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Development of Algorithms for Effective Resource Allocation among Highway–Rail Grade Crossings: A Case Study for the State of Florida

Author

Listed:
  • Masoud Kavoosi

    (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)

  • Junayed Pasha

    (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)

  • 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

Smart cities directly rely on a variety of elements, including water, gas, electricity, buildings, services, transportation networks, and others. Lack of properly designed transportation networks may cause different economic and safety concerns. Highway–rail grade crossings are known to be a hazardous point in the transportation network, considering a remarkable number of accidents recorded annually between highway users and trains, and even solely between highway users at highway–rail grade crossings. Hence, safety improvement at highway–rail grade crossings is a challenging issue for smart city authorities, given limitations in monetary resources. In this study, two optimization models are developed for resource allocation among highway–rail grade crossings to minimize the overall hazard and the overall hazard severity, taking into account the available budget limitations. The optimization models are solved by CPLEX to the global optimality. Moreover, some heuristic algorithms are proposed as well. A case study focusing on the public highway–rail grade crossings in the State of Florida is performed to evaluate the effectiveness of the developed optimization models and the solution methodologies. In terms of the computational time, all the solution approaches are found to be effective decision support tools from the practical standpoint. Moreover, the results demonstrate that some of the developed heuristic algorithms can provide near-optimal solutions. Therefore, the smart city authorities can utilize the proposed heuristics as decision support tools for effective resource allocation among highway–rail grade crossings.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:6:p:1419-:d:334041
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    References listed on IDEAS

    as
    1. Oluwatosin Theophilus & Maxim A. Dulebenets & Junayed Pasha & Olumide F. Abioye & Masoud Kavoosi, 2019. "Truck Scheduling at Cross-Docking Terminals: A Follow-Up State-Of-The-Art Review," Sustainability, MDPI, vol. 11(19), pages 1-23, September.
    2. Dulebenets, Maxim A., 2019. "A Delayed Start Parallel Evolutionary Algorithm for just-in-time truck scheduling at a cross-docking facility," International Journal of Production Economics, Elsevier, vol. 212(C), pages 236-258.
    3. 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.
    4. Xian Yang & Yong Li & Ye Cai & Yijia Cao & Kwang Y. Lee & Zhijian Jia, 2018. "Impact of Road-Block on Peak-Load of Coupled Traffic and Energy Transportation Networks," Energies, MDPI, vol. 11(7), pages 1-12, July.
    5. Suk Ho Jin & Lianxi Bai & Jang Yeop Kim & Suk Jae Jeong & Kyung Sup Kim, 2017. "Analysis of GHG Emission Reduction in South Korea Using a CO 2 Transportation Network Optimization Model," Energies, MDPI, vol. 10(7), pages 1-18, July.
    6. M. Hadi Amini & Orkun Karabasoglu, 2018. "Optimal Operation of Interdependent Power Systems and Electrified Transportation Networks," Energies, MDPI, vol. 11(1), pages 1-25, January.
    7. Olumide F. Abioye & Maxim A. Dulebenets & Junayed Pasha & Masoud Kavoosi, 2019. "A Vessel Schedule Recovery Problem at the Liner Shipping Route with Emission Control Areas," Energies, MDPI, vol. 12(12), pages 1-28, June.
    8. Maxim A. Dulebenets, 2018. "A Diploid Evolutionary Algorithm for Sustainable Truck Scheduling at a Cross-Docking Facility," Sustainability, MDPI, vol. 10(5), pages 1-23, April.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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).

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