Train delay prediction can improve the quality of train dispatching, which helps the dispatcher to estimate the running state of the train more accurately and make reasonable dispatching decision. The delay of one train is affected by many factors, such as passenger flow, fault, extreme weather, dispatching strategy. The departure time of one train is generally determined by dispatchers, which is limited by their strategy and knowledge. The existing train delay prediction methods cannot comprehensively consider the temporal and spatial dependence between the multiple trains and routes. In this paper, we don't try to predict the specific delay time of one train, but predict the collective cumulative effect of train delay over a certain period, which is represented by the total number of arrival delays in one station. We propose a deep learning framework, train spatio-temporal graph convolutional network (TSTGCN), to predict the collective cumulative effect of train delay in one station for train dispatching and emergency plans. The proposed model is mainly composed of the recent, daily and weekly components. Each component contains two parts: spatio-temporal attention mechanism and spatio-temporal convolution, which can effectively capture spatio-temporal characteristics. The weighted fusion of the three components produces the final prediction result. The experiments on the train operation data from China Railway Passenger Ticket System demonstrate that TSTGCN clearly outperforms the existing advanced baselines in train delay prediction.

High-speed train delay prediction has always been one of the important research issues in the railway dispatching. Accurate and interpretable delay prediction can enable staff to implement preventive measures and scheduling decisions in advance, and guide relevant departments to cooperate in completing complex transportation tasks, so as to improve rail transit operations, service quality, and the efficiency of train operation. This article proposes a new interpretable model based on graph community neural network and time-series fuzzy decision tree. This model can well capture the influence of spatiotemporal characteristics, train community structure, and multifactor in high-speed train station delay prediction. Besides, the time series fuzzy decision tree based on multiobjective optimization and reduced error pruning can mine potential decision rules to improve the model's interpretability, transparency, and high reliability. Finally, we prove that the prediction effect of the proposed model is superior than the other seven state-of-the-art models and our model is interpretable.

High-speed train operation data are reliable and rich resources in data-driven research. However, the data released by railway companies are poorly organized and not comprehensive enough to be applied directly and effectively. A public high-speed railway network dataset suitable for research is still lacking. To support the research in large-scale complex network, complex dynamic system and intelligent transportation, we develop a high-speed railway network dataset, containing the train operation data in different directions from October 8, 2019 to January 27, 2020, the train delay data of the railway stations, the junction stations data, and the mileage data of adjacent stations. In the dataset, weather, temperature, wind power and major holidays are considered as factors affecting train operation. Potential research values of the dataset include but are not limited to complex dynamic system pattern mining, community detection and discovery, and train delay analysis. Besides, the dataset can be used to solve various railway operation and management problems, such as passenger service network improvement, train real-time dispatching and intelligent driving assistance.

Train station delay prediction is always one of the core research issues in high-speed railway dispatching. Reliable prediction of station delay can help dispatchers to accurately estimate the train operation status and make reasonable dispatching decisions to improve the operation and service quality of rail transit. The delay of one station is affected by many factors, such as spatiotemporal factor, speed limitation or suspension caused by strong wind or bad weather, and high passenger flow caused by major holiday. But previous studies have not fully combined the spatiotemporal characteristics of station delay and the impact of external factors. This paper makes good use of the train operation data, proposes the multiattention mechanism to capture the spatiotemporal characteristics of train operation data and process the external factors, and establishes a Multiattention Train Station Delay Graph Convolution Network (MATGCN) model to predict the train delay at high-speed railway stations, so as to provide references for train dispatching and emergency plan. This paper uses real train operation data coming from China high-speed railway network to prove that our model is superior to ANN, SVR, LSTM, RF, and TSTGCN models in the prediction effect of MAE, RMSE, and MAPE.

With the increasing traffic of train communication network (TCN), real-time Ethernet becomes the development trend. However, Train Control and Management System (TCMS) is inevitably faced with more security threats than before because of the openness of Ethernet communication protocol. It is necessary to introduce effective security mechanism into TCN. Therefore, we propose a train real-time Ethernet anomaly detection system (TREADS). TREADS introduces a multiple streams clustering algorithm to realize anomaly detection, which considers the correlation between the data dimensions and adopts the decay window to pay more attention to the recent data. In the experiment, the reliability of TREADS is tested based on the TRDP data set collected from the real network environment, and the models of anomaly detection algorithms are established for evaluation. Experimental results show that TREADS can provide a high reliability guarantee, besides, the algorithm can detect and analyze network anomalies more efficiently and accurately.

During the COVID-19 epidemic, the online prescription pattern of Internet healthcare provides guarantee for the patients with chronic diseases and reduces the risk of cross-infection, but it also raises the burden of decision-making for doctors. Online drug recommendation system can effectively assist doctors by analysing the electronic medical records (EMR) of patients. Unlike commercial recommendations, the accuracy of drug recommendations should be very high due to their relevance to patient health. Besides, concept drift may occur in the drug treatment data streams, handling drift and location drift causes is critical to the accuracy and reliability of the recommended results. This paper proposes a multi-model fusion online drug recommendation system based on the association of drug and pathological features with online-nearline-offline architecture. The system transforms drug recommendation into pattern classification and adopts interpretable concept drift detection and adaptive ensemble classification algorithms. We apply the system to the Percutaneous Coronary Intervention (PCI) treatment process. The experiment results show our system performs nearly as good as doctors, the accuracy is close to 100%

<p>Traffic forecasting is one of the core issues in transportation systems. Graph convolution based spatiotemporal model can process the complex and highly nonlinear traffic data, but rely heavily on the graph construction, some creative works make innovations in dynamic graph modeling and expansion of the graph. However, these works ignored the dynamic community structure of traffic graph and the inter-community traffic flow interaction, and not fully exploited the spatiotemporal characteristics. In this paper, we propose a dynamic community graph convolution model (DCGCN), which can more precisely characterize the dynamic traffic network and capture the dynamic spatiotemporal dependencies by capturing the community properties and community interactions of traffic flow. First, we propose a noval method to capture inter-community traffic flow interactions. Second, we take advantage of the spatiotemporal properties of traffic data and introduce a community-based dynamic traffic graph learning mechanism to construct traffic networks adaptively. Finally, we fuse inter-community traffic flow interactions and dynamic traffic graph to update the traffic graph, introduce a multi-attention spatiotemporal convolution for traffic forecasting. We conduct experiments on three large-scale real datasets containing multiple traffic scenarios, prove the superiority of our model compared with SOTA models such as ASTGCN, MATGCN, and DCRNN. Meanwhile, the visualization in these datasets shows the method can effectively identify the stations that have a large impact in the forecasting process and dynamic community changes, and the extracted inter-station dependencies and traffic flow region interactions are interpretable. The source code is available  athttps://github.com/1998XuYi/DCGCN.</p>

<p>Traffic forecasting is one of the core issues in transportation systems. Graph convolution based spatiotemporal model can process the complex and highly nonlinear traffic data, but rely heavily on the graph construction, some creative works make innovations in dynamic graph modeling and expansion of the graph. However, these works ignored the dynamic community structure of traffic graph and the inter-community traffic flow interaction, and not fully exploited the spatiotemporal characteristics. In this paper, we propose a dynamic community graph convolution model (DCGCN), which can more precisely characterize the dynamic traffic network and capture the dynamic spatiotemporal dependencies by capturing the community properties and community interactions of traffic flow. First, we propose a noval method to capture inter-community traffic flow interactions. Second, we take advantage of the spatiotemporal properties of traffic data and introduce a community-based dynamic traffic graph learning mechanism to construct traffic networks adaptively. Finally, we fuse inter-community traffic flow interactions and dynamic traffic graph to update the traffic graph, introduce a multi-attention spatiotemporal convolution for traffic forecasting. We conduct experiments on three large-scale real datasets containing multiple traffic scenarios, prove the superiority of our model compared with SOTA models such as ASTGCN, MATGCN, and DCRNN. Meanwhile, the visualization in these datasets shows the method can effectively identify the stations that have a large impact in the forecasting process and dynamic community changes, and the extracted inter-station dependencies and traffic flow region interactions are interpretable. The source code is available  athttps://github.com/1998XuYi/DCGCN.</p>