IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v174y2023ics0960077923006719.html
   My bibliography  Save this article

Exploring the relationship between the spatial distribution of roads and universal pattern of travel-route efficiency in urban road networks

Author

Listed:
  • Lee, Minjin
  • Cheon, SangHyun
  • Son, Seung-Woo
  • Lee, Mi Jin
  • Lee, Sungmin

Abstract

Urban road networks are well-known to exhibit universal characteristics and scale-invariant patterns, despite the different geographical and historical contexts of cities. Previous studies on the universal characteristics of urban road networks have mostly focused on their network properties but have often ignored the spatial network structures. To address this research gap, we explore the underlying spatial patterns of road networks. We examine the travel-route efficiency in a given road network across 70 global cities, which provides information on the usage pattern and functionality of the road structure. The efficiency of travel routes is measured by analyzing the detour patterns, as determined by the detour index (DI). The DI is a long-standing popular measure, but its spatial pattern has been barely considered so far. In this study, we investigate the behavior of DI with respect to spatial variables by scanning the network radially from a city center. Through empirical analysis, we first discover universal properties in DI throughout most cities, which are summarized as a constant behavior of DI regardless of the radial position from a city center and a clear collapse into a single curve for DIs for various radii with respect to the angular distance. Especially the latter enables us to determine the scaling factor in the length scale. We further reveal that the universal pattern is induced by the center-periphery spatial structure of urban roads through the model study of an artificial road network. In addition to exploring the universality of DI, we delve into the specific characteristics of DI associated with the unique internal structure of individual cities. By visualizing the spatial DI network on city maps, we identify distinct city-specific DI characteristics. The case studies of selected cities demonstrate that our proposed method of spatial DI networks has the potential for practical implications in analyzing individual cities.

Suggested Citation

  • Lee, Minjin & Cheon, SangHyun & Son, Seung-Woo & Lee, Mi Jin & Lee, Sungmin, 2023. "Exploring the relationship between the spatial distribution of roads and universal pattern of travel-route efficiency in urban road networks," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
  • Handle: RePEc:eee:chsofr:v:174:y:2023:i:c:s0960077923006719
    DOI: 10.1016/j.chaos.2023.113770
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077923006719
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chaos.2023.113770?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Alex Anas & Richard Arnott & Kenneth A. Small, 1998. "Urban Spatial Structure," Journal of Economic Literature, American Economic Association, vol. 36(3), pages 1426-1464, September.
    2. David J Giacomin & David M Levinson, 2015. "Road network circuity in metropolitan areas," Environment and Planning B, , vol. 42(6), pages 1040-1053, November.
    3. Minjin Lee & Hugo Barbosa & Hyejin Youn & Petter Holme & Gourab Ghoshal, 2017. "Morphology of travel routes and the organization of cities," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
    4. Chen, Yanguang & Wang, Yihan & Li, Xijing, 2019. "Fractal dimensions derived from spatial allometric scaling of urban form," Chaos, Solitons & Fractals, Elsevier, vol. 126(C), pages 122-134.
    5. Alec Kirkley & Hugo Barbosa & Marc Barthelemy & Gourab Ghoshal, 2018. "From the betweenness centrality in street networks to structural invariants in random planar graphs," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    6. Levinson, David & El-Geneidy, Ahmed, 2009. "The minimum circuity frontier and the journey to work," Regional Science and Urban Economics, Elsevier, vol. 39(6), pages 732-738, November.
    7. Cirunay, Michelle T. & Batac, Rene C., 2023. "Evolution of the periphery of a self-organized road network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 617(C).
    8. Merchán, Daniel & Winkenbach, Matthias & Snoeck, André, 2020. "Quantifying the impact of urban road networks on the efficiency of local trips," Transportation Research Part A: Policy and Practice, Elsevier, vol. 135(C), pages 38-62.
    9. Rémi Lemoy & Geoffrey Caruso, 2020. "Evidence for the homothetic scaling of urban forms," Environment and Planning B, , vol. 47(5), pages 870-888, June.
    10. Dani Broitman & Eric Koomen, 2020. "The attraction of urban cores: Densification in Dutch city centres," Urban Studies, Urban Studies Journal Limited, vol. 57(9), pages 1920-1939, July.
    11. S. Chan & R. Donner & S. Lämmer, 2011. "Urban road networks — spatial networks with universal geometric features?," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 84(4), pages 563-577, December.
    12. Wang, Fahui & Antipova, Anzhelika & Porta, Sergio, 2011. "Street centrality and land use intensity in Baton Rouge, Louisiana," Journal of Transport Geography, Elsevier, vol. 19(2), pages 285-293.
    13. Lämmer, Stefan & Gehlsen, Björn & Helbing, Dirk, 2006. "Scaling laws in the spatial structure of urban road networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 363(1), pages 89-95.
    14. Han Yue & Xinyan Zhu, 2019. "Exploring the Relationship between Urban Vitality and Street Centrality Based on Social Network Review Data in Wuhan, China," Sustainability, MDPI, vol. 11(16), pages 1-19, August.
    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. Zhang, Tong & Zeng, Zhe & Jia, Tao & Li, Jing, 2016. "Examining the amenability of urban street networks for locating facilities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 469-479.
    2. Merchán, Daniel & Winkenbach, Matthias & Snoeck, André, 2020. "Quantifying the impact of urban road networks on the efficiency of local trips," Transportation Research Part A: Policy and Practice, Elsevier, vol. 135(C), pages 38-62.
    3. Boeing, Geoff, 2018. "Urban Spatial Order: Street Network Orientation, Configuration, and Entropy," SocArXiv qj3p5, Center for Open Science.
    4. Xiaoshu Cao & Feiwen Liang & Huiling Chen & Yongwei Liu, 2017. "Circuity Characteristics of Urban Travel Based on GPS Data: A Case Study of Guangzhou," Sustainability, MDPI, vol. 9(11), pages 1-21, November.
    5. David Levinson, 2012. "Network Structure and City Size," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-11, January.
    6. repec:asg:wpaper:1049 is not listed on IDEAS
    7. Perez, Yuri & Pereira, Fabio Henrique, 2021. "Simulation of traffic light disruptions in street networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    8. Zexun Chen & Sean Kelty & Alexandre G. Evsukoff & Brooke Foucault Welles & James Bagrow & Ronaldo Menezes & Gourab Ghoshal, 2022. "Contrasting social and non-social sources of predictability in human mobility," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    9. Yang, Wenyue & Chen, Huiling & Wang, Wulin, 2020. "The path and time efficiency of residents' trips of different purposes with different travel modes: An empirical study in Guangzhou, China," Journal of Transport Geography, Elsevier, vol. 88(C).
    10. Huang, Jie & Levinson, David M., 2015. "Circuity in urban transit networks," Journal of Transport Geography, Elsevier, vol. 48(C), pages 145-153.
    11. Boeing, Geoff, 2017. "Methods and Measures for Analyzing Complex Street Networks and Urban Form," SocArXiv 93h82, Center for Open Science.
    12. Benita, Francisco & Piliouras, Georgios, 2020. "Location, location, usage: How different notions of centrality can predict land usage in Singapore," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    13. Yat Yen & Pengjun Zhao & Muhammad T Sohail, 2021. "The morphology and circuity of walkable, bikeable, and drivable street networks in Phnom Penh, Cambodia," Environment and Planning B, , vol. 48(1), pages 169-185, January.
    14. Pengjun Zhao & Hao Wang & Qiyang Liu & Xiao-Yong Yan & Jingzhong Li, 2024. "Unravelling the spatial directionality of urban mobility," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    15. Bono, Flavio & Gutiérrez, Eugenio & Poljansek, Karmen, 2010. "Road traffic: A case study of flow and path-dependency in weighted directed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(22), pages 5287-5297.
    16. Boeing, Geoff, 2019. "The Morphology and Circuity of Walkable and Drivable Street Networks," SocArXiv edj2s, Center for Open Science.
    17. 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.
    18. Cirunay, Michelle T. & Batac, Rene C., 2023. "Evolution of the periphery of a self-organized road network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 617(C).
    19. Batac, Rene C. & Cirunay, Michelle T., 2022. "Shortest paths along urban road network peripheries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    20. Sergio Porta & Vito Latora & Fahui Wang & Salvador Rueda & Emanuele Strano & Salvatore Scellato & Alessio Cardillo & Eugenio Belli & Francisco CÃ rdenas & Berta Cormenzana & Laura Latora, 2012. "Street Centrality and the Location of Economic Activities in Barcelona," Urban Studies, Urban Studies Journal Limited, vol. 49(7), pages 1471-1488, May.
    21. Moreno Bonaventura & Luca Maria Aiello & Daniele Quercia & Vito Latora, 2021. "Predicting urban innovation from the US Workforce Mobility Network," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-9, December.

    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:eee:chsofr:v:174:y:2023:i:c:s0960077923006719. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.