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A Comprehensive Assessment of the Existing Accident and Hazard Prediction Models for the Highway-Rail Grade Crossings in the State of Florida

Author

Listed:
  • Junayed Pasha

    (Department of Civil & Environmental Engineering, Florida A&M University-Florida State University, Tallahassee, FL 32310-6046, USA)

  • Maxim A. Dulebenets

    (Department of Civil & Environmental Engineering, Florida A&M University-Florida State University, Tallahassee, FL 32310-6046, USA)

  • Olumide F. Abioye

    (Department of Civil & Environmental Engineering, Florida A&M University-Florida State University, Tallahassee, FL 32310-6046, USA)

  • Masoud Kavoosi

    (Department of Civil & Environmental Engineering, Florida A&M University-Florida State University, Tallahassee, FL 32310-6046, USA)

  • Ren Moses

    (Department of Civil & Environmental Engineering, Florida A&M University-Florida State University, Tallahassee, FL 32310-6046, USA)

  • John Sobanjo

    (Department of Civil & Environmental Engineering, Florida A&M University-Florida State University, Tallahassee, FL 32310-6046, USA)

  • Eren E. Ozguven

    (Department of Civil & Environmental Engineering, Florida A&M University-Florida State University, Tallahassee, FL 32310-6046, USA)

Abstract

Accidents at highway-rail grade crossings can cause fatalities and injuries, as well as significant property damages. In order to prevent accidents, certain upgrades need to be made at highway-rail grade crossings. However, due to limited monetary resources, only the most hazardous highway-rail grade crossings should receive a priority for upgrading. Hence, accident/hazard prediction models are required to identify the most hazardous highway-rail grade crossings for safety improvement projects. This study selects and evaluates the accident and hazard prediction models found in the highway-rail grade crossing safety literature to rank the highway-rail grade crossings in the State of Florida. Three approaches are undertaken to evaluate the candidate accident and hazard prediction models, including the chi-square statistic, grouping of crossings based on the actual accident data, and Spearman rank correlation coefficient. The analysis was conducted for the 589 highway-rail grade crossings located in the State of Florida using the data available through the highway-rail grade crossing inventory database maintained by the Federal Railroad Administration. As a result of the performed analysis, a new hazard prediction model, named as the Florida Priority Index Formula, is recommended to rank/prioritize the highway-rail grade crossings in the State of Florida. The Florida Priority Index Formula provides a more accurate ranking of highway-rail grade crossings as compared to the alternative methods. The Florida Priority Index Formula assesses the potential hazard of a given highway-rail grade crossing based on the average daily traffic volume, average daily train volume, train speed, existing traffic control devices, accident history, and crossing upgrade records.

Suggested Citation

  • Junayed Pasha & Maxim A. Dulebenets & Olumide F. Abioye & Masoud Kavoosi & Ren Moses & John Sobanjo & Eren E. Ozguven, 2020. "A Comprehensive Assessment of the Existing Accident and Hazard Prediction Models for the Highway-Rail Grade Crossings in the State of Florida," Sustainability, MDPI, vol. 12(10), pages 1-27, May.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:10:p:4291-:d:362327
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    References listed on IDEAS

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    1. Shannon C. Mok & Ian Savage, 2005. "Why Has Safety Improved at Rail‐Highway Grade Crossings?," Risk Analysis, John Wiley & Sons, vol. 25(4), pages 867-881, August.
    2. Masoud Kavoosi & Maxim A. Dulebenets & Junayed Pasha & Olumide F. Abioye & Ren Moses & John Sobanjo & Eren E. Ozguven, 2020. "Development of Algorithms for Effective Resource Allocation among Highway–Rail Grade Crossings: A Case Study for the State of Florida," Energies, MDPI, vol. 13(6), pages 1-28, March.
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    Cited by:

    1. Anna Dolinayova & Vladislav Zitricky & Lenka Cerna, 2020. "Decision-Making Process in the Case of Insufficient Rail Capacity," Sustainability, MDPI, vol. 12(12), pages 1-21, June.
    2. Singh, Prashant & Pasha, Junayed & Moses, Ren & Sobanjo, John & Ozguven, Eren E. & Dulebenets, Maxim A., 2022. "Development of exact and heuristic optimization methods for safety improvement projects at level crossings under conflicting objectives," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    3. Prashant Singh & Junayed Pasha & Amir Khorram-Manesh & Krzysztof Goniewicz & Abdolreza Roshani & Maxim A. Dulebenets, 2021. "A Holistic Analysis of Train-Vehicle Accidents at Highway-Rail Grade Crossings in Florida," Sustainability, MDPI, vol. 13(16), pages 1-28, August.
    4. Yan, Dongyang & Li, Keping & Zhu, Qiaozhen & Liu, Yanyan, 2023. "A railway accident prevention method based on reinforcement learning – Active preventive strategy by multi-modal data," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    5. Joaquin Haces-Garcia & Arturo Haces-Garcia & Francisco Haces-Garcia & Francisco Haces-Fernandez, 2021. "Advanced Warning System to Improve Safety at Train Grade Crossings," Sustainability, MDPI, vol. 13(21), pages 1-16, October.
    6. Ercegovac, Pamela & Stojić, Gordan & Tanackov, Ilija & Sremac, Siniša, 2022. "Application of Statistical Analysis for Risk Estimate of Railway Accidents and Traffic Incidents at Level Crossings," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2022), Hybrid Conference, Opatija, Croatia, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Hybrid Conference, Opatija, Croatia, 17-18 June 2022, pages 225-238, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.

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