Search Results (90)

Real-time train rescheduling is a widely used strategy to minimize knock-on delays in railway networks. While recent research has introduced intelligent solutions to railway traffic management, the tight interdependence of train timetables and the intrinsic complexity of railway networks have hindered the scalability of these approaches to large-scale systems. This paper proposes a multi-agent system (MAS) that addresses these challenges by decomposing the network into single-junction levels, significantly reducing the search space for real-time rescheduling. The MAS employs a Condorcet voting-based collaborative approach to ensure global feasibility and prevent overly localized optimization by individual junction agents. This decentralized approach enhances both the quality and scalability of train rescheduling solutions. We tested the MAS on a railway network in the UK and compared its performance with the First-Come-First-Served (FCFS) and Timetable Order Enforced (TTOE) routing methods. The computational results show that the MAS significantly outperforms FCFS and TTOE in the tested scenarios, yielding up to a 34.11% increase in network capacity as measured by the defined objective function, thus improving network line capacity. Full article

Cross-line operation and express–local train coordination are both crucial for enhancing the efficiency of multi-level urban rail transit systems. Most studies address suburban railway operations in isolation, overlooking coordination and inducing supply–demand mismatches that weaken system efficiency. This study addresses the joint optimization of cross-line operation and express–local scheduling by proposing a novel train timetable model. The model determines train service plans and departure times to minimize total system cost, including train operating and passenger travel costs. A space–time network represents integrated train–passenger interactions, and an extended adaptive large neighborhood search (E-ALNS) algorithm is developed to solve the model efficiently. Numerical experiments verify the effectiveness of the proposed approach. The E-ALNS achieves near-optimal solutions with less than 4% deviation from Gurobi. Comparative analysis shows that the proposed hybrid operation mode reduces total passenger travel cost by 6% and improves the cost efficiency ratio by 13% compared to independent operations. Sensitivity analyses further confirm the model’s robustness to variations in transfer walking time, passenger penalties, and waiting thresholds. This study provides a practical and scalable framework for optimizing train timetables in complex cross-line transit systems, offering insights for enhancing system coordination and passenger service quality. Full article

Periodic train timetables provide passengers with easily accessible rail transport services. However, in networked railway operations, some long-distance cross-line trains have high operational priority and pose difficulties for scheduling local services. In this paper, we address the minimal-cycle-length periodic train timetabling problem with high-priority cross-line operations and complex local train types. We propose a special set of constraints to accommodate the prespecified operational times of cross-line operations with regard to system robustness. As the cycle length is regarded as a decision variable, the formulation is nonlinear. To solve the problem, we exploit the connection between cycle length and consumed capacity of periodic timetables and propose high-level cycle-capacity and binary search-guided iterative solution frameworks, which implicitly enumerate the periodic train timetabling problems. Using the real-world operational data of the Guangzhou–Zhuhai Intercity Rail Line, we explore the solution performance of the proposed solution approaches and the straight linearization of the problem, and we also compare the practices of fixing prespecified operational times and our proposed constraints for the cross-line services. The results demonstrate that our proposed method can efficiently achieve flexible while recoverable operational times for the cross-line services and the proposed implicit enumeration algorithms significantly outperform the direct linearization, which increases the search space significantly due to the considerable dimensionality of the periodic decision variables involved. Numerical computations also suggest that our proposed constraints provide a type of approach for balancing the operational convenience and stability margins available in the periodic timetable with the presence of cross-line operations. Full article

For a given timetable in urban rail transit systems, this paper presents a practical energy efficiency optimization problem that carries out adjustments to the timetable, with the goal of energy saving. We propose two strategies to address this challenge, including adjusting the section running time by selecting a speed profile and improving the utilization of regenerative braking energy by adjusting the trains’ departure time. Constraints on the range of adjustment for energy-efficient time elements are constructed for maintaining the stability of elements of the given timetable. An energy efficiency optimization model is then established to minimize the total net energy consumption of the timetable, and a solution algorithm based on a genetic algorithm is proposed. We make small-scale adjustments to trains’ running trajectories to optimize the overlap time of braking and traction conditions among multiple trains. The case of the Guangzhou Metro Line 8 in China is presented to verify the effectiveness and practicality of our method. The results show that the consumption of traction energy is reduced by 0.95% and the use of regenerative braking energy is increased by 8.18%, with an improvement in energy efficiency of 6.78%. This method can achieve relatively significant energy efficiency results while ensuring the stable service quality of the train timetable and can provide support for an energy-efficient train timetable for urban rail transit operation enterprises. Full article

Predicting passenger flows in rail transport systems plays an important role for traffic management centers to make fast decisions during service disruptions. This paper presents an innovative cross-disciplinary approach based on digital twins, deep learning, and traffic simulation to predict the total number of passengers in each train stations and evaluate the impact of service disruptions across stations. First, we present a four-layer system architecture for building a digital twin which ingests real-time data streams, including train movements and timetable scheduling. Second, we deploy several deep learning models to predict the total number of passengers in each station using mobile data. The results showcase significant accuracy for recurrent versus non-recurrent traffic conditions even under severe large disruptions such as the COVID-19 travel restrictions. Our case study of the Sydney rail network demonstrates that the proposed digital twin powered by deep learning can provide more granular real-time insights into the impact on passengers, allowing rail operation centers to better mitigate service disruptions. Full article

The current technical condition of facilities designated for the technical–hygienic maintenance of railway rolling stock is unsatisfactory, as they are neither technologically nor technically equipped to meet the required quality standards. Maintenance is often carried out in open spaces or directly on the tracks of major railway junctions, which prevents year-round execution of these services and causes operational limitations. This article analyses and proposes solutions for the technical–hygienic maintenance center (THU) of railway rolling stock at the Nové Zámky railway station in Slovakia, focusing on improving the efficiency and quality of the provided services. The analysis includes an assessment of technological procedures, identification of operational deficiencies, and a comparison of current maintenance standards with the requirements for contemporary railway systems, such as automated diagnostic platforms, predictive maintenance modules, and modular cleaning infrastructure. The optimization of THU services considers the average time norms for selected technological procedures and the characteristics of train sets passing through the center. The proposed solution involves a more efficient scheduling of operations in line with the valid railway traffic timetable and train set circulation, utilizing a graphical planning method for modelling and optimizing the facility’s service processes. The implementation of optimization measures can lead to increased capacity and efficiency of maintenance, reduced time required for individual procedures, and lower operational costs. The study’s results provide practical recommendations for improving the quality of technical–hygienic maintenance at railway junction stations, contributing to greater railway transport reliability and an overall improvement in passenger comfort. Additionally, the findings offer a transferable framework that may inform the planning and modernization of maintenance facilities at other regional railway stations facing similar infrastructural and operational challenges. Full article

Simulation is a vital tool for understanding rail traction energy consumption. Simulating such energy consumption requires an understanding of the interactions between timetable, infrastructure, and driver behavior to be encapsulated within a multi-train system model. This is critical to simulating systemic interactions that affect energy consumption on a rail network. However, building and executing such a system simulation is challenging because of diverse models, stakeholders, and knowledge, as well as a lack of tools to support flexible and scalable simulation. This paper presents a demonstration of co-simulation—an approach originating in the automotive industry and now being used in other sectors—that enables a system model to be assessed for different configurations of timetable, rolling stock, infrastructure, and driver behavior. This paper describes the co-simulation approach before outlining the development process that allowed three research institutes, each with diverse models, to collaborate and deliver an integrated, holistic modeling approach. The results of this work are presented and discussed, both in terms of the quantified outputs and findings for energy consumption, and the lessons learned through collaborative co-simulation. Future avenues to build on this work are identified. Full article

To enhance the accessibility and efficiency of airports, the concept of airport connectivity is extended to High-Speed Rail (HSR), as major hub airports now have direct access to an HSR station. The traditional hub connectivity index is supplemented by the number and quality of connections between train and flight departures/arrivals (or timetables). The methodology is tested at the Paris-Charles de Gaulle airport. The results highlight that air–rail and rail–air connections can represent up to 72% of the total hub connectivity. A disaggregated analysis of connectivity across origin–destination pairs was conducted, revealing potential synchronization gaps. These findings demonstrate that this tool can assist transportation service providers in synchronizing their timetables, by measuring the degree to which it contributes to improve connectivity. Moreover, the findings offer new insights into air–rail timetable coordination and provide policy implications regarding the replacement of feeder flights by HSR. Full article

High-speed railways (HSRs), with their advantages of safety, energy conservation, and convenience, are increasingly becoming the preferred mode of transportation. Railway operators schedule full-schedule timetables to operate as many trains and serve as many passengers as possible. However, due to the fluctuation in passenger demands, it is not necessary to operate all trains in full-schedule timetable, which results in high operation costs and too much energy consumption. Based on this, we propose an integrated approach to schedule passenger train plans and train timetables by selecting trains to operate from the full-schedule timetable, adjusting their stopping scheme and operation sequence to reduce operation costs and energy consumption and contribute to sustainable development. In the scheduling process, both operation costs and passenger service quality are considered, and a two-objective model is established. An algorithm is designed based on Non-dominated Sorting Genetic Algorithms-II (NSGA-II) to solve the model, containing techniques for acceleration that utilize overtaking patterns, in which overtaking chromosomes are used to illustrate the train operation sequence, and parallel computing, in which the decoding process is computed in parallel. A set of Pareto fronts are obtained to offer a diverse set of results with different operation costs and passenger service quality. The model and algorithm are verified by cases based on the Beijing–Shanghai HSR line. The results indicate that compared to the full-schedule timetable, the operation costs under three sets of passenger demands decreased by 35.4%, 27.7%, and 15.7% on average. Compared to the genetic algorithm with weighting multiple objectives and NSGA-II without acceleration techniques, the algorithm proposed in this paper with the two acceleration techniques of utilizing overtaking patterns and parallel computing can significantly accelerate the solution process, with an average reduction of 42.9% and 38.3% in calculation time, indicating that the approach can handle the integrated scheduling problem economically and efficiently. Full article

This study discusses the issue of train rescheduling in high-speed railways (HSR) when unexpected interruptions occur. These interruptions can lead to delays, cancellations, and disruptions to passenger travel. An optimization model for train rescheduling under uncertain-duration interruptions is proposed. The model aims to minimize both the decline in passenger service quality and the total operating cost, thereby achieving sustainable rescheduling. Then, a hybrid optimization algorithm combining rolling horizon optimization with a deep-learning-embedded NSGA-II algorithm is introduced to solve this multi-objective problem. This hybrid algorithm combines the advantages of each single algorithm, significantly improving computational efficiency and solution quality, particularly in large-scale scenarios. Furthermore, a case study on the Beijing–Shanghai high-speed railway shows the effectiveness of the model and algorithm. The optimization rates are 16.27% for service quality and 15.58% for operational costs in the small-scale experiment. Compared to other single algorithms or algorithm combinations, the hybrid algorithm enhances computational efficiency by 26.21%, 15.73%, and 25.13%. Comparative analysis shows that the hybrid algorithm outperforms traditional methods in both optimization quality and computational efficiency, contributing to enhanced overall operational efficiency of the railway system and optimized resource utilization. The Pareto front analysis provides decision makers with a range of scheduling alternatives, offering flexibility in balancing service quality and cost. In conclusion, the proposed approach is highly applicable in real-world railway operations, especially under complex and uncertain conditions, as it not only reduces operational costs but also aligns railway operations with broader sustainability goals. Full article

It is particularly important to develop efficient air–rail intermodal path planning methods for making full use of the advantages of air–rail intermodal networks and providing passengers with richer and more reasonable travel options. A Time-Expanded Graph (TEG) is used to model the timetable information of public transportation providing a theoretical basis for public transportation path planning. However, if the TEG includes a large amount of data such as train stations, airports, train and air schedules, the network scale will become very large, making path planning extremely time-consuming. This study proposes an XGBoost-based heuristic path planning algorithm (XGB-HPPA) for large scale air–rail intermodal networks, which use the XGBoost model to predict transfer stations before path planning, and quickly eliminate unreasonable transfer edges by adding a heuristic factor, reducing the network scale, thus accelerating the computation speed. Comparative results indicate that XGB-HPPA can markedly enhance computational speed within large-scale networks, while obtaining as many valid solutions as possible and approximating the optimal solution. Full article

The rapid growth of cities and their populations in recent years has resulted in significant tidal passenger flow characteristics, primarily manifested in the imbalance of passenger numbers in both directions. This imbalance often leads to a shortage of train capacity in one direction and an inefficient use of capacity in the other. To accommodate the tidal passenger flow demand of urban rail transit, this paper proposes a timetable optimization method that combines multiple strategies, aimed at reducing operating costs and enhancing the quality of passenger service. The multi-strategy optimization method primarily involves two key strategies: the unpaired operation strategy and the express/local train operation strategy, both of which can flexibly adapt to time-varying passenger demand. Based on the decision variables of headway, running time between stations, and dwell time, a mixed integer linear programming model (MILP) is established. Taking the Shanghai Suburban Railway airport link line as an example, simulations under different passenger demands are realized to illustrate the effectiveness and correctness of the proposed multi-strategy method and model. The results demonstrate that the multi-strategy optimization method achieves a 38.59% reduction in total costs for both the operator and the passengers, and effectively alleviates train congestion. Full article

In recent years, passenger holiday travel momentum continues to increase, which proposes a challenge to the refined transportation organization of China’s high-speed railway. In order to save the cost of transportation organization, this paper proposes a collaborative optimization method using a high-speed railway train diagram and Electric Multiple Unit (EMU) routing considering the coordination of weekdays and holidays. Based on the characteristics of the train diagram and EMU routing, this method optimizes the EMU routing synchronously when compiling the train diagram. By constructing a space–time–state network, considering the constraints of train headway, operation conflict, and EMU maintenance, a collaborative optimization model of the train diagram and EMU routing considering the coordination of weekdays and holidays is established. This research combines the actual operation data to verify the model and algorithm. Based on five consecutive days of holidays, a seven-day transportation plan covering before and after the holidays and during the holidays is designed, and a case study is carried out. The results show that the proposed collaborative optimization theory has practical significance in the application scenarios of high-speed railway holidays. Full article

Line planning and timetabling play important roles in the design of urban rail transportation services. Due to the complexity of the integrated optimization of entire transportation plans, previous studies have generally considered line planning and timetabling design independently, which cannot ensure the global optimality of transportation services. In this study, the integrated design problem of line planning and timetabling was characterized as an equilibrium space–time network design problem and solved with a bi-objective nonlinear integer programming model. The model, in which train overtaking and passenger path choice behavior were considered, adjusted the network topology and link attributes (time and capacity) of the travel space–time network by optimizing the train service frequency, operation zone, stopping pattern, train formation, and train order to minimize the system life cycle cost and total passenger travel time perception. An algorithm was constructed using the non-dominated sorting genetic algorithm II combined with the self-adaptive gradient projection algorithm to solve the model. A real-world case was considered to evaluate the effectiveness of the proposed model and algorithm. The results showed that the model not only performed well in the trade-off between system cost and passenger travel efficiency, but it could also reduce the imbalance of train and station loads. Pareto front analysis of the model with different parameters showed that more types of trains did not correlate with a better performance, some line-planning strategies had a combination effect, and multi-strategy line planning was more suitable for scenarios with a high imbalance in the temporal and spatial distributions of passenger flow. Full article

This paper presents a solution to address the challenges of unexpected events in the operation of metro trains, which can lead to increased delays and safety risks. An improved Q-learning algorithm is proposed to reschedule train timetables via incorporating train detention and different section running times as actions. To enhance computational efficiency and convergence rate, a simulated annealing dynamic factor is introduced to improve action selection strategies. Additionally, importance sampling is employed to evaluate different policies effectively. A case study of Shenzhen Metro is conducted to demonstrate the effectiveness of the proposed method. The results show that the method achieves convergence, fast computation speed, and real-time adjustment capabilities. Compared to traditional methods such as no adjustment, manual adjustment, and FIFO (First-In-First-Out), the proposed method significantly reduces the average total train delay by 54% and leads to more uniform train headways. The proposed method utilizes a limited number of variables for practical state descriptions, making it well suited for real-world applications. It also exhibits good scalability and transferability to other metro systems. Full article