Demand responsive transit (DRT) echoes the new requirements of modern travel on flexibility and carbon reduction, as well as achieving a better match between demand and supply. However, many DRTs still failed. An important step named adaptability analysis helps to understand the context, desirability, and feasibility of introducing DRT. An adaptability analysis includes three sub-questions. Question 1 focuses on policy, regulation, funding, and technologies. Question 2 looks at the interactions of travel demand with operation parameters such as fare and fleet size. Question 3 tries to figure out the impacts of DRT on mobility, society, and the environment. To answer Question 1, macro-level methods collect information and generalise from empirical knowledge, including experience and barriers from real-world operation cases. To answer Question 2, meso-level methods determine the operation mode of DRT by quantifying related factors and establishing evaluation models or boundary condition decision models. To answer Question 3, micro-level methods use microscopic models for simulating the interaction between passengers and vehicles under different scenarios. This paper further discusses the advantages, disadvantages, and future directions of adaptability analysis methods of DRT. Overall, DRT presents great potential and future adaptability analysis should be developed by considering new trends in DRT and more complex and practical-oriented scenarios.

The problem of train trajectory optimization of two successive trains on the same line is studied in this paper. The two-train optimal control problem is formulated by using the multiple-phase method of optimal control, in which the train schedules as well as train performance parameters and track gradients, curves and speed limits are taken into account. The objective is to ensure a smooth, safe, punctual, and energy-efficient train operation. A green-wave (GW) policy is adopted to ensure that the trains proceed safely under all green signals. Special objective functions and constraints are developed in case of train delays. The method is applied in a case study of two successive trains running on a railway line with various input delays showing the benefit of the GW policy and the ability of delay recovery.

Employing regenerative braking in trains contributes to reducing the amount of energy used, especially when applied to commuter trains and to those used on very dense suburban networks. This paper presents a method to fine-tune the periodic timetable to improve the utilization of regenerative energy and to shave power peaks while maintaining the structure and robustness of the original timetable. First, a mixed-integer linear programming model based on the periodic event scheduling framework is proposed. A set of feasible timetables is determined and optimized with the aim of increasing synchronized acceleration and braking events at the same station, and maintaining the timetable robustness at the specified level. Next, a local search algorithm is developed to optimize the timetable such that the power peak value is minimized. The max-plus system model is adopted to estimate the delay propagation. Monte Carlo simulation is used to evaluate the utilization of regenerative energy and power peaks in random delayed circumstances. The proposed method was adopted to fine-tune the 2019 timetable for a sub-network of the Dutch railway. In the case of on- time scenarios, the optimized timetable increases the regenerative energy usage by almost 290% and decreases the 15-minute power peaks by 8.5%. In the case of delay scenarios, the optimized timetable outperforms the original timetable in terms of using regenerative energy and shaving power peaks.