Design of Integrated Monitoring and Early Warning System of Urban Rail Transit Train Running State

The monitoring and warning of urban rail transit is the core of operation management, and the breadth and depth of the monitoring range directly affect the quality of urban rail transit operation. For the current domestic monitoring system, most of the critical equipments and technologies are introduced from abroad;it is diseconomy, and also causes hidden danger. Realizing the localization of monitoring and early warning system is imperative. Based on the analysis of the present situation of urban rail transit operation safety at home and abroad, the paper proposes to use integrated technology to design basic framework of monitoring and warning system of urban rail train, and puts forward the critical technologies to realize the system. Compared with the existing monitoring system, the integrated monitoring system has the characteristics of wide monitoring range, clear division of labor, centralized management, coordination and integration operation and intelligent management, and embodies the concept of people-oriented. It has scientific significance for future construction of domestic Integrated Monitoring and Early Warning System (IMEWS) of urban rail transit.

OTFS Modulation and PAPR Reduction for IoT-Railways

The internet of things(IoT)has been widely considered to be integrated with high-speed railways to improve safety and service.It is important to achieve reliable communication in IoT for railways(IoT-R)under high mobility scenarios and strict energy constraints.Orthogonal time frequency space(OTFS)modulation is a two-dimensional modulation technique that has the potential to overcome the challenges in high Doppler environments.In addition,OTFS can have lower peak-to-average power ratio(PAPR)compared to orthogonal frequency division multiplexing,which is especially important for the application of IoT-R.Therefore,OTFS modulation for IoT-R is investigated in this paper.In order to decrease PAPR of OTFS and promote the application of OTFS modulation in IoT-R,the peak windowing technique is used in this paper.This technique can reduce the PAPR of OTFS by reducing the peak power and does not require multiple iterations.The impacts of different window functions,window sizes and clipping levels on PAPR and bit error rate of OTFS are simulated and discussed.The simulation results show that the peak windowing technique can efficiently reduce the PAPR of OTFS for IoT-R.

Cooperative adaptive bidirectional control of a train platoon for efficient utility and string stability

This paper proposes cooperative adaptive control schemes for a train platoon to improve efficient utility and guarantee string stability. The control schemes are developed based on a bidirectional strategy, i.e., the information of proximal（preceding and following） trains is used in the controller design. Based on available proximal information（prox-info） of location, speed, and acceleration, a direct adaptive control is designed to maintain the tracking interval at the minimum safe distance. Based on available prox-info of location, an observer-based adaptive control is designed to achieve the same target, which alleviates the requirements of equipped sensors to measure prox-info of speed and acceleration. The developed schemes are capable of on-line estimating of the unknown system parameters and stabilizing the closed-loop system, the string stability of train platoon is guaranteed on the basis of Lyapunov stability theorem. Numerical simulation results are presented to verify the effectiveness of the proposed control laws.

Modeling and simulation of high-speed passenger train movements in the rail line

In this paper, we propose a new formula of the real-time minimum safety headway based on the relative velocity of consecutive trains and present a dynamic model of high-speed passenger train movements in the rail line based on the proposed formula of the minimum safety headway. Moreover, we provide the control strategies of the high-speed passenger train operations based on the proposed formula of the real-time minimum safety headway and the dynamic model of highspeed passenger train movements. The simulation results demonstrate that the proposed control strategies of the passenger train operations can greatly reduce the delay propagation in the high-speed rail line when a random delay occurs.

A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit Line

Regular coronavirus disease 2019(COVID-19)epidemic prevention and control have raised new require-ments that necessitate operation-strategy innovation in urban rail transit.To alleviate increasingly seri-ous congestion and further reduce the risk of cross-infection,a novel two-stage distributionally robust optimization(DRO)model is explicitly constructed,in which the probability distribution of stochastic scenarios is only partially known in advance.In the proposed model,the mean-conditional value-at-risk(CVaR)criterion is employed to obtain a tradeoff between the expected number of waiting passen-gers and the risk of congestion on an urban rail transit line.The relationship between the proposed DRO model and the traditional two-stage stochastic programming(SP)model is also depicted.Furthermore,to overcome the obstacle of model solvability resulting from imprecise probability distributions,a discrepancy-based ambiguity set is used to transform the robust counterpart into its computationally tractable form.A hybrid algorithm that combines a local search algorithm with a mixed-integer linear programming(MILP)solver is developed to improve the computational efficiency of large-scale instances.Finally,a series of numerical examples with real-world operation data are executed to validate the pro-posed approaches.

Neural adaptive chaotic control with constrained input using state and output feedback

This paper presents neural adaptive control methods for a class of chaotic nonlinear systems in the presence of constrained input and unknown dynamics. To attenuate the influence of constrained input caused by actuator saturation, an effective auxiliary system is constructed to prevent the stability of closed loop system from being destroyed. Radial basis function neural networks（RBF-NNs） are used in the online learning of the unknown dynamics, which do not require an off-line training phase. Both state and output feedback control laws are developed. In the output feedback case, high-order sliding mode（HOSM） observer is utilized to estimate the unmeasurable system states. Simulation results are presented to verify the effectiveness of proposed schemes.

Wireless channel estimation for high-speed rail communications:Challenges,solutions and future directions

With the development of High-Speed Rail(HSR),countries and individual passengers alike have enjoyed far ranging benefits as a result-economic,social,environment and in added convenience.One of the important parts of HSR construction is the signaling system,where wireless communications play a key role in the transmission of train control data.Channel estimation has a significant impact on the quality of the wireless communication,however,whose performance is degraded due to the fast mobility of HSR.This paper focuses on the channel estimation technology in HSR.We first summarize the key challenges for HSR channel estimation,especially the Inter-Carrier Interference(ICI)faced by Orthogonal Frequency Division Multiplexing(OFDM)systems.Then we provide a comprehensive review of existing pilot-aided channel estimation schemes from three points:channel model,estimation algorithm,joint channel estimation and ICI mitigation schemes.Lastly,we present the challenges of channel estimation for the Orthogonal Time Frequency Space(OTFS)system and Reconfigurable Intelligent Surface(RIS),which are promising techniques for HSR systems in the future sixth Generation(6G)wireless communication.

Detection and Diagnosis of Urban Rail Vehicle Auxiliary Inverter Using Wavelet Packet and RBF Neural Network

This study concerns with fault diagnosis of urban rail vehicle auxiliary inverter using wavelet packet and RBF neural network. Four statistical features are selected: standard voltage signal, voltage fluctuation signal, impulsive transient signal and frequency variation signal. In this article, the original signals are decomposed into different frequency subbands by wavelet packet. Next, an automatic feature extraction algorithm is constructed. Finally, those wavelet packet energy eigenvectors are taken as fault samples to train RBF neural network. The result shows that the RBF neural network is effective in the detection and diagnosis of various urban rail vehicle auxiliary inverter faults.

Fault Isolation of Light Rail Vehicle Suspension System Based on D-S Evidence Theory and Improvement Application Case

This paper presents an innovative approach for the fault isolation of Light Rail Vehicle (LRV) suspension system based on the Dempster-Shafer (D-S) evidence theory and its improvement application case. The considered LRV has three rolling stocks and each one equips three sensors for monitoring the suspension system. A Kalman filter is applied to generate the residuals for fault diagnosis. For the purpose of fault isolation, a fault feature database is built in advance. The Eros and the norm distance between the fault feature of the new occurred fault and the one in the feature database are applied to measure the similarity of the feature which is the basis for the basic belief assignment to the fault, respectively. After the basic belief assignments are obtained, they are fused by using the D-S evidence theory. The fusion of the basic belief assignments increases the isolation accuracy significantly. The efficiency of the proposed method is demonstrated by two case studies.

A Control Simulation Method of High-Speed Trains on Railway Network with Irregular Influence

Based on the discrete time method,an effective movement control model is designed for a group of highspeedtrains on a rail network.The purpose of the model is to investigate the specific traffic characteristics of high-speedtrains under the interruption of stochastic irregular events.In the model,the high-speed rail traffic system is supposedto be equipped with the moving-block signalling system to guarantee maximum traversing capacity of the railway.Tokeep the safety of trains’ movements,some operational strategies are proposed to control the movements of trains inthe model,including traction operation,braking operation,and entering-station operation.The numerical simulationsshow that the designed model can well describe the movements of high-speed trains on the rail network.The researchresults can provide the useful information not only for investigating the propagation features of relevant delays underthe irregular disturbance but also for rerouting and rescheduling trains on the rail network.

Measurement and analysis of Doppler shift for high-speed rail scenario

Based on the analysis of the impact of Doppler shift to the railway dedicated mobile communication quality under the condition of high-speed mobility,this paper presents a Doppler shift measurement approach based on the phase estimation of frequency correction channel(FCCH) which correctly reflects the time-varying characteristic of the channel with a testing system developed.The Doppler shift data under the high-speed condition is collected,processed,analyzed,and compared with the simulation results.The scientific laws of the Doppler shift distribution of radio channel under high-speed condition are obtained.These data and analysis are essential for the establishment of high-speed railway Doppler power spectrum model and the development of the key technology of antiDoppler shift.

Stationarity Intervals of Time-Variant Channel in High Speed Railway Scenario

The non-stationary behavior, caused by the train movement, is the main factor for the variation of high speed railway channel. To measure the time-variant effect, the parameter of stationarity interval, in which the channel keeps constant or has no great change, is adopted based on Zhengzhou-Xi'an (Zhengxi) passenger dedicated line measurement with different train speeds. The stationarity interval is calculated through the definition of Local Region of Stationarity (LRS) under three train velocities. Furthermore, the time non-stationary characteristic of high speed railway channel is compared with five standard Multiple-Input Multiple-Output (MIMO) channel models, i.e. Spatial Channel Model (SCM), extended version of SCM (SCME), Wireless World Initiative New Radio Phase II (WINNERII), International Mobile Telecommunications-Advanced (IMT-Advanced) and WiMAX models which contain the high speed moving scenario. The stationarity interval of real channel is 9 ms in 80% of the cases, which is shorter than those of the standard models. Hence the real channel of high speed railway changes more rapidly. The stationarity intervals of standard models are different due to different modeling methods and scenario definitions. And the compared results are instructive for wireless system design in high speed railway.

Future 5G-Oriented System for Urban Rail Transit:Opportunities and Challenges

As a development direction of urban rail transit system,the train autonomous circumambulate system(TACS)can operate in a safer,more efficient,and more economical mode.However,most urban rail transit systems transmit signals through industrial,scientific,and medical(ISM)frequency bands or narrow frequency bands,which cannot meet the requirements of TACS.As a promising solution,the 5th generation(5G)mobile communication provides more services for the future urban rail transit systems,and covers the shortages of exiting communication technologies in terms of capacity and reliability.In this paper,we first briefly review the research status of current train control system and introduce its limitations.Next,we propose a novel network architecture,and present new technologies and requirements of the proposed architecture for TACS.Some potential challenges are then discussed to give insights for further research of TACS.

Analyzing the causation of a railway accident based on a complex network

In this paper, a new model is constructed for the causation analysis of railway accident based on the complex network theory. In the model, the nodes are defined as various manifest or latent accident causal factors. By employing the complex network theory, especially its statistical indicators, the railway accident as well as its key causations can be analyzed from the overall perspective. As a case, the ＂7.23＂ China-Yongwen railway accident is illustrated based on this model. The results show that the inspection of signals and the checking of line conditions before trains run played an important role in this railway accident. In conclusion, the constructed model gives a theoretical clue for railway accident prediction and, hence, greatly reduces the occurrence of railway accidents.

AoA-Based Channel Estimation for Massive MIMO OFDM Communication Systems on High Speed Rails

Channel estimation is a well-known challenge for wireless orthogonal frequency division multiplexing(OFDM)communication systems with massive antennas on high speed rails(HSRs).This paper investigates this problem and design two practicable uplink and downlink channel estimators for orthogonal frequency division multiplexing(OFDM)communication systems with massive antenna arrays at base station on HSRs.Specifically,we first use pilots to estimate the initial angle of arrival(AoA)and channel gain information of each uplink path through discrete Fourier transform(DFT),and then refine the estimates via the angle rotation technique and suggested pilot design.Based on the uplink angel estimation,we design a new downlink channel estimator for frequency division duplexing(FDD)systems.Additionally,we derive the Cramér-Rao lower bounds(CRLBs)of the AoA and channel gain estimates.Finally,numerical results are provided to corroborate our proposed studies.

Seat inventory control methods for Chinese passenger railways

Railway seat inventory control strategies play a crucial role in the growth of profit and train load factor. The railway passenger seat inventory control problem in China was addressed. Chinese passenger railway operation features and seat inventory control practice were analyzed firstly. A dynamic demand forecasting method was introduced to forecast the coming demand in a ticket booking period. By clustering, passengers' historical ticket bookings were used to forecast the demand to come in a ticket booking period with least squares support vector machine. Three seat inventory control methods: non-nested booking limits, nested booking limits and bid-price control, were modeled under a single-fare class. Different seat inventory control methods were compared with the same demand based on ticket booking data of Train T15 from Beijing West to Guangzhou. The result shows that the dynamic non-nested booking limits control method performs the best, which gives railway operators evidence to adjust the remaining capacity in a ticket booking period.

Channel Estimation and Throughput Evaluation for 5G Wireless Communication Systems in Various Scenarios on High Speed Railways

The fifth generation(5 G) wireless communication is currently a hot research topic and wireless communication systems on high speed railways(HSR) are important applications of 5 G technologies. Existing studies about 5 G wireless systems on high speed railways(HSR) often utilize ideal channel parameters and are usually based on simple scenarios. In this paper, we evaluate the downlink throughput of 5 G HSR communication systems on three typical scenarios including urban, cutting and viaduct with three different channel estimators. The channel parameters of each scenario are generated with tapped delay line(TDL) models through ray-tracing simulations, which can be considered as a good match to practical situations. The channel estimators including least square(LS), linear minimum mean square error(LMMSE), and our proposed historical information based basis expansion model(Hi BEM). We analyze the performance of the Hi BEM estimator in terms of mean square error(MSE) and evaluate the system throughputs with different channel estimates over each scenario. Simulation results are then provided to corroborate our proposed studies. It is shown that our Hi BEM estimator outperforms other estimators and that the sys-

Regression model for daily passenger volume of high-speed railway line under capacity constraint

A non-linear regression model is proposed to forecast the aggregated passenger volume of Beijing-Shanghai high-speed railway(HSR) line in China. Train services and temporal features of passenger volume are studied to have a prior knowledge about this high-speed railway line. Then, based on a theoretical curve that depicts the relationship among passenger demand, transportation capacity and passenger volume, a non-linear regression model is established with consideration of the effect of capacity constraint. Through experiments, it is found that the proposed model can perform better in both forecasting accuracy and stability compared with linear regression models and back-propagation neural networks. In addition to the forecasting ability, with a definite formation, the proposed model can be further used to forecast the effects of train planning policies.

Evolution of network from node division and generation

Aimed at lowering the effect of ＇rich get richer＇ in scale-free networks with the Barab^si and Albert model, this paper proposes a new evolving mechanism, which includes dividing and preference attachment for the growth of a network. A broad scale characteristic which is independent of the initial network topology is obtained with the proposed model. By simulating, it is found that preferential attachment causes the appearance of the scale-free characteristic, while the dividing will decrease the power-law behaviour and drive the evolution of broad scale networks.

Road traffic states estimation algorithm based on matching of regional traffic attracters

To effectively solve the traffic data problems such as data invalidation in the process of the acquisition of road traffic states,a road traffic states estimation algorithm based on matching of the regional traffic attracters was proposed in this work.First of all,the road traffic running states were divided into several different modes.The concept of the regional traffic attracters of the target link was put forward for effective matching.Then,the reference sequences of characteristics of traffic running states with the contents of the target link's traffic running states and regional traffic attracters under different modes were established.In addition,the current and historical regional traffic attracters of the target link were matched through certain matching rules,and the historical traffic running states of the target link corresponding to the optimal matching were selected as the initial recovery data,which were processed with Kalman filter to obtain the final recovery data.Finally,some typical expressways in Beijing were adopted for the verification of this road traffic states estimation algorithm.The results prove that this traffic states estimation approach based on matching of the regional traffic attracters is feasible and can achieve a high accuracy.