## 1 Introduction
### Literature Review
#### 1.1.3 Train Timetabling with Time-Dependent Passenger Demand  
The above integrated studies are often based on historical demand data and ignore the time-dependent nature of passenger travel demand. Recently, more and more researchers have focused on the optimization of demand-oriented train timetables to systematically provide satisfactory services to meet time-dependent passenger flow demand. [PERSON] and [PERSON] [23] introduced the latest arrival time of the boarding passengers to obtain a time-varying waiting time by calculating the effective loading time period. A nonlinear programming model was established to solve the timetabling problem, intending to reduce the passenger waiting time in an oversaturated urban rail corridor. Then, [PERSON], et al. [24] took train skip-stop patterns into account in the timetabling problem under time-varying passenger demand. Oriented to the dynamic travel demand, [PERSON], et al. [25] formulated three train timetabling models and took the minimum average passenger waiting time as the objective. A fast adaptive large neighborhood search was proposed for real-scale case application with faster solution efficiency in the study [26]. [PERSON], et al. [27] focused on the timetabling problem of two interconnected high-speed railway lines. A nonlinear programming model was developed based on time-dependent origin-destination passenger demand. Considering the train speed profiles of different train driving strategies, [PERSON], et al. [28] established an integrated model for the train timetabling problem to reduce energy consumption and the waiting time of passengers. A Lagrangian relaxation-based heuristic algorithm was implemented to find a good plan within a short computational time. [PERSON], et al. [29] formulated a bi-level programming model to deal with the train timetabling problem for an urban rail line. Authors introduced a two-phase genetic algorithm combining the successive average approach to solve the model. For the high-speed railway system, [PERSON] and [PERSON] [30] described the relationship between train service times and passenger behavioral responses, and proposed an integrated optimization model, taking passenger demand, train service and resource utilization into account. [PERSON], et al. [31] built a combination model to optimize both train stop plan and timetable with dynamic travel demand. Then, an extended adaptive large-scale neighborhood search algorithm was designed to find the optimal solution. For the combined optimization of train stop plan and timetable, [PERSON], et al. [32] and [PERSON] et al. [33] proposed an integrated optimization strategy, considering the passenger expected departure intervals between their origins to destinations. [PERSON], et al. [34] integrated the train route selection based on the optimized train timetable under dynamic passenger demand, and adopted a modified non-dominated sorted genetic algorithm-II to address the bi-objective optimization problem.  
Recent studies on stop planning and timetabling are shown in Table 1. According to the above literature review, little research has considered demand-responsive transportation in high-speed railways and completed the integrated optimization study of the train stop plan and train timetable with more realistic conditions. Few studies have considered minimum remaining passenger demand as one of the objectives. Previous studies have placed some limitations on this problem, with methods such as predefined timetables. Some actual operation factors are also ignored in the previous studies, such as the additional acceleration time and deceleration time, different headway types and overtaking modes between trains. To offer a high-efficiency service for passengers and operators, this paper proposes an integrated optimization approach of train stop planning and timetabling, considering passenger demand with expected departure time. The application of demand-response transport on high-speed railways with more practical constraints is also trialed in this study.