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Community detection in large scale congested urban road networks

Author

Listed:
  • Seyed Arman Haghbayan
  • Nikolas Geroliminis
  • Meisam Akbarzadeh

Abstract

Traffic congestion in large urban networks may take different shapes and propagates non-uniformly variations from day to day. Given the fact that congestion on a road segment is spatially correlated to adjacent roads and propagates spatiotemporally with finite speed, it is essential to describe the main pockets of congestion in a city with a small number of clusters. For example, the perimeter control with macroscopic fundamental diagrams is one of the effective traffic management tools. Perimeter control adjusts the inflow to pre-specified regions of a city through signal timing on the border of a region in order to optimize the traffic condition within the region. The precision of macroscopic fundamental diagrams depends on the homogeneity of traffic condition on road segments of the region. Hence, previous studies have defined the boundaries of the region under perimeter control subjected to the regional homogeneity. In this study, a cost-effective method is proposed for the mentioned problem that simultaneously considers homogeneity, contiguity and compactness of clusters and has a shorter computational time. Since it is necessary to control the cost and complexity of perimeter control in terms of the number of traffic signals, sparse parts of the network could be potential candidates for boundaries. Therefore, a community detection method (Infomap) is initially adopted and then those clusters are improved by refining the communities in relation to roads with the highest heterogeneity. The proposed method is applied to Shenzhen, China and San Francisco, USA and the outcomes are compared to previous studies. The results of comparison reveal that the proposed method is as effective as the best previous methods in detecting homogenous communities, but it outperforms them in contiguity. It is worth noting that this is the first method that guarantees the connectedness of clusters, which is a prerequisite of perimeter control.

Suggested Citation

  • Seyed Arman Haghbayan & Nikolas Geroliminis & Meisam Akbarzadeh, 2021. "Community detection in large scale congested urban road networks," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-14, November.
    • Handle: RePEc:plo:pone00:0260201
    DOI: 10.1371/journal.pone.0260201
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    References listed on IDEAS

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    1. Saeedmanesh, Mohammadreza & Geroliminis, Nikolas, 2016. "Clustering of heterogeneous networks with directional flows based on “Snake” similarities," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 250-269.
    2. Daganzo, Carlos F. & Geroliminis, Nikolas, 2008. "An analytical approximation for the macroscopic fundamental diagram of urban traffic," Transportation Research Part B: Methodological, Elsevier, vol. 42(9), pages 771-781, November.
    3. repec:cdl:itsrrp:qt4cb8h3jm is not listed on IDEAS
    4. Amin Mazloumian & Nikolas Geroliminis & Dirk Helbing, "undated". "The Spatial Variability of Vehicle Densities as Determinant of Urban Network Capacity," Working Papers CCSS-09-009, ETH Zurich, Chair of Systems Design.
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    Cited by:

    1. Wu, Jiaxin & Lu, Jing & Zhang, Lingye & Fan, Hanwen, 2024. "Spatial heterogeneity among different-sized port communities in directed-weighted global liner shipping network," Journal of Transport Geography, Elsevier, vol. 114(C).
    2. Shuaiming Chen & Ximing Ji & Haipeng Shao, 2024. "Revealing the Community Structure of Urban Bus Networks: a Multi-view Graph Learning Approach," Networks and Spatial Economics, Springer, vol. 24(3), pages 589-619, September.

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