## 2 Literature Review
### Train Timetabling Problem
This method is not suitable for large-scale railway networks. They proved the effectiveness of the algorithm through some small-scale examples. [PERSON] y et al. [37] used the heuristic method of optimization to generate the initial solution and then iterated. They used two systems, one to determine the sequence of trains passing through the station and the other to calculate the timetable. They verified the effectiveness of the understanding on two small-scale road networks. [PERSON] et al. [38] developed a model based on Sweden's new timetable scheduling process to meet the transportation demand when the infrastructure capacity changes. [PERSON] et al. [39] established a multi-objective optimization model. In this model, they set a series of objectives including the average train running time, energy consumption and passenger satisfaction. To solve the problem, they designed an improved genetic algorithm. [PERSON] et al. [40] analyzed the train operation plan optimization problem of an urban rail corridor. The railway system is divided into two parts: platform and vehicle. They aim to minimize the number of train departures, so as to meet the needs of passengers while driving as few trains as possible. [PERSON] et al. [41] proposed a space-time network that is based on three dimensions. Then they calculated the train timetable by a train timetable compilation method. [PERSON] et al. [42] established a DEA model based on train routes, so as to provide a basis for train route selection. [PERSON] et al. [43] studied aperiodic TTP. They found that linear programming could not describe the overtaking relationship of trains. Therefore, the problem was decomposed into several subproblems by the connected configurations method, and Lagrange relaxation was used to solve these subproblems. [PERSON] et al. [44] proposed a column generation algorithm with an acceleration scheme to solve the train timetable. [PERSON] et al. [45] proposed a space-time decompose approach to analyze the passenger travel demands. They established an integer programming model, which divides the timetable into three periods and solves the corresponding timetable in parallel. In terms of periodic and aperiodic mixing TTP, [28; 46; 47] studied the problem from the perspective of maximizing the profit of the operating company and the perspective of passengers' satisfaction. A periodic timetable is easier to solve, but in periodic timetables, some trains with excessive stops are repeated to meet the travel demands of passengers. This kind of train will lead to low passenger satisfaction. Due to the difficulty of solving aperiodic timetables directly, the time consumption will be beyond acceptance. Although the solving speed of periodic and aperiodic mixing timetable is faster, it is still a timetabling priority method, which may not be optimal for the train-set circulation plan. Moreover, there is some research on timetable rescheduling under disruptions; [48; 49; 50; 51] studied the timetable rescheduling problem with different strategies. They adjust the train stop plan and operating order to erase the negative effect as soon as possible. Most of the existing research deals with traditional TRP and TTP. Those studies are suitable for traditional operation scenarios, but transition time is a distinct scenario. Moreover, some research connected with TTP, on rolling stock rescheduling, is for the disturbed scenarios. The goal of these scenarios is to resume normal operation as soon as possible. Although there are some studies on transition time, the problem of wasting resources when timetable adjusting frequency increases still cannot be solved because much of the research is based on the current timetable. Hence, we propose a specific approach to TRP and TTP for transition time, in which the train-set circulation plan has a priority, to avoid the problems mentioned above.