Unifying Railcar Monitoring Sensor Data, Maintenance Records, and Railcar Usage Information through Big Data Processing - for Optimizing Railcar Maintenance and Safety

Sep 11, 2026

This project investigated how Big Data Analytics can improve rail transportation safety by preventing derailments caused by equipment failure. Railroads commonly schedule railcar maintenance using fixed best-practice intervals that overlook the large volume of actionable information contained in maintenance logs, track and sensor data, shipping documents, manufacturer history, and related records. The work used integrated data to adapt maintenance scheduling, aiming to reduce costs while increasing safety, and demonstrated strong potential for this approach.

LanguageEnglish

CategoryPresentations

FormatPDF

Pages38

Reading Time13 min

AccessFree to download

Introduction

Our Approach

Methodology

Implementation and Results

Input and Data

Important Features Selection

Data Collection and Generation

Data Processing

Summary and Conclusions

Summary

Future Work

Publications Resulting from Research

References

Unifying Railcar Monitoring Sensor Data, Maintenance Records, and Railcar Usage Information through Big Data Processing for Optimizing Railcar Maintenance and Safety Hamid Sharif, Ph. D. Charles Vranek Professor Electrical and Computer Engineering Department University of Nebraska-Lincoln Michael Hempel, Ph. D. Research Assistant Professor Electrical and Computer Engineering Department University of Nebraska-Lincoln A Report on Research Sponsored by University Transportation Center for Railway Safety (UTCRS) University of Nebraska-Lincoln August 2018 The University of Texas Rio Grande Valley / 1201 West University Drive / ENGR Portable 1.100 / Edinburg, Texas 78539-2999 Phone: +1 (956) 665-8878 / Fax: +1 (956) [665-8879 / ](665-8879 / railwaysafety@utrgv.edu / railwaysafety.utrgv.edu)[railwaysafety@utrgv.edu / railwaysafety.utrgv.edu](665-8879 / railwaysafety@utrgv.edu / railwaysafety.utrgv.edu) TECHNICAL REPORT DOCUMENTATION PAGE | 1. Report No.
26-1121-0018-010 | 2. Government Accession No. | | | 3. Recipient’s Catalog No. | | | | --- | --- | --- | --- | --- | --- | --- | | 4. Title and Subtitle
Unifying Railcar Monitoring Sensor Data, Maintenance Records, and Railcar Usage Information through Big Data Processing for Optimizing Railcar Maintenance and Safety | | | | 5. Report Date
August 2018 | | | | | | | | 6. Performing Organization Code | | | | 7. Author(s)
Hamid Sharif, Ph.D.
Michael Hempel, Ph.D. | | | | 8. Performing Organization Report No.
26-1121-0018-010 | | | | 9. Performing Organization Name and Address
Nebraska Transportation Center
University of Nebraska-Lincoln
Prem S. Paul Research Center at Whittier School
2200 Vine Street
Lincoln, NE 68583-0853 | | | | 10. Work Unit No. | | | | | | | | 11. Contract or Grant No.
DTRT13-G-UTC59 | | | | 12. Sponsoring Agency Name and Address
University Transportation Center for Railway Safety
The University of Texas Rio Grande Valley
1201 W University Drive
Edinburg, Texas 78539 | | | | 13. Type of Report and Period Covered
Final Report (October 2016 – June 2018) | | | | | | | | 14. Sponsoring Agency Code | | | | 15. Supplementary Notes
Conducted in cooperation with the U.S. Department of Transportation, Federal Highway Administration. | | | | | | | | 16. Abstract
With this project, we investigated the use of Big Data Analytics to make rail transportation safer, by preventing derailments due to equipment failure. Railroads typically schedule railcar maintenance on best-practice intervals, which may not include the plethora of information available from their maintenance logs, track data, sensors information, bills of lading, manufacturer history, etc. This project explored the use of this data to adapt maintenance scheduling to reduce cost and increase safety. We showed the great potential inherent in this approach. | | | | | | | | 17. Key Words
Railroad, Maintenance, Big Data, scheduling, Random Forest, Rail Safety | | | 18. Distribution Statement
No restrictions. | | | | | 19. Security Classif. (of this report)
unclassified | | 20. Security Classif. (of this page)
unclassified | | | 21. No. of Pages
31 | 22. Price | Form DOT F 1700.7 (8-72) Reproduction of completed page authorized Table of Contents [Abstract .......................................................................................................................................... vi](Abstract .......................................................................................................................................... vi) [Chapter 1 Introduction ...............................](Chapter 1 Introduction ...............................)...................................................................................... 1 Chapter 2 Our Approach ............................................................................................

FAQs

What problem does the project address?

The project addresses derailments due to equipment failure by exploring ways to improve railcar maintenance safety decisions using data-driven analytics.

How does the approach differ from typical maintenance scheduling?

Railroads often rely on fixed best-practice intervals, while this project uses additional information from maintenance logs, sensors, track data, and other records to tailor scheduling.

What is the intended impact of using Big Data processing?

The project targets reduced maintenance cost and increased rail safety by adapting maintenance schedules based on integrated big data signals.

Sep 11, 2026

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