An Optimization Method for Operation Adjustment of High-Speed Delayed Trains

The business scale of China's high-speed railroad is increasing, which has brought important impact on the economy of the cities passing along the high-speed railroad. High-speed railroad has faster speed, safer, and stronger ability to transport goods compared with train, which provides a better choice for people's transportation, tourism, etc., and also has a great impact on the economic development of the cities along the route. This paper is based on the Wuhan-Guangzhou high-speed railway along Hunan Province.To ensure accurate empirical results, we specifically choose Hunan Province as the basis for data analysis, as it is the only province in the area and eliminates potential interference from other municipalities. We collect data from 2005 to 2019, specifically from prefectural-level cities and above. The empirical analysis is conducted using the double difference model (DID). An analysis is conducted on the effects of high-speed railroads on the growth rate of GDP, per capita GDP, the total employment at the end of the year, and the urban development along the lines. The findings indicate that high-speed rail has a discernible bias in its economic impact on cities along its route. Specifically, it has a substantial influence on the employment and urbanisation development of these cities. However, it does not have a significant effect on the short-term per capita GDP and its growth rate.

Deceptive path planning (DPP) aims to find a path that minimizes the probability of the observer identifying the real goal of the observed before it reaches. It is important for addressing issues such as public safety, strategic path planning, and logistics route privacy protection. Existing traditional methods often rely on “dissimulation”—hiding the truth—to obscure paths while ignoring the time constraints. Building upon the theory of probabilistic goal recognition based on cost difference, we proposed a DPP method, DPP_Q, based on count-based Q-learning for solving the DPP problems in discrete path-planning domains under specific time constraints. Furthermore, to extend this method to continuous domains, we proposed a new model of probabilistic goal recognition called the Approximate Goal Recognition Model (AGRM) and verified its feasibility in discrete path-planning domains. Finally, we also proposed a DPP method based on proximal policy optimization for continuous path-planning domains under specific time constraints called DPP_PPO. DPP methods like DPP_Q and DPP_PPO are types of research that have not yet been explored in the field of path planning. Experimental results show that, in discrete domains, compared to traditional methods, DPP_Q exhibits better effectiveness in enhancing the average deceptiveness of paths. (Improved on average by 12.53% compared to traditional methods). In continuous domains, DPP_PPO shows significant advantages over random walk methods. Both DPP_Q and DPP_PPO demonstrate good applicability in path-planning domains with uncomplicated obstacles.

Though the high-speed railways are seen as a sustainable form of transportation, the fact that the rail wear in high-speed railways negatively affects the running safety and riding comfort, as well as the maintenance of railways, has drawn a wide range of concerns among researchers and scholars. In order to reduce the rail wear and achieve the goal of sustainable transportation, this paper proposes an ingenious optimization program of rail profiles based on the artificial neural network (ANN) and genetic algorithm (GA) coupled method. The candidate solutions of the nonlinear GA programming model are regarded as the inputs of the trained ANN model. Meanwhile, the outputs of the trained ANN model serve as the objective functions of the GA model. The computational results show that the optimized rail profile not only has superior performances in terms of the wheel/rail wear and contact conditions, but also maintains good dynamic performances. Therefore, this study can provide the theoretical and practical basis for the design and the preventive grinding of rails in the high-speed railways. Also, the ANN-GA coupled model can be extended and further employed on the optimization of other rail profiles.

Passenger transfer is one of the important research contents after the network operation of high-speed railway. Due to the high operation speed and high frequency, delays at transfer stations may occur frequently, leading to some potential safety problems. Main influence factors such as schedule time, transfer buffer time, waiting time, and delay time of delay and passengers classification during transfer were analyzed. A delay adjustment model of passengers at a transfer station was presented with minimal delay cost, waiting time cost, and adjustment cost as the optimization goal. Two adjustment strategies were proposed, including transfer passengers departure time adjustment and connecting trains departure time adjustment. And a delay adjustment algorithm at transfer station was designed for the optimal adjustment scheme. A case study proves the effectiveness of the method on optimal scheme design for passengers transferbased on the lowest delay cost of all passengers.

The values of longitudinal horizontal linear-stiffness of piers are very important parameters in the design of welded turnout on bridge and they can have a great impact on the force and displacement of the turnout. The layout form of turnout and bridge of welded turnout structure system on high-speed railway bridges are various, so the values of longitudinal horizontal linear-stiffness of piers have to be limited in order to insure the strength and stability of track structure on bridges and at the same time meet the requirement of comparative displacement of beam and rail, turnout proper and frog. To make the value-taking easy in the design process, a finite element model for welded turnout-bridge-platform is established in this paper, which is based on the principle of longitudinal interaction of welded turnout on bridges. Directing at three typical layout forms (No.18 single turnout+4×32m continuous beam, single crossover+6×32m continuous beam and typical throat point+4×32m continuous beam) of welded turnout and bridge on ballast track, a research of the relation between the force and displacement of turnout, and the values of longitudinal horizontal linear-stiffness of piers has been carried out. Based on the comprehensive analysis, minimal values of longitudinal horizontal linear-stiffness of piers which are suitable for these three kinds of layout forms, and the values are 1000,800 and 1600 kN/cm·double-line respectively.

In recent years, with the rapid development of high-speed railways, the problem of differential settlement of road-bridge transition sections has become increasingly prominent. The uneven settlement of the road-bridge transition has a great impact on the driving safety and comfort of high-speed vehicles, especially for high-speed railway projects under special ground conditions such as soft stop. And with the continuous increase in train speed and axle weight, the differential settlement requirements at the over-bands are becoming more stringent. The problem of “jump at the bridgehead” has become one of the important factors affecting the safety of highspeed trains. In view of the current situation, theoretical analysis and numerical calculation are used as research methods in this paper. The factors of uneven settlement and deformation of the road-bridge transition section are systematically analyzed and corresponding methods for foundation treatment are proposed.

Experimental investigation and prediction model of permeability in solidified coarse-grained soil under freeze-thaw cycles in water-rich environments

Least squares estimations for approximate fractional Vasicek model driven by a semimartingale

The influence of track irregularity and deck deformation on the running safety of high-speed railway (HSR) trains is investigated, with emphasis placed on those caused by the creep of continuous prestressed bridges. A bridge model is established with CRTS II ballastless track to account for a train-track-bridge (TTB) system. The creep effect is calculated by the finite element software MIDAS/Civil. The accuracy of the numerical simulation is confirmed by comparing the numerical predictions with field measurements. Considering the stability index of the moving train, the deformation threshold for safe operation of the train is determined. The results show that the initial prestressing stress has a great impact on the residual deformation of the bridge, but the deformation of the rail is less affected by the stiffness of the fastener. Creep irregularity has a great influence on the comfort of the moving train, but does not affect its running safety. When the creep amplitude is greater than 4[Formula: see text]mm, the increase in the amplitude of the creep irregularity has a greater effect on the acceleration of the car body.

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.

Due to the great potential of large cascade hydropower stations on power generation, long-term scheduling of large cascade hydropower stations (LSLCHS) plays an important role in electrical power system. As more and more concentrations focused on the optimal operation of large cascade hydropower stations, the LSLCHS has been taken into a multi-dimensional, non-convex and non-linear optimization problem due to its complicated hydraulic connection relationships and varieties of complex constraints with considering its power generation, shipping and ecological characteristics. In order to solve this problem, a strategy of solution rapid adjustment regarding the principle of monotone principle is proposed accordingly. The simulation results show that the proposed method is an efficient for solving joint optimization dispatch model of cascade hydropower stations with fast convergent rate, strong robustness.

Fractured-caved carbonate reservoirs typically exhibit multi-medium characteristics, comprising pores, fractures, and large-scale cavities. The coexistence of these complex flow systems gives rise to intricate fluid migration behavior and presents considerable challenges in controlling water production and enhancing oil recovery. Adjusting the fluid potential based on reservoir hydrodynamics is considered an essential approach for well pattern management in such reservoirs. Nevertheless, the existing fluid potential regulation theories are still constrained by incomplete formulations and a lack of reliable quantitative characterization tailored for fractured-caved carbonate settings. To address these limitations, this study develops a fluid potential calculation and characterization methodology specifically adapted to the flow mechanisms of fractured-caved carbonate reservoirs. Through a comparative analysis of the relative magnitudes of various potential energy components within fractured-caved units, the primary controlling factors influencing the fluid potential distribution were systematically identified, leading to the establishment of a theoretical representation model. Based on this framework, bottom-hole pressure data from representative production wells were processed to normalize the fluid potential distribution across the reservoir units. By incorporating production performance analysis, development potential assessment, and inter-well connectivity evaluation, well-specific adjustment strategies were formulated, and a practical fluid potential adjustment policy chart was constructed. The proposed methodology was applied to 27 well groups in the T10 oilfield of the Tarim Basin, China. The calculated fluid potential fields for different fractured-caved units enabled the design of differentiated adjustment schemes targeting specific potential wells. Field implementation over a five-year period resulted in a cumulative oil production increase of approximately 746000 t, while maintaining the average water cut consistently between 75% and 85%. These outcomes demonstrate the reliability and practical value of the proposed approach. The fluid potential calculation method and the adjustment policy chart presented in this work offer effective technical solutions for optimizing the development of similar fractured-caved carbonate reservoirs. Keywords: fractured-caved carbonate reservoirs; fluid potential; development potential evaluation; reservoir production optimization; hydrodynamic adjustment strategy

There is an increasing need for developing strategies to face droughts because of high dependencies of societies on water. This need has been intensified due to the water scarcity and the impact of climate change. The frequency and magnitude of droughts have been increased in many parts of the world because of climate change effects. When facing droughts, using sound strategies in real time could decrease the drought damages considerably. Virtual Drought Exercise (VDE), a strategy for drought management, can increase a system's readiness to deal with droughts. VDE gives the decision makers the opportunity to simulate the disasters without taking any risk. In this study, an algorithm for VDE has been presented. In developing VDE scenarios, the first step is identification of key triggers of drought. In this paper, after determination of all effective triggers on drought management, their consequences are evaluated using the unranked pairwise comparison technique. The triggers with greatest impact can be used for developing VDE scenarios. The Ahar-chay river basin located in northwestern Iran has been as case study of this paper. Utilizing the proposed scenarios, decision makers can prepare the system for better management of water shortages during droughts.

With the increasing complexity of power system structures and the increasing penetration of renewable energy, the number of possible power system operation modes increases dramatically. It is difficult to make manual power flow adjustments to establish an initial convergent power flow that is suitable for operation mode analysis. At present, problems of low efficiency and long time consumption are encountered in the formulation of operation modes, resulting in a very limited number of generated operation modes. In this paper, we propose an intelligent power flow adjustment and generation model based on a deep network and reinforcement learning. First, a discriminator is trained to judge the power flow convergence, and the output of this discriminator is used to construct a value function. Then, the reinforcement learning method is adopted to learn a strategy for power flow convergence adjustment. Finally, a large number of convergent power flow samples are generated using the learned adjustment strategy. Compared with the traditional flow adjustment method, the proposed method has significant advantages that the learning of the power flow adjustment strategy does not depend on the parameters of the power system model. Therefore, this strategy can be automatically learned without manual intervention, which allows a large number of different operation modes to be efficiently formulated. The verification results of a case study show that the proposed method can independently learn a power flow adjustment strategy and generate various convergent power flows.

The Traction Power Supply System (TPSS) as the only power source of High-Speed Railway (HSR), its maintenance management needs to be supported by a complete set of solutions. Maintenance mode decision is the primary and first part of the solutions. The most appropriate maintenance mode leads to the best maintenance effect, the least cost and the highest reliability. Because of the huge number of influence factors of maintenance mode decision and intricate relationship among those factors, the decision process is complicated. Therefore several alternative maintenance modes are summarized with their pros and cons. Factors Set of Maintenance Mode Decision (FSMMD) is established while the interdependence and the feedback relationships among factors are clarified and quantified. The Analytic Network Process (ANP) is applied and the ANP maintenance decision model is built to decide the most appropriate maintenance modes. The case study shows that the appropriate maintenance modes can be decided while the inappropriate maintenance modes of HSR TPSS can be avoid by this method.