Public transport networks (PTNs) have an impact on both travelers’ behavior and system operations. A meaningful approach to investigate PTNs is via their topological structure because it was found to be correlated to the operational performance, the total ridership, consumer experience, passenger flow distribution, and network resilience. A handful number of past studies characterized and compare PTNs structure, but little is known about broader or general classification worldwide. This study examined how PTNs can be clustered into groups when considering multiple features. Centralization, accessibility, robustness, service connectivity and directness are five main considered network features used in previous studies to analyze the network structure. K-means, hierarchical clustering and principal component analysis were performed to identify the cluster of PTNs defined from those five features. To illustrate the method, we conducted a case study of 20 real-life rail-bound networks worldwide generated by the up-to-date general transit feed specification (GTFS) data. As a result, we were able to identify four main meaningful clusters: tram, tram-related, metro and tram and mixed modes. Although modes of transportation and the size of the network were not parts of features, they heavily influence the clusters. The proposed method show automatic and reproducible tools to empirically identify topological patterns of PTNs.

In this study, a data-driven, generic and transfer-based methodology for separation and ranking the PTNs has been put forward. With the hierarchy of a network, this is beneficiary for the management and operation of operators for focusing on the higher level network layer and in turn provide better service for passengers. The study introduces three steps to rank the hierarchy of a PTN: (1) using the passenger journey and ride data to derive transfer flow matrix; (2) applying C-space network representation with community detection method to separate and visualize the PTN layer; (3) performing ranking method, regarding inner- and intra- transfer flow. To this end, the hierarchy of a PTN could be presented with temporal attributes. Different day of week and various time period of a day could potentially yield different hierarchy. The proposed unsupervised learning algorithm is based on passenger transfer flow data, independent from geographic location and the mode of transportation. The study shows that the level is changing based on the selected time slot and can be a mixture of different modes, which is dissimilar from the hierarchy purely based on qualitative method.