Train operation adjustment based on conflict resolution for high-speed rail

The forecast of delay time is of great assistance to decision making in train operation adjustment when the schedule is disturbed either by infrastructure fault or natural hazard. This paper presents a railway delay propagation model to forecast the delay time, described by discrete event dynamic system (DEDS) and formulated by max-plus algebra theory. On the basis of the train operation regulations and headway constraints, a system matrix of max-plus algebra is acquired to illustrate the mechanism of delay propagation. And then a function to predict the delay time is proposed to solve the model, with two advantages: Firstly, the specific delay time is able to be calculated; secondly, the result of the prediction is comparatively precise due to the highly match of the model to the actual operation. Finally, by analysis of the prediction, this paper offers the decision support in train adjustment, from which the dispatcher can proactively conduct countermeasures to alleviate the propagation and even stop it.

The running time of high-speed train is generally not late, and it can run normally in most cases. However, when severe weather conditions, train components and equipment, accidents or emergencies occur, it will lead to train operation delay and traffic congestion. Therefore, when an accident occurs, we need to adjust the train time or route timely and accurately. As an important algebraic system, max-plus algebra is widely used in the field of industrial production control. In industrial production, the most production mode is the discrete system , but the characteristics and the ability of discrete systems depends on the periodic of system and the number of workpiece produced by the system in unit time, and the characteristics of the system are closely related to the properties of the matrix, especially, the eigenvalues and eigenvectors of the matrix in the sense of max-plus algebra. Therefore, this paper studies the max-plus algebra theory and the solution of eigenvalues and eigenvectors of matrices in the sense of max-plus algebra, establish the operation time matrix to optimize the train operation adjustment model of high-speed railway, and analyze the failure propagation model.

Train delay is a serious issue that can spread rapidly in the railway network leading to further delay of other trains and detention of passengers in stations. However, the current practice in the event of the trail delay usually depends on train dispatcher’s experience, which cannot manage train operation effectively and may have safety risks. The application of intelligent railway monitor and control system can improve train operation management while increasing railway safety. This paper presents a methodology in which train timetabling, platforming and routing models are combined by studying the real-time adjustment and optimization of high-speed railway in the case of the train delay in order to produce a cooperative adjustment algorithm so that the train operation adjustment plan can be obtained. MATLAB computer programs have been developed based on the proposed methodology and adjustment criteria have been established from knowledge data bases in order to calculate optimized solutions. A case study is used to demonstrate the proposed methodology. The results show that the proposed method can quickly adjust the train operation plan in the case of the train delay, restore the normal train operation order, and reduce the impact of train delay on railway network effectively and efficiently.

For the low accuracy and slow convergence speed of artificial bee colony(ABC) algorithm in solving complex optimization problems, an improved artificial bee colony(ABC) algorithm based on the new full dimensional updating ABC/best /1 strategy, namely FNABC was proposed in this paper. In the FNABC, for the low efficiency of one-dimensional search, the full dimensional updating search strategy and ABC/best /1 strategy were combined to design a new full dimensional updating ABC/best/1 strategy, which expanded the search space, improved the mining ability and search efficiency. Then, a new evolutionary phase is designed to balance the global search ability and local mining ability to avoid falling into local optimum and improve the convergence accuracy. Finally, the FNABC is compared with eight state-of-the-art ABC variants such as AABC, iqABC, MEABC, ABCVSS, GBABC, DFSABC, MABC-NS, MGABC in solving 12 complex functions. All functions have obtained the best optimal values among 9 algorithms. Additionally, FNABC is applied to solve a real-world train operation adjustment problem. The experiment results indicate that the FNABC has better optimization ability, scalability and robustness. It obtains the ideal train operation adjustment results.

This paper explores methods to include aspects of controller taskload into conflict-resolution programs through a parametric approach. We are motivated by the desire to create conflict-resolution decision-support tools that operate within a human-in-the-loop control architecture by actively accounting for, and moderating, controller taskload. Specifically, we introduce two conflict-resolution programs with the objective of managing controller conflict-resolution taskload, i.e., the number of maneuvers used to separate air traffic. Managing conflict-resolution taskload is accomplished by penalizing aircraft maneuvers through their <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm in the cost function or constraining the number of maneuvers directly. Analysis of the programs reveals that both approaches are successful at managing controller conflict-resolution taskload and minimizing fuel burn. Directly constraining conflict-resolution taskload is more successful at minimizing the variation in the number of aircraft maneuvers issued and returning the aircraft to their desired exit point. Penalizing maneuvers through <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm costs is more successful at reducing controller conflict-resolution taskload at lower traffic volumes. Ultimately, results demonstrate that the inclusion of such parametric models can successfully regulate controller conflict-resolution taskload.

Railway network delay evolution: A heterogeneous graph neural network approach

Train operations are subject to stochastic variations, reducing service punctuality and thus the quality of service (QoS). Models of such variations are needed to evaluate and predict the potential impact of disturbances and to avoid service punctuality reduction in train service management and timetabling. In this paper, through a case study of the Wuhan–Guangzhou (WH–GZ) high-speed rail (HSR), we show how a wealth of train operation records can be used to model the stochastic nature of train operations at each level, section and station. Specifically, we examine different distribution models for running times of individual sections and show that the Log-logistic probability density function is the best distributional form to approximate the empirical distribution of running times on the specified line. Next, we show that the distribution of running times in each section can be used to accurately infer arrival delays. Consequently, we construct the underlying analytical model and derive the respective arrival delay distribution at the downstream stations. The results support the correctness of the model presented and show that the proposed model is suitable for constructing the distribution of arrival delays at every station of the specified line. We show that the integrated distribution models of running times and arrival delays, driven by empirical data, can also be used to evaluate the QoS at individual track sections.

This study investigated the potential for development of station catchment areas around the proposed California High-Speed Rail System. The study was prompted by a review of practices of Japanese railway company groups that engage not only in train operations, but also in business diversification and property development within the station areas of their lines. These actions allow the company groups to diversify their revenues streams, increase ridership on their lines, and operate as a whole with net profits. This is in contrast to transit agencies in the United States, which only focus on transporting passengers along their lines and do not engage in other commercial activities. This situation limits the potential for transit in the United States to play a larger role in urban transportation. With the implementation of the California High-Speed Rail System, an opportunity exists to introduce the commercial transit model seen in Japan to the United States. Since the California High-Speed Rail System is a brand-new system with few entrenched interests to impede change, it has the potential to lead as an example of transit operating with net profits and providing additional benefits to the station areas it serves. However, since planning for station areas to turn into commercially successful activity centers is still a new concept and practice in the United States, a methodology has to be developed to assess the potential for development of station areas. This study set out to answer the two questions: 1) To what extent are the locations of the California High-Speed Rail System’s planned stations currently attractive to development within their respective contexts? 2) Given the information gathered from the study, what policies should be taken to enhance the future development potential of the California High-Speed Rail System’s planned stations as activity centers within their respective station areas? The potential for development was quantified by calculating accessibility indices for each station catchment area using the inputs of number of jobs, population size, and number of housing units within a gravity model. The results of the analysis indicate that the station areas at the ends of the alignment in the San Francisco Bay Area and the Los Angeles Metropolitan Area will benefit the most. The next biggest beneficiaries are the major population centers in the San Joaquin Valley, which are Fresno and Bakersfield. Other stations that are not likely enjoy the benefits of a high-speed rail connection as much as other

A Bayesian network model to predict the effects of interruptions on train operations

The European Commission initiated the process of liberalization and introducing competition in the European railway sector more than twenty-five years ago. Despite the liberalization of the railway sector, train paths are currently administratively allocated in all EU countries using the train service priority criterion, which may not treat all train operators equally. This is especially evident in those network sections where demand exceeds the available capacity. In these situations, economic theory suggests the implementation of a market-based mechanism for allocation of capacity, such as auctions. However, due to its incompatibilities with priority criteria in the process of the capacity allocation, it is necessary to develop a new procedure to support the implementation of an auction. In this paper, the proposed algorithm fills the technological gap between train timetable design and train operator requests. The new algorithm for decentralized capacity allocation is the result of a multidimensional approach, which encompasses setting new relations between train operators and the infrastructure manager, train timetable drafting and resolving the conflicting request. In addition, the algorithm provides a feasible solution ensuring equal treatment of train operators and efficient allocation, in order to foster the development of the competition in the European rail market.

Background: Introducing Magnetic Levitation (Maglev) technology in a developing country is a big challenge that needs huge investments in infrastructure, operations and maintenance. Background information about the development of Maglev worldwide to date is included.&#x0D; Aim: Determine a methodology to estimate the full cost of travel and provide insights into the working model developed to include the calculations of the total social costs of building a new Maglev line for the Riyadh-Dammam corridor in Saudi Arabia and understand in what circumstances it is a suitable technology to use.&#x0D; Methods: The Spreadsheet Total Cost Model (STCM) is used to determine the calculations of operator costs, user costs, and environmental costs. However, the operator costs are related to infrastructure construction and maintenance costs, and costs associated with the acquisition, operation and maintenance of Maglev rolling stock. The user costs are dependent on the journey time, including access/egress time, waiting time, and in-vehicle time. The value of time is considered in order to get the user costs calculated. The external costs include air pollution, noise pollution, accident, and climate change per passenger-km.&#x0D; Results: The travel demand has to be forecasted in order to determine the total social costs, using the elasticity approach between the the proposed HSR and Maglev lines in terms of their number of trips and the generalised journey times. In addition, the generalised journey time is based on the in-vehicle time and service interval penalty. The Maglev system is operated at the capacity limit and the change in service is therefore forecasted to increase the Maglev demand by 24.6 % (3.25 million passengers). In terms of the total infrastructure costs, the infrastructure construction and maintenance costs are included and computed to be about € 835.4 million per year, using the capital recovery factor (0.06) based on 35 years of operation and a 5 % social discount rate. The acquiring, operating, and maintaining train’s unit cost is included in the calculation of rolling stock to achieve results of € 22.4 million, € 22.5 million, and € 40.9 million, respectively. In terms of the user costs, the access/egress time is computed as of 33 minutes, using the car, while the in-vehicle travel time and waiting time are resulted of 61.8 minutes and 7.8 minutes, respectively. The external environmental costs are based on accidents, and climate changes of € 8.87 million per year and € 8.13 million per year, respectively. However, the total social costs of Maglev line are computed as € 1.18 billion for 16.45 million passengers per year. This gives an average social cost of € 71.9 per passenger. The comparable figures for High-Speed Rail (HSR) are € 1.10 billion for 13.21 million passengers per year, giving an average social cost of € 83 per passenger.&#x0D; Conclusion: In conclusion, the Riyadh-Dammam Maglev system introduces a new intercity system into Saudi Arabia and brings new competition in the intercity transit market as a part of the future transport developments in the country. The average social cost for HSR is around 16 % higher than Maglev- but that is more proven technology.

The purpose of this study is to analyze the challenges and opportunities associated with the operation of high-speed trains in Indonesia as part of the transformation towards a more advanced transportation system. This study aims to identify obstacles that may arise in the construction and operation of high-speed railways, as well as look at the economic, social, and environmental opportunities that can result from the adoption of this transportation system. From the results of the analysis of opportunities and challenges, the right strategy will be developed to overcome existing obstacles. This research uses a qualitative approach with descriptive analysis. Data will be collected through literature studies and interviews with resource persons who have met the criteria. Next, data analysis is performed to identify patterns, similarities, and differences in terms of emerging challenges and opportunities. The results of the analysis show that despite facing challenges in infrastructure, regulation, and integration, high-speed rail has great potential in addressing congestion issues, improving connectivity, and bringing positive economic impact. This positive impact also extends to social and environmental aspects, with changes in mobility patterns and the potential for reducing greenhouse gas emissions.

The energy consumption of freight train is very huge. Among all kinds of the energy consumptions, the energy consumption of traction is the largest, which leads to a great cost of freight trains. The optimization of the speed curve plays an important role in energy-saving operation. In this paper, a speed curve optimization method based on Ant Colony Optimization (ACO) is established. A simulation model combined with the characteristics of train operation is built. The related data of energy consumption and the DYNAMIS are applied to analyze the train operation. The effectiveness of the optimization algorithm is demonstrated by the simulation results.

Train operation is a complex nonlinear process; it is difficult to establish accurate mathematical model. In this paper, we design ATO speed controller based on the input and output data of the train operation. The method combines multimodeling with predictive functional control according to complicated nonlinear characteristics of the train operation. Firstly, we cluster the data sample by using fuzzy-c means algorithm. Secondly, we identify parameter of cluster model by using recursive least square algorithm with forgetting factor and then establish the local set of models of the process of train operation. Then at each sample time, we can obtain the global predictive model about the system based on the weighted indicators by designing a kind of weighting algorithm with error compensation. Thus, the predictive functional controller is designed to control the speed of the train. Finally, the simulation results demonstrate the effectiveness of the proposed algorithm.

High-speed rail and air transport competition: Game engineering as tool for cost-benefit analysis