Print Email Facebook Twitter Quantification and comparison of hierarchy in Public Transport Networks Title Quantification and comparison of hierarchy in Public Transport Networks Author Wang, Z. (TU Delft Transport and Planning) Huang, Ketong (Beijing Transport Institute; Student TU Delft) Massobrio, R.M. (TU Delft Transport and Planning; University of Cadiz) Bombelli, A. (TU Delft Air Transport & Operations) Cats, O. (TU Delft Transport and Planning) Date 2023 Abstract Network hierarchy describes the relative arrangement of network elements and reflects its fundamental structure. We propose a multi-dimensional topology-based method for quantifying and comparing the extent to which different Public Transport Networks (PTNs) exhibit a hierarchical structure. The proposed method considers the uneven distribution of node importance with different definitions (e.g., degree centrality and betweenness centrality) in a PTN, the clustering of nodes and the node connection patterns. We apply the developed method on 63 high-capacity PTNs worldwide using General Transit Feed Specification (GTFS) data. In addition to global indicators, we use the goodness-of-fit between the probability density function of local indicators and a skew-normal distribution to quantify the extent of PTN hierarchy. Results show that the scale-free network structure and preferential attachment do not vary much across PTNs. In contrast, stop accessibility and traffic intermediacy vary considerably across PTNs as reflected by the closeness centrality and betweenness centrality distributions. Lastly, metro systems exhibit a more hierarchical structure than their tram and Bus Rapid Transit (BRT) counterparts. This work makes a first step towards a better mapping and comparison of different PTNs, which can assist academics and practitioners in better (re)designing and planning the PTNs of the future. Subject Public transport networksHierarchyNetwork scienceTopologyGeneral Transit Feed Specification (GTFS) To reference this document use: http://resolver.tudelft.nl/uuid:d7be3bdc-bfaf-4ff1-b024-61d8c903c2ac DOI https://doi.org/10.1016/j.physa.2023.129479 ISSN 0378-4371 Source Physica A: Statistical Mechanics and its Applications, 634 Part of collection Institutional Repository Document type journal article Rights © 2023 Z. Wang, Ketong Huang, R.M. Massobrio, A. Bombelli, O. Cats Files PDF 1_s2.0_S0378437123010348_main.pdf 1.3 MB Close viewer /islandora/object/uuid:d7be3bdc-bfaf-4ff1-b024-61d8c903c2ac/datastream/OBJ/view