IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-52313-6.html
   My bibliography  Save this article

Similarity and economy of scale in urban transportation networks and optimal transport-based infrastructures

Author

Listed:
  • Daniela Leite

    (Max Planck Institute for Intelligent Systems, Cyber Valley)

  • Caterina De Bacco

    (Max Planck Institute for Intelligent Systems, Cyber Valley)

Abstract

Designing and optimizing the structure of urban transportation networks is a challenging task. In this study, we propose a method inspired by optimal transport theory and the principle of economy of scale that uses little information in input to generate structures that are similar to those of public transportation networks. Contrarily to standard approaches, it does not assume any initial backbone network infrastructure but rather extracts this directly from a continuous space using only a few origin and destination points, generating networks from scratch. Analyzing a set of urban train, tram and subway networks, we find a noteworthy degree of similarity in several of the studied cases between simulated and real infrastructures. By tuning one parameter, our method can simulate a range of different subway, tram and train networks that can be further used to suggest possible improvements in terms of relevant transportation properties. Outputs of our algorithm provide naturally a principled quantitative measure of similarity between two networks that can be used to automatize the selection of similar simulated networks.

Suggested Citation

  • Daniela Leite & Caterina De Bacco, 2024. "Similarity and economy of scale in urban transportation networks and optimal transport-based infrastructures," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52313-6
    DOI: 10.1038/s41467-024-52313-6
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-52313-6
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-024-52313-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Cantarella, G.E. & Pavone, G. & Vitetta, A., 2006. "Heuristics for urban road network design: Lane layout and signal settings," European Journal of Operational Research, Elsevier, vol. 175(3), pages 1682-1695, December.
    2. Derrible, Sybil & Kennedy, Christopher, 2010. "The complexity and robustness of metro networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(17), pages 3678-3691.
    3. Michael Mc Gettrick, 2020. "Correction to: The role of city geometry in determining the utility of a small urban light rail/tram system," Public Transport, Springer, vol. 12(3), pages 517-518, October.
    4. David Levinson, 2012. "Network Structure and City Size," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-11, January.
    5. Rémi Louf & Camille Roth & Marc Barthelemy, 2014. "Scaling in Transportation Networks," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-8, July.
    6. Farahani, Reza Zanjirani & Miandoabchi, Elnaz & Szeto, W.Y. & Rashidi, Hannaneh, 2013. "A review of urban transportation network design problems," European Journal of Operational Research, Elsevier, vol. 229(2), pages 281-302.
    7. 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.
    8. Cats, Oded, 2017. "Topological evolution of a metropolitan rail transport network: The case of Stockholm," Journal of Transport Geography, Elsevier, vol. 62(C), pages 172-183.
    9. Silberston, Aubrey, 1972. "Economies of Scale in Theory and Practice," Economic Journal, Royal Economic Society, vol. 82(325), pages 369-391, Supplemen.
    10. Rui Ding & Norsidah Ujang & Hussain Bin Hamid & Mohd Shahrudin Abd Manan & Rong Li & Safwan Subhi Mousa Albadareen & Ashkan Nochian & Jianjun Wu, 2019. "Application of Complex Networks Theory in Urban Traffic Network Researches," Networks and Spatial Economics, Springer, vol. 19(4), pages 1281-1317, December.
    11. Michael Mc Gettrick, 2020. "The role of city geometry in determining the utility of a small urban light rail/tram system," Public Transport, Springer, vol. 12(1), pages 233-259, March.
    12. Sybil Derrible & Christopher Kennedy, 2010. "Characterizing metro networks: state, form, and structure," Transportation, Springer, vol. 37(2), pages 275-297, March.
    13. Jingyi Lin & Yifang Ban, 2013. "Complex Network Topology of Transportation Systems," Transport Reviews, Taylor & Francis Journals, vol. 33(6), pages 658-685, November.
    14. Zhang, Jianhua & Zhao, Mingwei & Liu, Haikuan & Xu, Xiaoming, 2013. "Networked characteristics of the urban rail transit networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1538-1546.
    15. Laporte, G. & Mesa, J.A. & Ortega, F.A. & Perea, F., 2011. "Planning rapid transit networks," Socio-Economic Planning Sciences, Elsevier, vol. 45(3), pages 95-104, September.
    16. Javier Durán-Micco & Pieter Vansteenwegen, 2022. "A survey on the transit network design and frequency setting problem," Public Transport, Springer, vol. 14(1), pages 155-190, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cats, Oded & Krishnakumari, Panchamy, 2020. "Metropolitan rail network robustness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    2. Zhang, Jianhua & Wang, Meng, 2019. "Transportation functionality vulnerability of urban rail transit networks based on movingblock: The case of Nanjing metro," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    3. Luo, Ding & Cats, Oded & van Lint, Hans & Currie, Graham, 2019. "Integrating network science and public transport accessibility analysis for comparative assessment," Journal of Transport Geography, Elsevier, vol. 80(C).
    4. Cats, Oded, 2017. "Topological evolution of a metropolitan rail transport network: The case of Stockholm," Journal of Transport Geography, Elsevier, vol. 62(C), pages 172-183.
    5. Yang, Zhijie & Chen, Xiaolong, 2018. "Evolution assessment of Shanghai Urban Rail Transit Network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1263-1274.
    6. Siddharth Patwardhan & Marc Barthelemy & Şirag Erkol & Santo Fortunato & Filippo Radicchi, 2024. "Symmetry breaking in optimal transport networks," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    7. Kopsidas, Athanasios & Kepaptsoglou, Konstantinos, 2022. "Identification of critical stations in a Metro System: A substitute complex network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    8. Xueguo Xu & Chen Xu & Wenxin Zhang, 2022. "Research on the Destruction Resistance of Giant Urban Rail Transit Network from the Perspective of Vulnerability," Sustainability, MDPI, vol. 14(12), pages 1-26, June.
    9. Zhang, Jianhua & Wang, Shuliang & Wang, Xiaoyuan, 2018. "Comparison analysis on vulnerability of metro networks based on complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 72-78.
    10. Jing, Weiwei & Xu, Xiangdong & Pu, Yichao, 2020. "Route redundancy-based approach to identify the critical stations in metro networks: A mean-excess probability measure," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    11. B. G. Tóth, 2021. "The effect of attacks on the railway network of Hungary," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(2), pages 567-587, June.
    12. Nir Sharav & Yoram Shiftan, 2021. "Optimal Urban Transit Investment Model and Its Application," Sustainability, MDPI, vol. 13(16), pages 1-29, August.
    13. Laura Alessandretti & Luis Guillermo Natera Orozco & Meead Saberi & Michael Szell & Federico Battiston, 2023. "Multimodal urban mobility and multilayer transport networks," Environment and Planning B, , vol. 50(8), pages 2038-2070, October.
    14. Ali Enes Dingil & Federico Rupi & Domokos Esztergár-Kiss, 2021. "An Integrative Review of Socio-Technical Factors Influencing Travel Decision-Making and Urban Transport Performance," Sustainability, MDPI, vol. 13(18), pages 1-20, September.
    15. Elnaz Miandoabchi & Reza Farahani & W. Szeto, 2012. "Bi-objective bimodal urban road network design using hybrid metaheuristics," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(4), pages 583-621, December.
    16. Shaopei Chen & Dachang Zhuang, 2020. "Evolution and Evaluation of the Guangzhou Metro Network Topology Based on an Integration of Complex Network Analysis and GIS," Sustainability, MDPI, vol. 12(2), pages 1-18, January.
    17. Khooban, Zohreh & Farahani, Reza Zanjirani & Miandoabchi, Elnaz & Szeto, W.Y., 2015. "Mixed network design using hybrid scatter search," European Journal of Operational Research, Elsevier, vol. 247(3), pages 699-710.
    18. Zhang, Jianhua & Wang, Ziqi & Wang, Shuliang & Shao, Wenchao & Zhao, Xun & Liu, Weizhi, 2021. "Vulnerability assessments of weighted urban rail transit networks with integrated coupled map lattices," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    19. Hu, Xinlei & Huang, Jie & Shi, Feng, 2022. "A robustness assessment with passenger flow data of high-speed rail network in China," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    20. Chen, Junlan & Pu, Ziyuan & Guo, Xiucheng & Cao, Jieyu & Zhang, Fang, 2023. "Multiperiod metro timetable optimization based on the complex network and dynamic travel demand," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52313-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.