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Lane Optimization of Highway Reconstruction and Expansion Work Zone Considering Carbon Dioxide Emission Factors

Author

Listed:
  • Chi Sun

    (Beijing Laboratory of General Aviation Technology, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

  • Weiqi Hong

    (Beijing Laboratory of General Aviation Technology, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

  • Hao Li

    (Beijing Laboratory of General Aviation Technology, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

  • Chenjing Zhou

    (Beijing Laboratory of General Aviation Technology, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

Abstract

During the highway reconstruction and expansion, some lanes are often closed on the construction side to ensure that the construction is carried out normally. The presence of the work zone increases the traffic pressure on the construction side of the highway, causing traffic congestion, increased C O 2 emissions from motor vehicles, and increasing environmental pollution. The bi-level programming model was developed based on the objective of minimizing the travel time and total C O 2 emissions of the system so as to solve it using a quantum particle swarm algorithm with high convergence speed and high intelligence to form the lane optimization scheme for the three forms of reclosing and expanding six-lane highways in both directions. The results show that reasonable use of opposite non-construction lanes in the work zone of a partially closed highway expansion can reduce the total system travel cost, alleviate traffic congestion, reduce C O 2 emissions, and contribute to the sustainable development of transportation, as well as the environment.

Suggested Citation

  • Chi Sun & Weiqi Hong & Hao Li & Chenjing Zhou, 2022. "Lane Optimization of Highway Reconstruction and Expansion Work Zone Considering Carbon Dioxide Emission Factors," Sustainability, MDPI, vol. 14(19), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12090-:d:924094
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    References listed on IDEAS

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    1. Lígia Conceição & Gonçalo Homem de Almeida Correia & José Pedro Tavares, 2020. "The Reversible Lane Network Design Problem (RL-NDP) for Smart Cities with Automated Traffic," Sustainability, MDPI, vol. 12(3), pages 1-22, February.
    2. Marguerite Frank & Philip Wolfe, 1956. "An algorithm for quadratic programming," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 3(1‐2), pages 95-110, March.
    3. Urmila Pyakurel & Tanka Nath Dhamala & Stephan Dempe, 2017. "Efficient continuous contraflow algorithms for evacuation planning problems," Annals of Operations Research, Springer, vol. 254(1), pages 335-364, July.
    4. Urmila Pyakurel & Tanka Nath Dhamala, 2016. "Continuous Time Dynamic Contraflow Models and Algorithms," Advances in Operations Research, Hindawi, vol. 2016, pages 1-7, March.
    5. Di, Zhen & Yang, Lixing, 2020. "Reversible lane network design for maximizing the coupling measure between demand structure and network structure," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
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