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Hierarchy is regarded as a natural phenomenon of public transport networks (PTN). The imbalanced distribution of passenger flow result in a hierarchical structure of PTN and it is also related to the development of technology and the introduction of new modes. However, there is still a lack of knowledge on how to identify the hierarchical structure of the multi-layer PTN. This study proposes a three-step passenger transfer flow based methodology for separating and ranking the PTN: (1) using passenger journey data to derive transfer flow matrix; (2) applying network representation with Louvain method of community detection to separate the PTN layers; (3) performing ranking method, separating inner-transfer and intertransfer flow. To demonstrate our method, we use one-month smart card data of The Hague, the Netherlands provided by the PTN operator HTM. Our results show that our method is able to, regardless of the geographic location and the mode of transportation, identify the hierarchy of PTN based on the passenger transfer flow pattern. Temporal attributes are also discussed to illustrate how hierarchy is time-dependent, e.g. with respect to the day of the week and the time of the day. Our method supports public transport (PT) operators during design and optimization of PTN and in determining which sets of higherlevel service to prioritize during different time periods.

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With the aim of supporting future traffic needs, an account of how to reconstruct an existing cyclic timetable by inserting additional train services will be given in this paper. The Timetable-based Extra Train Services Inserting (TETSI) problem is regarded as an integration of railway scheduling and rescheduling problem. The TETSI problem therefore is considered involving many constraints, such as flexible running times, dwell times, headway and time windows. Characterized based on an event-activity graph, a general Mixed Integer Program model for this problem is formulated. In addition, several extensions to the general model are further proposed. The real-world constraints that concerning the acceleration and deceleration times, priority for overtaking, allowed adjustments, periodic structure and frequency of services are incorporated into the general model. From numerical investigations using data from Shanghai-Hangzhou High-Speed Railway in China, the proposed framework and associated techniques are tested and shown to be effective.

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