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The Reversible Lane Network Design Problem (RL-NDP) for Smart Cities with Automated Traffic

Author

Listed:
  • Lígia Conceição

    (Research Center for Territory, Transports and Environment (CITTA), Department of Civil Engineering, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal)

  • Gonçalo Homem de Almeida Correia

    (Department of Transport and Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology, 2628 CN Delft, The Netherlands
    CITTA, Department of Civil Engineering, University of Coimbra, 3030-790 Coimbra, Portugal)

  • José Pedro Tavares

    (Research Center for Territory, Transports and Environment (CITTA), Department of Civil Engineering, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal)

Abstract

With automated vehicles (AVs), reversible lanes could be a sustainable transportation solution once there is vehicle-to-infrastructure connectivity informing AVs about the lane configuration changes. This paper introduced the reversible lane network design problem (RL-NDP), formulated in mixed-integer non-linear mathematical programming—both the traffic assignment and the reversible lane decisions were embedded. The model was applied on an hourly basis in the case study of the city of Delft, the Netherlands. Reversible lanes are examined under no traffic equilibrium (former paths are maintained); user-equilibrium (UE) assignment (AVs decide their own paths); and system-optimum (SO) traffic assignment (AVs are forced to follow SO paths). We found out that reversible lanes reduce congested roads, total travel times, and delays up to 36%, 9%, and 22%, respectively. The SO scenario was revealed to be beneficial in reducing the total travel time and congested roads in peak hours, whereas UE is equally optimal in the remaining hours. A dual-scenario mixing SO and UE throughout the day reduced congested roads, total travel times, and delay up to 40%, 8%, and 19%, respectively, yet increased 1% in travel distance. The spatial analysis suggested a substantial lane variability in the suburbs, yet a strong presence of reversible lanes in the city center.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:3:p:1226-:d:318081
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    References listed on IDEAS

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    1. T. L. Magnanti & R. T. Wong, 1984. "Network Design and Transportation Planning: Models and Algorithms," Transportation Science, INFORMS, vol. 18(1), pages 1-55, February.
    2. Jingyi Hua & Gang Ren & Yang Cheng & Chen Yu & Bin Ran, 2015. "Large-scale evacuation network optimization: a bi-level control method with uncertain arterial demand," Transportation Planning and Technology, Taylor & Francis Journals, vol. 38(7), pages 777-794, October.
    3. Scott B. Kelley & Bradley W. Lane & John M. DeCicco, 2019. "Pumping the Brakes on Robot Cars: Current Urban Traveler Willingness to Consider Driverless Vehicles," Sustainability, MDPI, vol. 11(18), pages 1-15, September.
    4. Correia, Gonçalo Homem de Almeida & van Arem, Bart, 2016. "Solving the User Optimum Privately Owned Automated Vehicles Assignment Problem (UO-POAVAP): A model to explore the impacts of self-driving vehicles on urban mobility," Transportation Research Part B: Methodological, Elsevier, vol. 87(C), pages 64-88.
    5. Ben-Ayed, Omar & Boyce, David E. & Blair, Charles E., 1988. "A general bilevel linear programming formulation of the network design problem," Transportation Research Part B: Methodological, Elsevier, vol. 22(4), pages 311-318, August.
    6. Debbie Hopkins & Tim Schwanen, 2018. "Automated Mobility Transitions: Governing Processes in the UK," Sustainability, MDPI, vol. 10(4), pages 1-19, March.
    7. Guangbing Xiao & Haibo Zhang & Ning Sun & Yong Chen & Jiamin Shi & Yong Zhang, 2019. "Cooperative Bargain for the Autonomous Separation of Traffic Flows in Smart Reversible Lanes," Complexity, Hindawi, vol. 2019, pages 1-12, October.
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    Cited by:

    1. 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.
    2. 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).
    3. Vilaça, Mariana & Santos, Gonçalo & Oliveira, Mónica S.A. & Coelho, Margarida C. & Correia, Gonçalo H.A., 2022. "Life cycle assessment of shared and private use of automated and electric vehicles on interurban mobility," Applied Energy, Elsevier, vol. 310(C).

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