Ping Huang

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Huang

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Ping

ORCID

0000-0002-2794-0188

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Publications 1 - 10 of 29

Modeling the influence of disturbances in high-speed railway systems
Huang, Ping; Wen, Chao; Peng, Qiyuan; et al. (2019)
Journal of Advanced Transportation
Accurately forecasting the influence of disturbances in High-Speed Railways (HSR) has great significance for improving real-time train dispatching and operation management. In this paper, we show how to use historical train operation records to estimate the influence of high-speed train disturbances (HSTD), including the number of affected trains (NAT) and total delayed time (TDT), considering the timetable and disturbance characteristics. We first extracted data about the disturbances and their affected train groups from historical train operation records of Wuhan-Guangzhou (W-G) HSR in China. Then, in order to recognize the concatenations and differences of disturbances, we used a K-Means clustering algorithm to classify them into four categories. Next, parametric and nonparametric density estimation approaches were applied to fit the distributions of NAT and TDT of each clustered category, and the goodness-of-fit testing results showed that Log-normal and Gamma distribution probability densities are the best functions to approximate the distribution of NAT and TDT of different disturbance clusters. Specifically, the validation results show that the proposed models accurately revealed the characteristics of HSTD and that these models can be used in real-time dispatch to predict the NAT and TDT, once the basic features of disturbances are known.
OORNet: A deep learning model for on-board condition monitoring and fault diagnosis of out-of-round wheels of high-speed trains
Ye, Yunguang; Zhu, Bin; Huang, Ping; et al. (2022)
Measurement
The problem of train wheel out-of-roundness (OOR) negatively affects both humans and the vehicle-track system, incl. reduced passenger comfort, rapid aging of vehicle/track components, increase in derailment risk, etc. It is therefore of interest to develop an on-board condition monitoring and fault diagnosis (CM&FD) technique for wheel OOR, which contributes not only to the maintenance decision-making of wheelsets but also to clarifying its triggering and evolution mechanisms. This paper first shows how to express the problem of CM&FD of our-of-round wheels as a machine learning problem. A deep learning model, OORNet, is then developed for CM&FD of out-of-round wheels. A vehicle-track multi-body dynamics model of a China railway high-speed (CRH) trailer is meanwhile built to produce a database consisting of vertical axlebox vibration accelerations caused by 2000 different wheel OOR curves. The simulated database is finally used to test the performance of OORNet, and its feasibility and superiority are verified.
Modeling train timetables as images
Huang, Ping; Li, Zhongcan; Wen, Chao; et al. (2021)
Expert Systems with Applications
As a vital component of train operational control, train delay propagation pattern discovery is critically important for both railway controllers and passengers. In this study, we present a carefully designed deep learning model, called FCF-Net, that comprises fully connected neural networks (FCNN) and convolutional neural networks (CNN) for train delay propagation pattern recognition in railway systems. FCF-Net first uses a CNN component that handles train timetables as images to capture interactions of train events and an FCNN component to capture the influence of non-operational features separately; then it uses another FCNN component to combinedly learn the dependencies between operational and non-operational features. In addition, considering the imbalance of train delay data, a cost-sensitive technique that assigns different misclassification costs for different class was used to better deal with the imbalanced data. The main goal of the FCF-Net is to realize efficient and accurate train delay propagation pattern recognition by mining potential knowledge from train operation data. The predictive and computational performance of the model was tested and evaluated on data from two high-speed railway lines with different operational features in China. The results show that FCF-Net, once trained with sufficient data, outperforms conventional deep learning with common loss and other state-of-the-art deep learning models for train delay propagation pattern recognition, indicating its capability in knowledge discovery from train operation data. In addition, the computational results show that FCF-Net exhibits more efficient training process than existing state-of-the-art deep learning models.
A Bayesian network model to predict the effects of interruptions on train operations
Huang, Ping; Lessan, Javad; Wen, Chao; et al. (2020)
Transportation Research Part C: Emerging Technologies
Based on the Bayesian network (BN) paradigm, we propose a hybrid model to predict the three main consequences of disruptions and disturbances during train operations, namely, the primary delay (L), the number of affected trains (N), and the total delay times (T). To obtain an effective BN structure, we first analyze the dependencies of the involved factors on each station and among adjacent stations, given domain knowledge and expertise about operational characteristics. We then put forward four candidate BN structures, integrating expert knowledge, the interdependencies learned from real-world data, and real-time prediction and operational requirements. Next, we train the candidate structures based on a 5-fold cross-validation method, using the operational data from Wuhan-Guangzhou (W-G) and Xiamen-Shenzhen (X-S) high-speed railway (HSR) lines in China. The best performing structure is nominated to predict the consequences of disruptions and disturbances in the two HSR lines. Comparisons results show that the proposed model outperforms three other commonly used predictive models, reaching an average prediction accuracy of 96.6%, 74.8%, and 91.0% on the W-G HSR line, and 94.8%, 91.1%, and 87.9% on the X-S HSR line for variables L, N, and T, respectively.
A data-driven time supplements allocation model for train operations on high-speed railways
Huang, Ping; Wen, Chao; Peng, Qiyuan; et al. (2019)
International Journal of Rail Transportation
This paper presents a time supplements allocation (TSA) method that incorporates historical train operation data to optimize buffer-time distribution in the sections and stations of a published timetable. First, delay recovery behavior is investigated and key influential factors are identified using real-world train movement records from the Wuhan–Guangzhou High-speed Railway (WH-GZ HSR) in China. Then, a ridge regression model is proposed that explains delay recovery time (RT) regarding buffer times at station (BTA), buffer times in section (BTE), and the severity of the primary delay (PD). Next, a TSA model is presented that takes the quantitative effects of identified factors as input to optimize time supplements locally. The presented model is applied to a case study comparing the existing and optimized timetables of 24 trains operating during peak morning hours. Results indicate an average 12.9% improvement in delay recovery measures of these trains.
Deep learning-based fault diagnostic network of high-speed train secondary suspension systems for immunity to track irregularities and wheel wear
Ye, Yunguang; Huang, Ping; Zhang, Yongxiang (2022)
Railway Engineering Science
Fault detection and isolation of high-speed train suspension systems is of critical importance to guarantee train running safety. Firstly, the existing methods concerning fault detection or isolation of train suspension systems are briefly reviewed and divided into two categories, i.e., model-based and data-driven approaches. The advantages and disadvantages of these two categories of approaches are briefly summarized. Secondly, a 1D convolution network-based fault diagnostic method for high-speed train suspension systems is designed. To improve the robustness of the method, a Gaussian white noise strategy (GWN-strategy) for immunity to track irregularities and an edge sample training strategy (EST-strategy) for immunity to wheel wear are proposed. The whole network is called GWN-EST-1DCNN method. Thirdly, to show the performance of this method, a multibody dynamics simulation model of a high-speed train is built to generate the lateral acceleration of a bogie frame corresponding to different track irregularities, wheel profiles, and secondary suspension faults. The simulated signals are then inputted into the diagnostic network, and the results show the correctness and superiority of the GWN-EST-1DCNN method. Finally, the 1DCNN method is further validated using tracking data of a CRH3 train running on a high-speed railway line.
Statistical modeling of the distribution characteristics of high-speed railway disruptions
Huang, Ping (2019)
Linköping Electronic Conference Proceedings ~ RailNorrköping 2019. 8th International Conference on Railway Operations Modelling and Analysis (ICROMA)
Studies on the spatiotemporal distribution and duration characteristics of railway disruptions are very significant for the advanced prediction of disruption and development of real-time dispatch strategies. In this study, historical disruption records of some Chinese High-Speed Railways (HSRs) lines from 2014–2016 were used to investigate the distribution characteristics of railway disruptions. The spatiotemporal probability distribution of four railway lines were calculated and their hotspots (coordinates with high probabilities) and coldspots (coordinates with low probabilities) were revealed using heatmaps. Furthermore, all the disruptions were classified into seven clusters based on their causes, and statistical analysis was carried out on each cluster. In addition, three right-skewed distribution models, namely Log-normal, Weibull, and Gamma distributions, were used to fit the duration of each cluster to uncover its duration regularities. Finally, goodness-of-fit test was performed on the models using the Kolmogorov-Smirnov method, indicating that the duration of each classified disruption can be estimated using a Log-normal distribution function. The obtained spatiotemporal probabilities and duration time distribution models thus can be further applied into estimating the occurrence and duration of railway disruption in real-time dispatching to help dispatchers make advanced decisions.
Railway network delay evolution: A heterogeneous graph neural network approach
Li, Zhongcan; Huang, Ping; Wen, Chao; et al. (2024)
Applied Soft Computing
Accurate delay evolution prediction plays a pivotal role in train rescheduling decision-making for the railway network. Existing studies on delay prediction predominantly centered around predicting delays for each train in the subsequent stations (i.e., following a train-oriented perspective). Furthermore, train operations in the railway network involve different types of entities (stations, trains, etc.), making the current graph/network models with homogenous nodes (i.e., the same kind of nodes) incapable of effectively capturing the interactions between the entities. This paper develops a network-oriented model to investigate the train delay evolution on railway networks, by predicting the delays of running trains in the network after a given time interval. The proposed model combines the GraphSAGE graph neural network (GNN) and the heterogeneous graph neural network (HetGNN) architecture, thus called SAGE-Het, enabling it to capture interactions between heterogeneous nodes (i.e., different types of nodes) based on different edges (e.g., edges between trains, trains, and stations). Additionally, SAGE-Het allows for flexible inputs in contrast to conventional machine learning techniques, whose inputs must meet the consistent dimension requirement (e.g., in the form of rectangular or grid-like arrays). The performance and robustness of the suggested SAGE-Het model are assessed in experiments on the data from two sub-networks of the China railway network. The experimental results demonstrate that SAGE-Het outperforms the existing delay prediction methods and some advanced HetGNNs used for prediction tasks in other domains; SAGE-Het demonstrates excellent scalability, capable of handling various types of nodes; the predictive performances of SAGE-Het under different prediction time horizons (10/20/30 min ahead) all exhibit better performance over other baselines; the accuracies are over 90 % under the permissible 3-minute errors for the three prediction time horizons. Specifically, the impact of train interactions on delay evolution is investigated based on the flexible input characteristic of the proposed model. The results illustrate that train interactions become subtle with the increase of train headways. This finding directly contributes to decision-making in situations where conflict resolution or train-canceling actions are needed.
Train traffic control in merging stations: A data-driven approach
Huang, Ping; Li, Zhongcan; Zhu, Yongqiu; et al. (2023)
Transportation Research Part C: Emerging Technologies
Railway operations are subject to deviations from the planned schedule, i.e., delays. In those situations, high-quality traffic control actions are needed to reduce the delays. Existing studies mainly used prescriptive techniques (e.g., mathematical programming, heuristics) to identify the best control action. These methods have limitations in the firm reliance on deterministic parameters prescriptively or normatively determined beforehand, and little understandability by the practitioners. These drawbacks hinder their acceptance in practice. This study exploits instead past realization data to provide decision support for traffic control. The realized data describe the traffic control actions taken by human controllers, and their effects; those latter are more complex than a linear sum of predetermined parameters. We use decision graphs to identify which traffic control action leads to the best solution, in terms of reduction of delays, based on the past performance of the same action in similar conditions. We are also able to explain the reasons and the factors that lead to each suggested action. We focus on the relevant case of merging stations, where multiple lines merge as one line, deciding the relative order between two consecutive trains. The method determines the stochastic effects of the two possible decisions at merge points, which allows for choosing the best one. Compared within the framework of realized data, the action suggested is the best out of a series of benchmarks, including simple rules and optimization, improving (reducing delays) approximately 11.7% on the common benchmarks. The variables with the highest impact on the utility are the length of the planned dwell time and the planned presence of an overtaking. The variables influencing the utility most are the actual delays of trains, the train type, and the order actually implemented.
Train operation conflict detection for high-speed railways: a naive Bayes approach
Li, Jie; Li, Zhongcan; Wen, Chao; et al. (2023)
International Journal of Rail Transportation
Accurately detecting train operation conflicts (TOC) has great significance for improving the emergency handling ability of dispatchers during interference. In this study, a conflict detection model for high-speed train operation is proposed, with the train operation data from Xiamen to Shenzhen high-speed railway. Firstly, a TOC detection model framework considering data imbalance is determined, based on Bernoulli naive Bayes model. Then, the hyper-parameter of the proposed model is tuned with the training and validation dataset. Next, the performance result of the proposed model is compared to other three commonly used naive Bayes models, namely the Gaussian naive Bayes, multinomial naive Bayes and complement naive Bayes. Comparison analyses based on the commonly used classification model evaluation indexes show that the detection accuracy of the proposed model is significantly higher than other naive Bayes models. The proposed model also achieves high robustness and detection accuracy in each category.

Publications 1 - 10 of 29

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