## 2 Literature Review
[29] solves metro network expansion using deterministic and robust optimisation models; Ref. [30] deals with real-time optimisation of urban rail transit train scheduling via advantage actor-critic deep reinforcement learning; and Ref. [31] deals with collaborative optimisation of passenger flow control and bus-bridging services in commuting metro lines. Line planning determines the critical service contents of a passenger railway schedule. In real-world scenarios, passengers' travel expectations or preferences of service quality tend to be heterogeneous among different origin-destination (in short O-D) pairs or even in the same O-D pair, which requires railway operators to schedule train services catering to those diverse passenger demands. However, previous line-planning approaches either treated passengers homogeneously or roughly divided simple passenger groups by travel purposes or service types. In [32], the authors focused on each passenger O-D pair as an individual O-D market and proposed a line-planning approach where they concluded train lines can deliver matched service levels of trains for each O-D market. Based on the set-covering problem (in short SCP), we establish a novel bi-objective mixed-integer linear programming (in short MILP) model that considers the benefits of both railway operators and passengers. Multiple service qualities for each passenger O-D pair, such as travel speed, direct or transfer connection, frequency, price, etc., are seen as the objects to be \"covered\" by train lines to achieve a more accurate supply-demand match. Today, the planning of metropolitan areas considers improving the quality of life of their inhabitants, and urban mobility is one of the main concerns. Studies point out that investments in public transportation and other modes are aimed at the overall improvement of mobility. However, there is a gap in the proper tools for optimising public transport networks. In fact, network optimisation is an NP-hard problem (in short a nondeterministic polynomial time problem), and there are usually many conflicting objectives that need to be optimised simultaneously. The authors in [33] proposed the use of many objective evolutionary algorithms to address the problem of public transport network optimisation, focusing on metropolitan bus lines. The proposal consists of optimising the position of bus stops and consequently obtaining new routes that pass through these stops to minimise the average travel time, the time spent between the origin and destination, and the variance of distance between the stops. Integrating emerging shared mobility with traditional fixed-line public transport is a promising solution to the mismatch between supply and demand in urban transportation systems. In [34], the authors studied the problem of jointly optimising the timetable and the vehicle schedule within an intermodal urban transit network utilising modular vehicles (in short MVs) within the context of distributionally robust optimisation (in short DRO), which allows MVs to dynamically (de)couple at each stop and permits flexible circulations of modular units (in short MUs) across different transportation modes. The authors in [35] deal with the issue of the organisational structure of integrated transport systems. It addresses the issue of the responsibility, powers, and competences of the so-called participants in integrated transport systems. Finally, it also covers an important area, which is the transport service control system. Other research focusing on integrated transport systems deals with the methods of combining edges into transport lines within the framework of line formation for integrated timetables [36], the development stages of the creation of integrated transport systems [37], and information tools for integrated public passenger transport [38].