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A syncretic railway network (SRN) that comprises the railway, regional railway, and urban rail transit is the cornerstone for the socio-economic development of urban agglomerations. Most existing research studies consider a syncretic network as a single network, and therefore, they cannot capture the interdependent patterns in the network. Thus, the characteristics of interdependent node/edges and robustness of the interdependent critical infrastructure of the SRN remain under-researched. In this study, we investigated the critical infrastructure interdependent networks (CIINs) of the SRN. To this end, we designed a cascading failure model based on the real data of the CIINs of the SRN in the Chengdu-Chongqing urban agglomeration, and we examined the cascading failure in both the syncretic and single networks. Our results indicate that robustness decreased with increasing the number of layers for a targeted attack on the highest connected nodes of the theoretical coupled network. The robustness of the theoretical coupled network was inferior to that of the single-layer network, whereas the robustness of the actual network was contrary to this result. The robustness of the CIINs in the SRN with different coupled modes increased with an increase in the number of nodes/sections of the single-layer network forming the interdependent network. However, it decreased with an increasing number of interdependent nodes/sections. Further, the robustness of a load-based SRN was found to be extremely sensitive to the failure of higher-load stations, therefore, it is important to facilitate decision-making processes in the daily operations and emergency management of the SRN.

How well stations are connected with each other is a critical performance assessment of railway systems. Network analysis is a highly intuitive and interpretable approach to characterizing the physical connectivity of railway systems. However, the physical connectivity is often limited in depicting transportation network dynamics due to the lack of traffic flow information. This paper first comprehensively reviews the physical connectivity metrics and applies these metrics to evaluate the connectivity of China’s high-speed railway system. Then, through integrating the real-world network topology and travel demand data, we conduct the first data-driven research on comparing the physical connectivity with the logical connectivity of railway systems. The experiments demonstrate that the physical connectivity metrics cannot well represent the connectivity of railway systems, due to the neglect of the heterogeneous distribution and temporal patterns of the passenger flows.

Travel route options for passengers can provide data support for railway line planning, passenger flow organisation, and train operation establishment. A critical review of the literature indicates that previous studies mainly focused on choices offered by a single railway network path without much consideration of China’s normal-speed and high-speed integrated railway network and the effect of train timetable on passengers’ travel choice. In this study, a method based on generalised cost is proposed to discover the valid routes of passenger travel in the integrated network of China’s normal-speed and high-speed railways. After quantifying the effects of train fare, travel time, transfer, travel convenience, comfort, and other factors on the generalised expenses of passengers, this study presents a generalised cost determination method when individuals select an option from different seats of different trains of specific railway transport products. Theoretically, the valid routes considering the train schedule is defined, and a valid route search algorithm is designed using the deep traversal idea in a new valid route searching network. Considering the Lanzhou-Beijing passenger travel routes as an example, this study verifies the practicability of the generalised cost calculation method, as well as that of the valid routes search method.

Due to the introduction of the cloud platform, the business data of rail-water combined transport is concentrated and deposited in the cloud, which makes the secondary utilization of the big data of combined transport possible. At the same time, with the rapid growth of orders, the demand for intelligent matching arises at the historic moment. Based on the big data support system of rail-water combined transportation cloud platform, this paper constructs a hierarchical recommendation model for the application of order matching big data. Combined with distributed data computing technology, the personalized matching algorithm is studied, and the performance test of the algorithm is carried out. IAMS is used to deploy the distributed recommendation system based on different algorithms, and acceptance and coverage tests are carried out to verify the effectiveness of IOMS matching algorithm. It is of great practical value to optimize the matching efficiency of orders and thus improve the service quality and business handling efficiency of the freight e-commerce platform.

To study the performance and mutual influence of a syncretic railway network (SRN) that comprises high-speed railway, regional railway, and urban rail transit under the condition of traffic overload during peak hours, we discuss the interdependent characteristics on cascading overload failure of SRNs under the cooperative organization from the perspective of an interdependent network. However, most existing research on cascading failure in interdependent network ignores the inconsistency between the physical structure and transportation organization of the subnetwork in an actual network, in addition to the restrictions on the load redistribution strategy of stations and sections in the load-capacity model of the interdependent network; especially, the influence of transfer behavior on the load redistribution inter subnetwork. In this study, we investigate the robustness of an interdependent SRN under overload and risk propagation. We propose a partially interdependent network model of a multimode rail transit, develop a novel cascading overload failure model with tunable parameters of load redistribution inter subnetwork, and analyze interdependent characteristics, cascade failure process, and robustness of an SRN under multiscene conditions, i.e., different attack and load distribution strategies, via simulations. A case study of an SRN in the metropolitan area of Chengdu, China is presented; the results indicate that, when the reserve coefficient of the metro subnetwork is 0.4 and the overload coefficient of the regional railway subnetwork is greater than 1.2, the station reserve capacity and overload capacity of the SRN is appropriately improved. When passenger load increases to a certain range, the reserve and overload capacities of stations in the regional railway subnetwork do not considerably contribute to robustness. Thus, a surplus load distribution strategy is recommended to improve robustness. The results of this paper have considerable significance for the planning, structural optimization, and operation safety of SRNs.

The maintenance window scheme (MWS) is one of the most important railway transportation organizational plans and plays an important role in ensuring railway operational safety. However, MWS setting is a very complicated process, and most countries currently do so with the help of a computer-aided decision system. In general, a decision system can generate multiple alternatives for MWS within an acceptable time. Therefore, how to choose the best option from the alternatives is a vital decision. This paper presents a novel framework for MWS evaluation based on the Chinese railway system. Specifically, the requirements of each department related to MWS setting are analysed, and we construct an evaluation indicator system for MWS based on the preferences of different departments. Then, we apply the fuzzy soft set theory to MWS evaluation, a method that not only effectively deals with evaluation of uncertain information, but also gives flexibility for experts to input their subjective judgment. Additionally, using the “AND” operation of soft set theory allows combing evaluation information from multiple evaluators to give comprehensive results. Finally, a case study illustrates the proposed framework, showing that the proposed evaluation indicator system and evaluation method are effective and practical.

The stability of train timetables plays an important role in ensuring the safety and punctuality of train operations. Meanwhile, a reasonable stop plan has a significant effect on passenger and energy consumption. Existing research has focused exclusively on adjusting timetables after delays occur, or on adjusting them without considering stop plans. By contrast, in this study, we developed a train timetable and stop plan synchronization optimization model that is passenger and energy-saving oriented. The following objectives were considered for minimization: probability of train delays, energy consumption, and travel time of the trains. A parallelism selection genetic algorithm was designed to solve the model. The section of high-speed railway from Nanjing South to Shanghai Hongqiao was utilized as a case study to evaluate the proposed model. The results showed that, while meeting passenger demand, the proposed optimization approach increased the punctuality rate by 12.55% and decreased the energy consumption by 8.16%.

Train timetable plays an important role in the passenger train operations, which is typically developed or adjusted from the draft train timetable (DTT). The DTT serves as the basic framework and t...

Timetable optimization techniques offer opportunity for saving energy and hence reducing operational costs for high-speed rail services. The existing energy-saving timetable optimization is mainly concentrated on the train running state adjustment and the running time redistribution between two stations. Not only the adjustment space of timetables is limited, but also it is hard for the train to reach the optimized running state in reality, and it is difficult to get feasible timetable with running time redistribution between two stations for energy-saving. This paper presents a high-speed railway energy-saving timetable based on stop schedule optimization. Under the constraints of safety interval and stop rate, with the objective of minimizing the increasing energy consumption of train stops and the shortest travel time of trains, the high-speed railway energy-saving timetable optimization model is established. The fuzzy mathematics programming method is used to design an efficient algorithm. The proposed model and algorithm are demonstrated in the actual operation data of Beijing-Shanghai high-speed railway. The results show that the total operating energy consumption of the train is reduced by 3.7%, and the total travel time of the train is reduced by 11 minutes.

This paper presents a novel railway timetable diagnostic analysis framework based on train operation data. Firstly, we define the concept of train deviation and give a new definition of train delay. Secondly, we propose the delay high-incidence section, station and train identification methods On the basis of deviation-based visualization technology. Finally, we take Chengdu-Kunming railway line (from Puxiong-Xichangbei section) as an example to verify the proposed method. The results show that: (1) Our method can effectively identify stations and sections that are prone to delays; (2) Cargo trains are more likely to occur delay than passenger trains; (3) There is a strong linear correlation between arrival delay and departure delay.

The costs of fatalities and injuries from train accidents have a great impact on society. As part of our effort to understand the characteristics of past train accidents, this paper presents an analysis of significant train accidents occurring in China from 1954 to 2014. Rough set theory and associated rules approaches are applied in analyzing the collected data. The results show that although most derived rules are unique, some rules are worth noting. Collision accidents generally lead to more casualties than derailment accidents, and the most frequent cause of accidents is human error. Additionally, most train accidents occur during summer. These findings can provide railway leaders with lessons and rules learned from past accidents, thus facilitating the establishment of a safer railway operation environment in China.

A simple, rapid and environmentally friendly method for the determination of organophosphorus pesticides (OPPs) in soil and cucumber samples was developed by using dispersive solid phase extraction and low-toxicity vortex-assisted liquid–liquid microextraction (DSPE-LT-VALLME) and high performance liquid chromatography with ultraviolet detection (HPLC-UV). Clean-up of the extract from soil and cucumber was conducted by DSPE using multi-walled carbon nanotubes (MWCNTs) as the sorbent. Parameters that could influence the extraction efficiencies, including the type and volume of extraction solvent, time of the vortex, pH of the sample solution and salt addition, were investigated. The linearity of the method was in the range from 1.0 to 600 ng g−1 with correlation coefficients (r) ranging from 0.9992 to 0.9999. The limits of detection (LODs) were 0.018 to 0.12 ng g−1. The relative standard deviations (RSDs) varied from 1.66% to 3.62% (n = 6), and the enrichment factors (EFs) for the target analytes were in the range of 128–136. Finally, the proposed method has been successfully applied to the determination of target analytes in real samples. The recoveries of the target analytes in soil and cucumber samples were between 83.10% and 101.20%.

Short-term metro passenger flow forecasting is an essential component of intelligent transportation systems (ITS) and can be applied to optimize the passenger flow organization of a station and offer data support for metro passenger flow early warning and system management. LSTM neural networks have recently achieved remarkable recent in the field of natural language processing (NLP) because they are well suited for learning from experience to predict time series. For this purpose, we propose an empirical mode decomposition (EMD)-based long short-term memory (LSTM) neural network model for predicting short-term metro inbound passenger flow. The EMD algorithm decomposes the original sequential passenger flow into several intrinsic mode functions (IMFs) and a residual. Selected IMFs that are strongly correlated with the original data can be obtained via feature selection. The selected IMFs and the original data are integrated into inputs for LSTM neural networks, and a single LSTM prediction model and an EMD-LSTM hybrid forecasting model are developed. Finally, historical real automatic fare collection (AFC) data from metro passengers are collected from Chengdu Metro to verify the validity of the proposed EMD-LSTM prediction model. The results indicate that the proposed EMD-LSTM hybrid forecasting model outperforms the LSTM, ARIMA and BPN models.

In view of the existing high-speed railway train operation plan system cannot accurately, comprehensively and intuitively reflect the transport equipment operating load and ability to adapt to the situation, cannot reflect the train group operation, operation diagram, station work plan between the complex interactive relationship, a train operation simulation module is designed in this paper which through the construction of high-speed railway traffic related electronic map and technical standards database, computer simulation on the train diagram has been made. Then, a high-speed railway operation plan simulation system is developed, which includes train planning, traction calculation and train operation simulation. Finally, simulation results show that high-speed railway operation plan simulation system can accurately simulate the whole process technical operation of the train group operation, operation process, generate statistical analysis results, thus we can get real-time visual inspection of the adaptability of operating equipment ability in the operation scheme, and provide strong support for the work of high-speed train running organization. It is of great significance to improve the level of high-speed railway traffic organization.

Fully implicit petroleum reservoir simulations result in huge, often very ill-conditioned linear systems of equations to solve for different unknowns, for example, pressure and saturations. It is well known that the full system matrix contains both hyperbolic as well as nearly elliptic sub-systems. Since the solution of the coupled system is mainly determined by the solution of their elliptic (typically pressure) components, (CPR-type) two-stage preconditioning methods still belong to the most popular approaches to tackle such coupled systems. After a suitable extraction and decoupling, the numerically most costly step in such two-stage methods consists in solving these elliptic sub-systems. It is known that algebraic multigrid (AMG) provides a technique to solve elliptic linear equations very efficiently. The main advantage of AMG-based solvers – their numerical scalability – makes them particularly efficient for solving huge linear systems. Depending on the application, the system’s properties range from simple to highly indefinite. Unfortunately decoupling pressure and saturation related parts may introduce further difficulties. Consequently, in complex industrial simulations, the application of AMG to elliptic sub-systems might not be straightforward. In fact, an important goal in defining an efficient two-stage preconditioning strategy consists in extracting elliptic sub-systems that are suitable for an efficient AMG solution and, at the same time, ensure a fast overall convergence of the two-stage approach. The importance of this will be demonstrated for several industrial cases. In particular, some of these cases are very hard to solve by AMG if applied in a standard way. Preliminary results for a CPR-type coupling of SAMG to CMG’s PARASOL, a variable degree variable ordering ILU preconditioner using FGMRES, are compared to using PARASOL by itself. Alternative preconditioning operators will be presented giving elliptic sub-systems which are not only more suitable for applying AMG efficiently but also help accelerate the CPR-type process. Comparisons with one-level iterative methods will show the acceleration by AMG is highly superior. Finally, a strategy is presented that combines all linear solver parts in one single AMG-iteration. In this sense CPR can be seen as a special case of AMG for systems. This, in turn, yields a – formally – very simple but simultaneously very flexible solution approach.

Short-term metro passenger flow forecasting is an essential component of intelligent transportation systems (ITS) and can be applied to optimize the passenger flow organization of a station and offer data support for metro passenger flow early warning and system management. LSTM neural networks have recently achieved remarkable recent in the field of natural language processing (NLP) because they are well suited for learning from experience to predict time series. For this purpose, we propose an empirical mode decomposition (EMD)-based long short-term memory (LSTM) neural network model for predicting short-term metro inbound passenger flow. The EMD algorithm decomposes the original sequential passenger flow into several intrinsic mode functions (IMFs) and a residual. Selected IMFs that are strongly correlated with the original data can be obtained via feature selection. The selected IMFs and the original data are integrated into inputs for LSTM neural networks, and a single LSTM prediction model and an EMD-LSTM hybrid forecasting model are developed. Finally, historical real automatic fare collection (AFC) data from metro passengers are collected from Chengdu Metro to verify the validity of the proposed EMD-LSTM prediction model. The results indicate that the proposed EMD-LSTM hybrid forecasting model outperforms the LSTM, ARIMA and BPN models.

This paper proposes a method of high-speed railway train operation diagram evaluation based on preferences of locomotive operation, track maintenance, S & C, vehicles and other railway departments, and customer preferences. The application of rough set-based attribute reduction obtains the important relative indicators by eliminating excessive and redundant evaluation indicators. Soft fuzzy set theory is introduced for the overall evaluation of train operation diagrams. Each expert utilizes a set of indicators during evaluation based on personal preference. In addition, soft fuzzy set theory is applied to integrate the information obtained via expert evaluation in order to obtain an overall evaluation. The proposed method was validated by a case study. Results demonstrate that the proposed method flexibly expresses the subjective judgments of experts while effectively and reasonably handling the uncertainty of information, which is consistent with the judgment process of humans. The proposed method is also applicable to the evaluation of train operation schemes which consist of multiple diagrams.

The stop-schedules for passenger trains are important to the operation planning of high-speed trains, and they decide the quality of passenger service and the transportation efficiency. This paper analyzes the specific manifestation of passenger travel convenience and proposes the concepts of interstation accessibility and degree of accessibility. In consideration of both the economic benefits of railway corporations and the travel convenience of passengers, a multitarget optimization model is established. The model aims at minimizing stop cost and maximizing passenger travel convenience. Several constraints are applied to the model establishment, including the number of stops made by individual trains, the frequency of train service received by each station, the operation section, and the 0-1 variable. A hybrid genetic algorithm is designed to solve the model. Both the model and the algorithm are validated through case study.

The initial schedule of passenger trains is the base of train working diagram . The problem of initial schedule of passenger trains is translated to the n-level decision-making problem in the paper. Then we proposed a heuristic ant colony optimization algorithm to resolve the problem and accelerated the speed of the ants search the optimal paths based on the neighborhood of the point . Furthermore, some constraint process strategy are introduced to improve the algorithm. Finally, the example is proposed to prove the effectiveness of the model and algorithm.

The departure time domain of passenger trains is mainly to solve the overall layout problem of the passenger trains running line. A reasonable departure time domain of passenger trains can not only construct the skeleton and the core issue of the passenger train working diagram, but also improve the feasibility of the working diagram. The system for optimizing departure time domain and arrival time domain of passenger trains is developed based on the genetic algorithm constraints of the receiving and dispatching trains capacity of railway passenger transportation nodes model in the paper. The goal and function of the system are detailed description. Then the corresponding actual simulation is proposed to validate the effectiveness of the system.

Railway freight volume is an important index in railway transportation. This paper combined Autoregressive Moving-average (ARMA) model with multivariate regression model to analyze the variables that affected the rail freight volume. Ten variables are selected as explanatory variables to study their relationship with rail freight volume. The results show that the rail freight volume is affected by many factors together and the authors also get the rail freight regression equation. The authors results can provide the support for the traffic department to develop the transport plan.

Locomotive scheduling and train scheduling are important parts of the railway operation planning, locomotive scheduling should be cooperated with train scheduling, which could be benefit railroad operation efficiency and make railroad operation be more productive. This paper discuss the Cooperation design for locomotive scheduling and train scheduling based on Train Working Diagram System (TWDS). First, analyzing the business work flow between locomotive scheduling and train scheduling, and coordination process in TWDS. Then, according to the data flow and business requirements, design the mode of data sharing and data synchronizing. Finally, cooperation function and cooperation mode were designed for functions cooperation between locomotive scheduling and train scheduling.

Passenger train operation plans under uncertain information based on the measures of the possibilities are discussed in the paper. Taking the maximum transportation profit and minimizing the costs of passengers as the objective function, this paper establishes a fuzzy chance-constrained programming model based on the fuzzy passengers, number of arrival and departure tracks, and interval capacity. Furthermore, we also discuss the heuristic strategy with algorithm.