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.

Influence of Random Track Irregularities on Dynamic Response of Bridge/Track Structure/High-Speed Train Systems

Random vertical track irregularities are one of essential vibration sources in bridge, track structure and high-speed train systems. The common model of such irregularities is a stationary and ergodic Gaussian process. The study presents the results of numerical dynamic analysis of advanced virtual models of composite BTT （bridge/ballasted track structure/high-speed train） systems. The analysis has been conducted for a series of types of single-span simply-supported railway composite （steel-concrete） bridges, with a symmetric platform, located on lines with ballasted track structure adapted for high-speed trains. The bridges are designed according to Polish bridge standards. A new methodology of numerical modeling and simulation of dynamic processes in BTT systems has been applied. The methodology takes into consideration viscoelastic suspensions of rail-vehicles, nonlinear Hertz wheel-rail contact stiffness and one-side wheel-rail contact, physically nonlinear elastic-damping properties of the track structure, random vertical track irregularities, approach slabs and other features. Computer algorithms of FE （finite element） modeling and simulation were programmed in Delphi. Both static and dynamic numerical investigations of the bridges forming the series of types have been carried out. It has been proved that in the case of common structural solutions of bridges and ballasted track structures, it is necessary to put certain limitations on operating speeds, macadam ballast and vertical track roughness.

Technical Feasibility Study of Passenger Rail Service along the West Route between Las Vegas and Los Angeles

The study evaluates the feasibility of running passenger train service from Las Vegas, NV on the Union Pacific Railroad (UPRR), to Barstow, on the Burlington Northern Santa Fe (BNSF) track, to Mojave on UPRR track again, and to Lancaster connecting Metrolink to their destinations in Southern California. In this study, the railroad infrastructure was inventoried and issues related to running the passenger service were identified. Passenger train operation was evaluated based on the Rail Traffic Controller (RTC) simulation model. The performance measures of passenger trains including travel time, overall delay and average speed are analyzed. The uncertainty in freight flow and its impact on providing the passenger service is addressed by conducting a sensitivity analysis. The conclusion is that the existing railroad infrastructure is sufficient to provide a passenger train service from Las Vegas to Los Angeles. From an operational perspective, the passenger train is not expected to influence freight trains’ performance on the existing railroads. When freight train flows are increased to 50%, the influence of passenger train service on the freight operation is still minimal. This study recommends restoring a platform at the Las Vegas Station. At the Mojave Station, special care should be given on running the passenger trains where there is no direct railroad connection from BNSF to UPRR. Platforms and walkways require construction at the Lancaster Station for transferring passengers between the Metrolink trains and X-Train. Transferring the passenger train at this station involves stopping the train on mainline and coordinating the operations between different railroads.

Coupling analysis of passenger and train flows for a large-scale urban rail transit system

Coupling analysis of passenger and train flows is an important approach in evaluating and optimizing the operation efficiency of large-scale urban rail transit(URT)systems.This study proposes a passenger–train interaction simulation approach to determine the coupling relationship between passenger and train flows.On the bases of time-varying origin–destination demand,train timetable,and network topology,the proposed approach can restore passenger behaviors in URT systems.Upstream priority,queuing process with first-in-first-serve principle,and capacity constraints are considered in the proposed simulation mechanism.This approach can also obtain each passenger’s complete travel chain,which can be used to analyze(including but not limited to)various indicators discussed in this research to effectively support train schedule optimization and capacity evaluation for urban rail managers.Lastly,the proposed model and its potential application are demonstrated via numerical experiments using real-world data from the Beijing URT system(i.e.,rail network with the world’s highest passenger ridership).

Changing Trains

“Harmony” proves more than just a name for commuters on China＇s new high-speed train.

A strategy for out-of-roundness damage wheels identification in railway vehicles based on sparse autoencoders

Wayside monitoring is a promising cost-effective alternative to predict damage in the rolling stock. The main goal of this work is to present an unsupervised methodology to identify out-of-roundness(OOR) damage wheels, such as wheel flats and polygonal wheels. This automatic damage identification algorithm is based on the vertical acceleration evaluated on the rails using a virtual wayside monitoring system and involves the application of a two-step procedure. The first step aims to define a confidence boundary by using(healthy) measurements evaluated on the rail constituting a baseline. The second step of the procedure involves classifying damage of predefined scenarios with different levels of severities. The proposed procedure is based on a machine learning methodology and includes the following stages:(1) data collection,(2) damage-sensitive feature extraction from the acquired responses using a neural network model, i.e., the sparse autoencoder(SAE),(3) data fusion based on the Mahalanobis distance, and(4) unsupervised feature classification by implementing outlier and cluster analysis. This procedure considers baseline responses at different speeds and rail irregularities to train the SAE model. Then, the trained SAE is capable to reconstruct test responses(not trained) allowing to compute the accumulative difference between original and reconstructed signals. The results prove the efficiency of the proposed approach in identifying the two most common types of OOR in railway wheels.