: allowed users to explore the distribution of a continuous variable 

across one categorical variable. 

In addition to the charts, three temporal filters could also be applied, including day of week, hour 

of day (binned), and month-year. 

5 To facilitate interpretation of visualizations in the multivariate dashboards, for variables with more than 12 levels, 

the dashboard used grouped categorical variables limited to the 11 highest frequency variable levels with the 

remaining categories grouped in an “other” group. These variables are designated with an asterisk in the dashboards.
16 

Figure 8. PI Multivariate Continuous Dashboard 

PI – Multivariate Categorical 

The PI Multivariate Categorical dashboard (Figure 9) enabled users to explore relationships 

between the categorical variables. This dashboard allowed users to explore two-way categorical 

variable5 relationships within seven different charts, including side-by-side bar charts, stacked 

bar charts, heat maps, circle charts, tree maps, Marimekko charts, and Sankey charts. The 

different chart options allowed users the flexibility to approach their exploration with different 

views of the data and ultimately select the chart with which they were most comfortable. As with 

the PI Multivariate Continuous dashboard, there were three temporal filters that could be applied. 

Figure 9. PI Multivariate Categorical Dashboard 

PI – Risk Matrix

The PI Risk Matrix dashboard (Figure 10) enabled users to analyze and understand the average 

lost days and estimated injury cost associated with rail incidents. This dashboard consisted of 

two risk matrix plots that allowed users to select a categorical variable and analyze the frequency 
17 

and associated average lost days and average estimated injury cost. The estimated injury cost 

was based on a preliminary mapping of the AIS and the VSL currently being finalized by FRA. 

Figure 10. PI Risk Matrix Dashboard 

RV – Univariate 

The RV Univariate dashboard (Figure 11) enabled users to explore the RVs. The dashboard had 

a similar look and feel as the PI Univariate dashboard and allowed users to investigate temporal 

relationships across month-year, hour of day, and day of week. The dashboard was limited in 

exploring categorical variables due to data availability and focused solely on the distribution, 

average count, and years of service associated with the violation type and the occupation 

category. In the RV dashboard, some of the participating railroad stakeholders indicated multiple 

individuals involved in a single violation event. In such cases, researchers assigned a portion of 

the violation to each employee involved. 

Figure 11. RV Univariate Dashboard 
18 

4.3 Statistical Tests

The following sections provide an overview of the statistical and modeling techniques performed 

on the PI Data. Note that no statistical modeling was performed on the RV data due to its limited 

size. This analysis served as a representative sample of the type of statistical analysis that can be 

performed using PI data and is focused on several areas:

1. Statistical differences – temporal variables 

2. Statistical differences – injury rate of railroad stakeholders 

3. Statistical differences – reportable versus non-reportable incidents

4. Statistical differences – two-way categorical variables of interest

5. Correlation fit – continuous variables 

4.3.1 Statistical Differences – Temporal Variables 

CATT Lab performed statistical tests and modeling on three temporal variables: day of week, 

hour of day, and month-year. The analysis focused on the non-normalized frequency count data 

and normalized frequency count data per employee hours worked each month. An overview of 

the analysis performed for temporal variables is summarized below.

Frequency Count Data Statistical Tests and Modeling: 

• Chi-square test of goodness of fit:

o Chi-square tests of goodness of fit were performed to establish whether an 

observed frequency distribution differed from a theoretical distribution. For the 

theoretical distributions the research team assumed an equal probability of each 

temporal category. In terms of day of week, this meant that the chance of an 

injury on a Monday is the same as the chance of an injury on a Tuesday. Next, if 

the result of the chi-square test was significant with a p-value less than 0.05, the 

team performed a post-hoc paired chi-square with a Bonferroni correction to 

determine the actual levels that were different. 

• General linear model (GLM) Poisson regression:

o GLM Poisson regression models were developed to examine the relationship 

between the temporal variable and the observed count of injuries. The Poisson 

Regression model z-tests performed on the independent variables determined the 

statistical significance of the temporal variable levels. 

Injury Rate per Employee Hours Worked Data Statistical Tests: 

• Analysis of variance (ANOVA)

o ANOVA tests were performed to determine whether there were any statistically 

significant differences between the means of the temporal variable level groups. 

Next, if the result of the ANOVA test was significant with a p-value less than 

0.05, the team performed a post-hoc Tukey-Kramer honestly Significant 

difference (HSD) test to determine the actual levels that were different. 
19 

4.3.2 Statistical Differences – Injury Rate of Railroad Stakeholders

CATT Lab performed statistical tests on the injury rate per employee hours worked across the 

railroad stakeholders. As with the temporal variable analysis, the normalized frequency count 

data per employee hours worked each month was analyzed using an ANOVA test. 

4.3.3 Statistical Differences – Reportable Versus Non-Reportable Incidents

CATT Lab performed statistical tests and modeling on variables related to reportable and nonreportable incidents, including nature of injury, injured body region, occupation title, occupation 

category, and Location 3: specific location of incident and cause/reason for incident. An 

overview of the analysis performed for reportable and non-reportable incidents is summarized 

below. 

• Chi-square test of independence:

o Chi-square tests of independence were performed to examine whether the 

variables were independent of one another. The null hypothesis of this chi-square 

test was that no relationship exists between the variables (independent) while the 

alternative hypothesis was that the variables were in fact related. Next, if the 

result of the chi-square test was significant with a p-value less than 0.05, the 

research team performed a post-hoc paired chi-square with a Bonferroni 

correction to determine the actual levels that were different. 

 Note that simulation of the sampling distribution of the test statistic was 

used to derive the p-value when expected counts were low. The team did

not anticipate that simulation would be needed for a larger dataset beyond 

this pilot, which included an 18-month sample of the five railroads data.

• Inference and variable importance based on predictive models:

o Inference models were developed to predict whether an incident was reportable or 

non-reportable. The purpose of these models was to better understand the 

variables contributing to the predictions. The following five types of interpretable 

models were used: logistic regression, grouped lasso, decision trees, random 

forests, and boosted trees. The logistic regression performed z-tests on the model 

parameter coefficients to identify variable importance utilizing p-values under 

0.05. The grouped lasso performed feature selection by identifying features that 

did not contribute to the model. Random forest, decision trees, and boosted trees 

approximated variable importance by calculating the relative contribution of the 

corresponding feature to the model calculated by taking each feature’s 

contribution in the model. 

 All models were based on a 75 percent training and 25 percent test dataset 

split. In terms of model tuning, a 10-fold cross-validation was then 

performed on the training sets to determine the optimal parameters.

4.3.4 Statistical Differences – Two-Way Categorical Variables of Interest

CATT Lab performed two-way statistical analysis on two pairs of variables of interest: nature of 

injury versus occupation title and severity of injury versus occupation title. Note that for future 

analyses, any pairs of variables could be analyzed. As with the reportable versus non-reportable 
20 

incidents, chi-square tests of independence were performed to determine whether the variables 

were independent of one another. 

4.3.5 Correlation – Continuous Variables

CATT Lab performed correlation analysis on the continuous variables within this dataset 

including age, years of service, and lost days. Pearson correlation coefficients were calculated for 

each pair to determine whether there was a relationship between the variables. 

4.4 C3RS and Inspection Data 

CATT Lab integrated the C3RS and county inspections with the PI and RV datasets. An 

overview of the analysis is summarized below: 

• C3RS: 

o C3RS was examined to identify the fields in common with the PI and RV datasets. 

Based on the two temporal fields in common, an analysis was conducted on the 

three datasets to identify distribution and trends across hour of day bin and 

month-year. 

• Inspections: 

o County inspection data was integrated with PI incidents to identify the date of the 

last county inspection prior to an incident occurring. The distribution of days 

since last inspection and the relationship with personal injuries were then 

analyzed. 
21 

5. Analysis Findings

This section provides an overview of the findings from the PI Analysis, including: 

• Use Case with Dashboards

• Overall Findings and Trends 

• Statistical Analysis Findings

• C3RS and Inspection Findings 

5.1 Use Cases with Dashboards

This sub-section provides an overview of the findings based on the use cases that were 

developed and analyzed. CATT Lab developed the first two use cases and railroad stakeholders 

recommended the third use case. The first use case – PI Reportable versus Non-Reportable 

incidents – was presented in the stakeholder meeting and included more in-depth analysis and 

output as opposed the rest of the use cases, summarized at a high level. All use cases were 

analyzed using the RISE platform dashboards and demonstrate the ability to help users gain 

insights on rail safety. 

Use Case 1: PI Reportable Versus Non-Reportable Incidents

This pilot study provided the opportunity to analyze PI non-reportable incidents, which, because 

they were less severe, did not meet FRA reporting requirements. The goal of this use case was to 

gain insights from incorporating both reportable and non-reportable incidents to understand what 

circumstances contribute to an injury being more serious and hence reportable to FRA. 

PI-Univariate, PI-Multivariate Numeric, and PI-Multivariate Categorical dashboards were used

to identify notable differences. PI-Univariate and PI-Multivariate Categorical were used to 

analyze FRA reportable and non-reportable incidents versus the other categorical variables, 

while PI-Multivariate Numeric was used to analyze FRA reportable and non-reportable incidents 

versus continuous variables. Appendix A provides a representative sample of the types of 

visualizations across the three dashboards used in this analysis.

Overall, the trends and distributions of the PI variables across reportable versus non-reportable 

incidents were very similar, indicating that additional information on the incidents may be 

required to determine the circumstances that led to a more severe reportable incident. There 

were, however, some differences noted across several variables that are summarized below. Note 

that “NR” stands for non-reportable incidents and “R” stands for reportable incidents. 

• Nature of Injury:

o Sprain and strain injuries were more prominent in reportable incidents (25 percent

of NR/42 percent of R) while bruise or contusion injuries were more prominent in 

non-reportable incidents (32 percent of NR/17 percent of R). 

o Fracture (broken bones and rupture/tear injuries were only found in reportable 

incidents – they tended to be more severe injuries, as demonstrated by their 

considerable number of lost days. These injuries made up about 7 percent of the 

overall data.

• Occupation Title: 
22 

o Maintenance of way (MOW) and structures staff injuries were more prominent in 

reportable injuries (19 percent of NR/34 percent of R), while maintenance of 

equipment and stores staff were more prominent in non-reportable injuries (30 

percent of NR/22 percent of R). 

• Injured Body Region: 

o Head or face injuries were more prominent in non-reportable incidents (20 

percent of NR/14 percent of R), while torso injuries were more prominent in 

reportable incidents (23 percent of NR/29 percent of R). 

o Other injuries were also more prominent in non-reportable incidents (6 percent of 

NR/1 percent of R).

• Cause/Reason for Incident:

o Following procedures for operating equipment injuries were more prominent for 

reportables (2 percent of NR/10 percent of R).

Use Case 2: PI Highest Injury Rate by 200k Hours Worked 

The goal of this use case was to compare the June 2018 period to the overall dataset to identify 

circumstances that could have contributed to the highest injury rate. This case focused on June 

2018, which had the highest rate of 6.31 injuries per 200k hours worked, demonstrating the value 

of incorporating both reportable and non-reportable incidents to understand the highest injury 

rate. Overall, there were only slight differences in the June 2018 period compared to the overall 

dataset. 

Use Case 3: PI Railroad Stakeholder Results Versus Aggregate Dataset

The goal of this use case was for each railroad to benchmark its results against the aggregate 

FRA RISE platform data. All railroads were encouraged to conduct this use case. 

5.2 Overall Findings and Trends

The following summarizes the more notable overall trends found in FRA RISE PI dataset.

• Occupation Category: 

o Most incidents involved road passenger conductors and engineers: 

 Road passenger conductors (21 percent of total injuries)

 Assistant road passenger conductors and ticket collectors (7 percent of 

total injuries)

 Road passenger engineers and motormen (7 percent of total injuries)

• Occupation Title:

o Most incidents involved transportation and mechanical staff: 

 Transportation train and engine and transportation other than train and 

engine (41 percent of total injuries)

 MOW and structures (30 percent of total injuries)

• Nature of Injury:
23 

o Most incidents involved sprains and strains and bruises or contusions: 

 Sprains and strains (36 percent of total injured)

 Bruises or contusions (22 percent of total injured)

• Injured Body Region: 

o Most incident injuries were equally dispersed across torso, arm or hand, and leg 

or foot body regions: 

 Torso (27 percent of total injured)

 Arm or hand (26 percent of total injured)

 Leg or foot (25 percent of total injured)

• Location1: General Location of Incident: 

o Most incidents took place in the main/branch and yard: 

 Main/branch (40 percent of total injuries)

 Yard (22 percent of total injuries)

• Location2: Equipment Type Involved:

o Most incidents were not associated with on-track equipment and involved 

combinations of standing/moving on a passenger train/passenger car: 

 The A/I not associated with on track equipment (51 percent of total 

injuries)

 Combinations of standing/moving on passenger train/passenger car (35 

percent of total injuries)

• Physical Act: 

o Most incidents occurred while staff was walking or standing: 

 Walking (19 percent of total injuries)

 Standing (9 percent of total injuries)

• Cause/Reason for Incident:

o Most incidents resulted from a human factor, an environmental factor and 

undetermined circumstances:

 Human factor (42 percent of total injuries)

 Environmental (14 percent of total injuries)

 Undetermined (14 percent of total injuries)

• Event Circumstance: 

o The event circumstance was disbursed across many categories, and the top event 

circumstance cause was overexertion:

 Overexertion (17 percent of total injuries)
24 

• AIS Severity:

o Almost all injuries were minor or moderate in severity:

 Minor (80 percent of total injuries)

 Moderate (12 percent of total injuries)

• Estimated VSL:

o Almost all incidents were estimated to be in the two lowest cost tiers:

 Tier 1 – $28,800 (85 percent of total injuries)

 Tier 2 – $451,200 (12 percent of total injuries)

• Hour of Day and Daylight: 

o Most injuries occurred between 9 a.m.–12 p.m. (25 percent of total injuries) and 

most injuries occurred during daylight hours (86 percent of total injuries).

• Weather: 

o Poor weather conditions during outdoor incidents were present in about 11 

percent of total injuries and in 17 percent of the outdoor injuries (excluding the 

indoor incidents). 

• Binned Years of Service: 

o Service personnel with fewer years of service were more susceptible to injuries; 

31 percent of total injuries were associated with 5 years of service or less. 

• Highest Rate of Injuries: 

o June 2018 had the highest injury rate, 6.31, per 200k hours worked, . 

5.3 Statistical Analysis Findings

The following section details the results of the statistical tests performed on the PI data discussed 

in Section 4.3. 

5.3.1 Temporal Variables

CATT Lab performed chi-square tests, GLM Poisson models and ANOVA tests to determine 

significant relationships across the temporal variables. Overall, there were significant differences 

in the observed versus theoretical distribution across all three variables. However, when 

normalizing the day of week and the month-year by employee hours worked, there were not 

significant differences in the injury rates. The research team also performed some sensitivity 

analysis with filtering solely reportable and non-reportable incidents to see whether the 

relationship held true or changed. Other filtering across occupation types or locations could be 

considered in future analyses. 

• Day of Week: 

o Frequency count: There was a significant difference in the number of injuries 

based on the day of week. Weekdays had a higher number of injuries than the 

weekend. 
25 

o Normalized rate: When normalizing the number of injuries by employee hours 

worked, there was no longer a significant difference, as employee hours worked 

were lower on the weekends. 

o Filter reportable and non-reportable incidents: Both the frequency count and 

normalized rate findings held true. 

• Month-Year:

o Frequency count: There was a slightly significant difference in the number of 

injuries based on the month-year. The number of injuries in November 2018, 

December 2018, and May 2019 were less than the other 15 months in the pilot 

analysis.

o Normalized rate: When normalizing the number of injuries by employee hours 

worked, there was no longer a significant difference as the employee hours 

worked. 

o Filter reportable and non-reportable incidents: The frequency count was no longer 

significant for reportables and non-reportables, while the normalized rate findings 

held true. 

• Hour of Day: 

o Frequency count: There was a significant difference in the number of injuries 

based on the hour of day. Most injures occurred during daytime shifts, from 7:00 

a.m.–3:00 p.m.

o Normalized rate: N/A, as employee hours worked were not available at this level 

of granularity. 

o Filter reportable and non-reportable incidents: The frequency count findings held 

true. 

5.3.2 Injury Rate of Railroad Stakeholders

CATT Lab performed ANOVA tests to identify a significant difference in the injury rate across 

railroad stakeholders. Overall, there were significant differences in the injury rates between the 

railroad stakeholder groups. Due to anonymity concerns, this finding will not be discussed in this 

report. 

5.3.3 Reportable Versus Non-Reportable Incidents: 

CATT Lab analyzed inference models and chi-square tests to determine variable importance and 

significant relationships between FRA reportable and non-reportable incidents. Overall, the 

inference models and chi-square tests revealed only slight differences and confirmed that 

reportable and non-reportable incidents were very similar. 

Inference Models 

Overall, the inference models had difficulty differentiating between reportable and nonreportable incidents and performed marginally better in accuracy than the no information rate. 

The following variables were identified as influential based on the various inference models 

analyzed: 
26 

• Nature of Injury

• Occupation Title

• Injured Body Region 

• Cause or Reason Occupation Category

• Location3: Specific Location of Incident 

Chi-Square Tests 

CATT Lab further explored the influential variables from the inference models for significant 

relationships with FRA reportable and non-reportable incidents. All the variables were found to 

be significant across chi-square tests, indicating there was a relationship between the variables. 

As a result, post-hoc, paired, chi-square tests were run to identify the actual levels of the 

variables that were different. Note the underlined levels correspond back to the Use Case 1: 

Reportable Versus Non-Reportable incidents findings discussed in Section 5.1. Overall, the chisquare post-hoc tests showed significant relationship across a small number of variables levels, 

as summarized below. 

• Nature of Injury: 

o Bruise or contusion: significantly more prevalent in non-reportable incidents

o Fracture (broken bone): only present in reportable incidents

o Rupture/tear: only present in reportable incidents 

o Sprain or strain: significantly more prevalent in reportable incidents 

o Animal/snake/insect bite: significantly more prevalent in non-reportable incidents 

o Symptoms due to one-time inhalation exposure to airborne contamination: 

significantly more prevalent in non-reportable incidents 

• Occupation Title: 

o Maintenance of equipment and stores: significantly more prevalent in nonreportable incidents.

o MOW and structures: significantly more prevalent in reportable incidents.

• Injured Body Region: 

o Other: significantly more prevalent in non-reportable incidents 

• Cause or Reason: 

o Procedures for operating/using equipment not followed: significantly more 

prevalent in reportable incidents 

o Undetermined: significantly more prevalent in non-reportable incidents

• Occupation Category: 

o Stationary engineers: significantly more prevalent in non-reportable incidents

• Location3: 

o Specific Location of Injury: No differences in levels identified. 
27 

5.3.4 Two-Way Categorical Variables of Interest

CATT Lab analyzed the nature of injury and severity of injury relationships with occupation 

title. Overall, the severity of injury chi-square test was not significant, while the nature of injury 

chi-square test was significant. The actual levels of occupation title that were different based on 

post-hoc paired chi-square tests are listed below. 

• Occupation Type Versus Nature of Injury: 

o Maintenance of way and structures: significantly more injuries with fractures 

(broken bones) and animal/snake/insect bites 

o Maintenance of equipment and stores: significantly more injuries with symptoms 

due to one-time exposure to loud noises 

o Transportation, train and engine: significantly more injuries with bruises or 

contusions 

5.3.5 Continuous Variables

CATT Lab analyzed the correlation relationship between lost days and the age and years of 

service of the injured employee. Overall, neither variable had a strong relationship with lost 

days, as summarized below.

• Age was not correlated with lost days. Higher ages of injured employees did not result in 

a higher number of lost days (correlation ~0.02). 

• Years of service was not correlated with lost days. Higher years of service of injured 

employees did not result in a higher number of lost days (correlation ~0.06). 

5.4 C3RS and Inspection Findings

There were no noteworthy findings regarding the integration of C3

RS and county inspections 

with the PI and RV datasets. For future analyses, the research team recommends that railroads: 

• Obtain more detailed inspection data, such as the specific location of inspection and the 

reason for the inspection, to better correlate it against the PI dataset.

• Invest in mapping the C3

RS data fields more closely to the PI and RV datasets to analyze 

additional features beyond temporal variables.
28 

6. Lessons Learned and Recommendations

Throughout this project, the team gained valuable insight on needs and challenges that passenger 

railroads face in collecting, archiving, and sharing data. These challenges ultimately affected the 

effort required to establish the pilot version of RISE that will be used to guide future, related 

development of the data trust. Below are the primary lessons learned in overcoming challenges 

faced in this project, as well as recommendations for the next phase of RISE. 

Lesson 1: Variability in how data is collected, especially the data not directly shared with FRA, 

required significant effort in the data cleaning and fusion process. As mentioned in Section 2.2

(data assessment and final safety topic selection), differences between data shared by railroad 

stakeholders required some data to be excluded to protect the identity of the railroads. 

Recommendation: The RISE team should work closely with FRA RRS to better utilize data 

collection forms and guidelines. Such information may have reduced the effort in requesting and 

cleaning data sent by railroad stakeholders.

 

Lesson 2: Significant time was spent building options for a comprehensive data security plan to 

ensure the data of all participants was safeguarded. Some of these options, such as CUIE, were 

not fully utilized. 

Recommendation: Future changes to the data security plan should involve both technical and 

legal representative from all stakeholders. These parties should come to an agreement on a 

security plan that meets everyone’s needs to avoid the effort required to modify the security 

options based on individual stakeholder needs. 

Lesson 3: Stakeholder engagement and buy-in is required for RISE to be successful. In addition 

to the usual resource challenges faced by railroads, this project was delayed by COVID-19. 

Changes in personnel and shifting priorities under constrained budgets presented additional 

challenges in developing the pilot version of RISE. Despite these challenges, participants from 

FRA, Volpe, and the participating railroads saw the potential for RISE to lay the framework for 

addressing critical rail safety issues. 

Recommendation: The RISE team should continue to grow RISE by inviting safety-driven 

stakeholders to join the team. Sharing data, expertise, and experiences are needed to break 

through the rail safety plateau. 

After the dashboards were shared, the RISE team asked the railroad stakeholders to provide 

feedback on their experience with the dashboards and the project in general using the feedback 

form shown in Appendix B. The responses provided a framework for final discussions with each 

stakeholder. In addition, the team also met with FRA safety analysts to document their 

experience with the dashboards. During these discussions, the team discovered that the railroad 

stakeholders found the dashboards to be useful not only for automating the analysis process but 

also for communicating findings to a broad audience. For example, the PI Univariate dashboards 

provide charts and visualizations that are commonly shared with upper management to 

communicate general safety trends. There was also interest in using the multivariate dashboards 
29 

to investigate deeper insights and communicating findings within the data analysis community. 

In addition, nearly every stakeholder appreciated the PI risk matrix, as converting injuries to 

monetary values was of great interest to every railroad stakeholder. 

For the next RISE phase, the team is considering the following enhancements: 

1. Extend RISE to include additional railroad stakeholders, including freight rail partners

and labor unions. 

2. Streamlined selection of additional safety topics to assist railroad agencies tackle difficult 

safety issues. 

3. Automate the secure data sharing process. 

4. Providing additional tools to support efficient safety data analysis and benchmarking. 
30 

7. Conclusion

The successful completion of the pilot RISE project has set a framework for continued secure 

railroad safety data sharing and analysis. In doing so, the RISE team made the following key 

contributions to the realm of railroad safety:

Engaged railroad stakeholders: This project was driven by the desire of five passenger railroads 

to address challenging safety issues. These stakeholders provided invaluable industry insights on 

safety topics, data collection procedures, data analysis practices, and potential future paths for 

RISE. 

Began establishing a railroad safety data trust: Following examples from other modes, RISE 

began to establish a data trust among passenger railroad stakeholders, FRA, Volpe and CATT 

Lab. This data trust formed the foundation for voluntary, non-punitive, secure data sharing – 

solely to better understand challenging safety topics. 

Created a data security plan: The RISE data security plan anonymized data and controlled access 

to the aggregate and railroad-specific data. This plan was designed to apply to any safetysensitive data.

Developed a secure data sharing and analysis platform: The end product of this project was the 

creation of the secure online data sharing and analysis tool. This streamlined the process of PI 

and RV data analysis and benchmarking. 

Demonstrated the ability of RISE to address a critical safety issue: Data provided by railroad 

stakeholders was used to demonstrate the analysis of the PI and RV topics. Further statistical 

analyses were conducted to illustrate the formal process of comparing safety statistics.

Documented lessons learned: Throughout the process of establishing RISE, the team documented 

the challenges faced and lessons learned. These lessons will provide the team with valuable 

experience in the future phases of developing the RISE data trust. 
31 

8. Appendix

8.1 Appendix A: Use Case 1 Representative Sample of Visualizations

Figure A1. FRA Reportable Versus Nature of Injury

Figure A2. FRA Reportable Versus Occupation Title
32 

Figure A3. FRA Reportable Versus Injured Body Region

Figure A4. FRA Reportable Versus Years of Service
33 

Figure A5. FRA Reportable Versus Cause or Reason for Incident
34 

8.2 Appendix B: Stakeholder Project Feedback Form

Stakeholder Analysis Dashboard Feedback

Context: The Center for Advanced Transportation Technology Laboratory (CATT Lab) seeks 

feedback on your agency’s initial experience with the Railroad Information Sharing Environment 

(RISE) analysis dashboards and the RISE project, itself. As you and your colleagues explore the 

Tableau dashboards, please review and answer the questions listed below. 

Your responses to the questions below will be used to document end user experiences, prioritize 

dashboard improvements, and inform future paths for this project. Please add your feedback 

directly to this Word document and provide screenshots of the dashboard as needed to 

supplement your responses. 

A follow-up meeting will be coordinated to review your responses. Any questions about the 

survey can be directed to: 

Mark Franz

email:mfranz1@umd.edu 

Thank you for your valuable feedback, 

Mark

General Use of Dashboard

• Did the dashboards meet your expectations for how to investigate the study topics 

identified by the group? 

• Who will be the primary users (job titles, not names of individuals) accessing and 

exploring the dashboards? Will any other employees be providing direction about how to 

explore the dashboards?

• How would you like to make use of these dashboards over the next 6 months? Do you 

intend/expect to use the dashboards for data exploration, presentations/data 

visualizations?

• Did the data in the dashboards provide any insights on how your agency may better 

collect and manage data on personal injuries and/or rules violations? For example, is 

there a data field that your agency is not currently collecting but may consider after this 

project?

Data Aggregation 

• Does the aggregated data on the dashboards help you understand things you could not 

understand with your data alone? Yes/No (please provide examples)
35 

• What insights have you uncovered by reviewing the aggregated data? 

• If you chose to benchmark your data against the aggregated data, did this comparison 

provide useful information? Yes/No (please provide examples) 

Interface Design and Data Presentation

• Is it easy to navigate the dashboards and filter/drill down to events of interest? Why or 

why not? 

• Do the dashboards support your agency’s data visualization and reporting needs? 

o If not, what functions and/or visualizations are needed?

Use Cases and Findings

• Have the charts and tables in the Tableau dashboard provided any insights that you have 

not observed from the ways that your data are currently analyzed and presented? Yes/No 

(please provide examples) 

• What insights have you uncovered from this new presentation of this data? 

o What contributed to those new insights? Were there particular charts or 

information displays that were particularly helpful?

• After going through the data sharing process, does your agency have or desire to collect 

any additional data fields to support personal injury and/or rules violation analysis? If so, 

please list those data fields. 

Data Collection/Maintenance

• Has the process, to date, demonstrated that this is a mechanism by which you feel 

comfortable sharing data? 

• Would you feel comfortable sharing additional data in a similar manner? 

• Do you have suggestions for ways to enhance the data collection process? 

Topic Selection

• The stakeholders decided to pursue personal injuries and rules violations as the initial 

use cases for the demonstration. Please consider the following:

o Did these use cases demonstrate the ability to stand up a functioning data trust? 

o Did these use cases show the potential benefits of RISE?

o Do you have suggestions for how to select topics as this demonstration continues? 

o Do you have any recommendations for topics to pursue? 

o Do you have any suggestions for how to improve RISE going forward? 
36 

Abbreviations and Acronyms

ACRONYMS EXPLANATION

CATT Lab Center for Advanced Transportation Technology

FRA Federal Rail Administration

PI Personal Injury

RISE Railroad Information Sharing Environment

RV Rule Violation

UMD University of Maryland