Train Timetable Rescheduling Research Articles

State-Space Modeling and Feedback Control for Real-Time Automatic Train Timetable Rescheduling of Intercity HSRs

State-Space Modeling and Feedback Control for Real-Time Automatic Train Timetable Rescheduling of Intercity HSRs

Handling uncertainty in train timetable rescheduling: A review of the literature and future research directions

External and internal factors can cause disturbances or disruptions in daily train operations, leading to deviations from official timetables and passenger delays. As a result, efficient train timetable rescheduling (TTR) methods are necessary to restore disrupted train services. Although TTR has been a popular research topic in recent years, the uncertain characteristics of railways have not been sufficiently addressed. This review first identifies the primary uncertainties of TTR and examines their impacts on both TTR and passenger routing during disturbances or disruptions. It finds that only a few uncertainties have been investigated, and the existing solution methods do not adequately meet practical requirements, such as considering the dynamic nature of disturbances or disruptions, which is crucial for real-world applications. Therefore, the review highlights problems associated with TTR uncertainties that need urgent attention and suggests promising methodologies that could effectively address these issues as future research directions. This review aims to help practitioners develop improved automatic train-dispatching systems with better train-rescheduling performance under disturbances or disruptions compared to current systems.

Reinforcement Learning for Online Dispatching Policy in Real-Time Train Timetable Rescheduling

Reinforcement Learning for Online Dispatching Policy in Real-Time Train Timetable Rescheduling

A train timetable rescheduling approach based on multi-train tracking optimization of high-speed railways

PurposeThis paper aims to propose a train timetable rescheduling (TTR) approach from the perspective of multi-train tracking optimization based on the mutual spatiotemporal information in the high-speed railway signaling system.Design/methodology/approachFirstly, a single-train trajectory optimization (STTO) model is constructed based on train dynamics and operating conditions. The train kinematics parameters, including acceleration, speed and time at each position, are calculated to predict the arrival times in the train timetable. A STTO algorithm is developed to optimize a single-train time-efficient driving strategy. Then, a TTR approach based on multi-train tracking optimization (TTR-MTTO) is proposed with mutual information. The constraints of temporary speed restriction (TSR) and end of authority are decoupled to calculate the tracking trajectory of the backward tracking train. The multi-train trajectories at each position are optimized to generate a time-efficient train timetable.FindingsThe numerical experiment is performed on the Beijing-Tianjin high-speed railway line and CR400AF. The STTO algorithm predicts the train’s planned arrival time to calculate the total train delay (TTD). As for the TSR scenario, the proposed TTR-MTTO can reduce TTD by 60.60% compared with the traditional TTR approach with dispatchers’ experience. Moreover, TTR-MTTO can optimize a time-efficient train timetable to help dispatchers reschedule trains more reasonably.Originality/valueWith the cooperative relationship and mutual information between train rescheduling and control, the proposed TTR-MTTO approach can automatically generate a time-efficient train timetable to reduce the total train delay and the work intensity of dispatchers.

Deep Q-Network Approach for Train Timetable Rescheduling Based on Alternative Graph

The disturbance of local areas with complex railway networks and high traffic density not only impedes the efficient use of rail networks in those areas, but also propagates delays to the entire railway network. This has motivated research on train rescheduling problems in high-density local areas to minimize train delays by modifying their planned arrival and departure times. In this paper, we present a train rescheduling method based on Q-learning in reinforcement learning. More specifically, we used deep neural networks to approximate the action-value function, and the underlying Markov decision process (MDP) is based on the alternative graph formulation for the train rescheduling problem. In the proposed MDP formulation, the status of the alternative graph corresponding to the current schedule is defined as the state, and the alternative arc corresponds to the action the agent can take. The MDP is approximately solved via deep Q-learning in which deep neural networks are used to approximate the action-value function in Q-learning. Although the size of the alternative graph depends on the number of trains, our MDP formulation is independent of the number of trains, which makes the proposed method more scalable. The evaluation of the method was performed on a simple railway network and a real-world example in Seoul, South Korea, with randomly generated initial train schedules and train delays. The experimental result showed that the proposed method is comparable to the mixed-integer-linear-programming (MILP)-based exact approach with respect to the quality of the solution.

A Multistage Decision Optimization Approach for Train Timetable Rescheduling Under Uncertain Disruptions in a High-Speed Railway Network

Railway operations are vulnerable to unexpected disruptions that should be handled in an efficient and passenger-friendly way. This paper focuses on a real-time train timetable rescheduling problem in a railway system with a seat-reserved mechanism during disruptions. An integer linear programming model with a compensation mechanism is established to provide a better overall solution for dynamic and multistage rescheduling problems. The main part of the model considers the strategies of retiming, adding train stops and reordering to reduce the impact of passengers, while the compensation mechanism part mainly deals with the uncertainty of disruption durations. The related constraints mainly include section track capacity, station track capacity,passenger transfer rules and some robust measures under the uncertainty of disruption durations. A multistage decision optimization framework is established, and a rolling horizon algorithm is embedded in it to approximate the desired optimal solution. A series of numerical experiments based on a real case of China’s high-speed railway subnetwork are carried out to verify the effectiveness and efficiency of the proposed optimization approach.

Cooperative train control during the power supply shortage in metro system: A multi-agent reinforcement learning approach

In metro system, the fault of traction power supply system may cause the power supply shortage around the failure substation. In this case, the dispatching measure should be immediately taken to reduce the impacts of disruption on the train operation. To deal with this real-time traffic management problem, a cooperative control approach is proposed in this paper. In this approach, the time to apply tractive force and the level of force are simultaneously adjusted for all the operated trains, to maximize the maintained line capacity when considering the power supply capacity. Compared with the existing train timetable rescheduling approach, cooperative control is more flexible to get a better train regulation solution. To solve the challenges for developing the cooperative control model (i.e., undetermined number and dynamically changing of controlled objects), an imaginary section method is newly developed to transform the original problem into an equivalent cooperative control problem with fixed controlled objects. Then, the mathematical models for the transformed problem are constructed by using the space–time–speed network methodology. According to the formulated model, a Decentralized-Markov Decision Process (Dec-MDP) framework is designed as the basis of the applied algorithm. Next, a Collaboration Mechanism Based-Independent Deep Q-Network (CMB-IDQN) algorithm is proposed to solve the cooperative control problem. Compared with classical IDQN algorithm, a credit assignment method based on the collaboration mechanism among trains is novelly considered in the designed multi-agent reinforcement learning algorithm. Finally, the effectiveness of the proposed cooperative control approach is verified by two case studies. When solving the cooperative control problem, the performance by using CMB-IDQN algorithm can be increased by up to 13.0% and 16.8% compared with other two classical reinforcement learning algorithms (i.e., DQN and IDQN), respectively. Compared with two train timetable rescheduling measures during the power supply shortage, the cooperative control approach can improve the solution quality by more than 180.4% and 17.4%, respectively.

Earthquake resilience assessment and improving method of high-speed railway based on train timetable rescheduling

Owning the characteristics of fast transportation speed and large transportation volume, high-speed railway (HSR) transportation is one of the most critical ways for passenger and freight transportation between cities. On the HSR, trains operate according to the planned timetable normally. However, error and conflict will occur when facing unexpected disruptions for the inability of timely and correct rearrangement by human beings also due to those characteristics. Most literature contributions fail in considering the change in traffic demand after the disruption caused by specific disasters. To this end, a mixed integer linear programming (MILP) model considering the main track outage, track recovery, station recovery, and dynamic traffic demand to enhance seismic resilience is proposed to solve the timetable rescheduling problem (TRP) after earthquakes automatically and in timely. Moreover, the use of the original timetable as a guidance solution reduces the computation time significantly. The proposed method is performed on a realistic simulation of the Dalian-Shenyang section of the Harbin-Dalian HSR of the China HSR network against an earthquake. The outcomes provide the optimal timetable from the recovery period to resume to the original timetable that leads to the best resilience. A computational test of the proposed model on a heavily used part of the China HSR reveals that the method can find optimal solutions with the highest resilience in short computation times.

A Memetic Algorithm for High-Speed Railway Train Timetable Rescheduling

This study addresses a high-speed railway train timetable rescheduling (TTR) problem with a complete blockage at the station and train operation constraints. The problem is formulated as a mixed-integer linear programming (MILP) model that minimizes the weighted sum of the total delay time of trains. A memetic algorithm (MA) is proposed, and the individual of MA is represented as a permutation of trains’ departure order at the disrupted station. The individual is decoded to a feasible schedule of the trains using a rule-based method to allocate the running time in sections and dwell time at stations. Consequently, the original problem is reformulated as an unconstrained problem. Several permutation-based operators are involved, including crossover, mutation, and local search. A restart strategy was employed to maintain the the population diversity. The proposed MA was compared with the first-scheduled-first-served (FSFS) algorithm and other state-of-the-art evolutionary algorithms. The experimental results demonstrate the superiority of MA in solving the TTR through permutation-based optimization in terms of constraint handling, solution quality, and computation time.

Improving Synchronization in High-Speed Railway and Air Intermodality: Integrated Train Timetable Rescheduling and Passenger Flow Forecasting

With the aim of synchronizing high-speed railway (HSR) and aviation services to adapt intermodality traffic needs, this paper is concerned with HSR-Air timetable coordination (HATC) problem. This problem is solved by rescheduling the HSR timetable with the goal of attracting the maximum HSR-Air passenger flow, and at the same time, considering the minimum adjustments to the initial HSR timetable. The HATC problem is an integration of train timetable rescheduling and HSR-air passenger flow predicting problem. There is a trade-off increasing the HSR-Air passenger flow and the adjustments to the initial train timetable. In order to capture the complex feature interactions of passenger flow impact features, a novel HSR-Air passenger flow prediction model is proposed by using factorization machine and deep neural networks in this paper. Moreover, this passenger flow prediction model then is integrated into the train timetable rescheduling model to calculate the passenger flow under different HSR-Air service network. An approach based on a genetic algorithm is developed to solve the integrated model of deep learning and integer programming. The model and approach are tested in a real-world HSR-Air case in China with 15 HSR stations, 200 trains and 82 flights. The results show that the proposed model can obtain the satisfactory predictions and effectively enhance the HSR-Air passenger flow within an acceptable level of deviations to initial train timetable.

A data-driven, variable-speed model for the train timetable rescheduling problem

Train timetable rescheduling — the practice of changing the routes and timings of trains in real-time to respond to delays — can help to reduce the impact of reactionary delay. There are a number of existing optimisation models that can be used to determine the best way to reschedule the timetable in any given traffic scenario. However, many of these models do not adequately account for the acceleration and deceleration required for trains to achieve the rescheduled timetable. The few models that do account for this are overly complex and cannot be solved to optimality in sufficiently short times. In this study, we propose a new model for train timetable rescheduling that uses statistical methods and historical data to parsimoniously take train speed into account. The model is tested using a new set of instances based on real data from Derby station in the UK. We show that the improved accuracy of the proposed model comes with little to no trade-off in terms of run time compared to fixed-speed timetable rescheduling models.

A Deep Reinforcement Learning Approach for the Energy-Aimed Train Timetable Rescheduling Problem Under Disturbances

At metro stations with very large passenger volumes and severe congestion, random disturbances often occur, resulting in the original offline optimal timetable no longer applicable. Deep reinforcement learning has the advantages of self-learning and online learning, making it possible to solve the energy-aimed train timetable rescheduling (ETTR) problem under random disturbances. In this article, a deep reinforcement learning approach (DRLA) has been proposed and applied to the ETTR problem to reschedule the train timetable in order to achieve the online optimal timetable for the minimum energy usage under disturbances. The proposed DRLA has the advantages of a real-time performance, high learning efficiency, and better energy-saving effect compared with the traditional heuristic algorithms such as genetic algorithm (GA), deep learning methods such as the combination of improved GA and long short-term memory (IGSA-LSTM), or other reinforcement learning algorithms such as deep deterministic policy gradient (DDPG) algorithm. Various experiments are conducted to compare the performance of DRLA and other algorithms. The experimental results indicate that in the two-train metro system and the five-train metro system, DRLA can save an average of energy by 5.11% and 7.29% with an average reaction time of only 0.0013 and 0.0019 s against disturbances, respectively.

An efficient method of disturbance analysis and train rescheduling using MWDLNN and BMW-SSO algorithms

The ability to efficiently reschedule the trains when disturbances occur is one among the vital factors in attaining higher punctuality on railway traffic systems. Numerous techniques were developed to carry out train rescheduling, but the existing methodologies don't consider the probability of conflicts (or) disturbance analysis (DA). Thus, in this paper, an efficient method of train rescheduling (TRS) system is proposed utilizing the Brownian motion weighted-based salp swarm optimization (BMW-SSO) algorithm. The proposed technique follows two processes: (i) DA and (ii) rescheduling. Primarily, the track detail is collected as an input, and the disturbances are identified utilizing the modified weight-based deep learning neural network. If any disturbance occurs, then the TRS process is performed utilizing the BMW-SSO. Aimed at this, the current timetable is utilized as an input, and some constraints are extracted from the input utilizing multi-choice mixed integer goal programming. After that, finally, the BMW-SSO chooses and optimizes the best constraints from the extracted constraints as well as reschedules the timetable. Here, the BMW-SSO alternatively chooses the best third rescheduling timetable. Subsequent to generating TRS, the first rescheduled timetable’s feasibility is checked centred on the dictionary-centred checking technique. If the first rescheduled timetable is feasible, then it is signified as the optimal timetable. Otherwise, the second-created rescheduled timetable is utilized. And if the second one is unfeasible, then the thirdly created timetable is utilized. At last, the experiential examination confirms the proposed train timetable rescheduling (TTR) system’s performance regarding the timetable deviation, operation cost, along with dwell time. The proposed TTR consumes low time while analogized with the scheduled table.

A Real-Time Train Timetable Rescheduling Method Based on Deep Learning for Metro Systems Energy Optimization under Random Disturbances

Considering that uncertain dwell disturbances often occur at metro stations, researchers have proposed many methods for solving the train timetable rescheduling (TTR) problem. This paper proposes a Modified Genetic Algorithm-Gate Recurrent Unit (MGA-GRU) method, which is a real-time TTR method based on deep learning. The proposed method takes the Gate Recurrent Unit (GRU) network as the decision network and uses the results produced by the Modified Genetic Algorithm (MGA) as the training set of the decision network. A well-trained decision network can provide effective solutions in real time after random disturbances occur, in order to optimize the net traction energy consumption of trains in metro systems. Based on the Shanghai Metro Line One (SML1) pilot network, this paper establishes a comprehensive model of the metro system as a training and testing environment to verify the energy-saving effect and real-time performance of the proposed method in solving the TTR problem. The experimental results show that in the two-train metro system, the three-train metro system, and the five-train metro system, the MGA-GRU method can save an average of energy by 4.45%, 6.16%, and 7.19%, while the average decision time is only 0.15 s, 0.27 s, and 0.33 s, respectively.

Deep learning based energy efficient optimal timetable rescheduling model for intelligent metro transportation systems

Due to the recent advances in intelligent transportation systems (ITS), Automatic Train System (ATS) gained significant attention among the research community. An effective ATS offers the whole railway network to operate in a safe, cost-effective and proficient manner against sudden disturbances like temporary platform blockages. Numerous Train Timetable Rescheduling (TTR) models have been presented for managing unforeseen events which might disturb the timetable. The main aim of an effective TTR model is to reduce power utilization by consuming the entire benefits of reproductive braking energy under a random situation. In this view, this paper presents a new TTR model to optimize the energy of metro systems by the incorporation of improved genetic algorithm (IGA) and long short term memory (LSTM) based recurrent neural network (RNN). The proposed method incorporates three different models, namely controller, timetable, and energy models. The proposed method requires minimum time to recompute a new schedule and offers effective solutions instantly after a random disturbance happens. The performance validation of the proposed IGSA-LSTM model is simulated using Chennai Metro Train Station. The proposed method significantly reduces the energy consumption of metro train and reaches to a minimum average energy utilization of 696 kWh.

Real-Time Passenger Flow Anomaly Detection Considering Typical Time Series Clustered Characteristics at Metro Stations

AbstractReal-time anomaly detection at metro stations is a very important task with considerable implications for massive passenger flow organization and train timetable rescheduling. State-of-the-...

A High-speed Train Timetable Rescheduling Approach in case of Disturbances

This paper from a macroscopic viewpoint develops a train timetable rescheduling approach on a single high-speed railway line under disturbances, i.e. inevitable train delays in the duration of the train operation. A mixed-integer linear programming model is formulated to minimize the arrival delay and the departure delay altogether. The commercial optimization software CPLEX is adopted in an effort to seek the optimal solution in an acceptably short time required in the real-time rescheduling process. The proposed approach is further tested on a real-world case study and the numerical results show that compared with the results obtain by the traditional genetic algorithm, using CPLEX to solve the model can yield better solutions and consume the desired computation time, thereby demonstrating its effectiveness and efficiency.

Application of a Deep Deterministic Policy Gradient Algorithm for Energy-Aimed Timetable Rescheduling Problem

Reinforcement learning has potential in the area of intelligent transportation due to its generality and real-time feature. The Q-learning algorithm, which is an early proposed algorithm, has its own merits to solve the train timetable rescheduling (TTR) problem. However, it has shortage in two aspects: Dimensional limits of action and a slow convergence rate. In this paper, a deep deterministic policy gradient (DDPG) algorithm is applied to solve the energy-aimed train timetable rescheduling (ETTR) problem. This algorithm belongs to reinforcement learning, which fulfills real-time requirements of the ETTR problem, and has adaptability on random disturbances. Superior to the Q-learning, DDPG has a continuous state space and action space. After enough training, the learning agent based on DDPG takes proper action by adjusting the cruising speed and the dwelling time continuously for each train in a metro network when random disturbances happen. Although training needs an iteration for thousands of episodes, the policy decision during each testing episode takes a very short time. Models for the metro network, based on a real case of the Shanghai Metro Line 1, are established as a training and testing environment. To validate the energy-saving effect and the real-time feature of the proposed algorithm, four experiments are designed and conducted. Compared with the no action strategy, results show that the proposed algorithm has real-time performance, and saves a significant percentage of energy under random disturbances.

Energy-Saving Metro Train Timetable Rescheduling Model Considering ATO Profiles and Dynamic Passenger Flow

For metro systems in over-crowded conditions, when an unexpected disturbance occurs, the operation of trains might be disturbed due to the high frequency and density of the metro traffic. A large number of passengers might be stranded on platforms due to service gaps and the limited free capacity of trains. In this paper, by introducing binary variables as selection indicators for ATO profiles which were preset in on-board ATO systems by metro signal suppliers, we develop a mixed integer programming (MIP) model for a metro train timetable rescheduling problem in order to jointly optimize the total train delay, the number of stranded passengers, and the energy consumption of trains. We formulate the total energy consumption as the difference between the tractive energy consumption and the regenerated energy by considering the mass of in-vehicle passengers. Then, we adopt commercial optimization software CPLEX to solve the proposed model, which can obtain tradeoff solutions in a short time. Finally, three numerical experiments based on real-world operational data are carried out to verify the effectiveness of the proposed method.

Path anticipation and prioritised conflict-free train rescheduling on a linear network

Rail schedule may get disturbed due to unforeseen set of events, which requires a quick response to plan a new feasible schedule under the given set of complicating constraints, and resolving the potential conflicts among trains. This gives rise to the classical train timetable rescheduling (TTR) problem, which is combinatorial in nature and computationally challenging. In this work, we present the macroscopic train orientation of TTR problem with explicit consideration of safety characteristics. In case of disturbances, path anticipation criteria are used to generate a feasible and conflict-free schedule. A novel algorithm based on inhibitor net to prioritising trains and conflict-resolution is presented. We demonstrate the application of a decision support system with the controller's intervention for the considered problem. Finally, the proposed solution approach is tested for its efficiency on several test instances generated for a real-life case of a single-track corridor in the Indian rail network.