Purpose-To optimize train operations,dispatchers currently rely on experience for quick adjustments when delays occur.However,delay predictions often involve imprecise shifts based on known delay times.Real-time and a...Purpose-To optimize train operations,dispatchers currently rely on experience for quick adjustments when delays occur.However,delay predictions often involve imprecise shifts based on known delay times.Real-time and accurate train delay predictions,facilitated by data-driven neural network models,can significantly reduce dispatcher stress and improve adjustment plans.Leveraging current train operation data,these models enable swift and precise predictions,addressing challenges posed by train delays in high-speed rail networks during unforeseen events.Design/methodology/approach-This paper proposes CBLA-net,a neural network architecture for predicting late arrival times.It combines CNN,Bi-LSTM,and attention mechanisms to extract features,handle time series data,and enhance information utilization.Trained on operational data from the Beijing-Tianjin line,it predicts the late arrival time of a target train at the next station using multidimensional input data from the target and preceding trains.Findings-This study evaluates our model’s predictive performance using two data approaches:one considering full data and another focusing only on late arrivals.Results show precise and rapid predictions.Training with full data achieves aMAEof approximately 0.54 minutes and a RMSEof 0.65 minutes,surpassing the model trained solely on delay data(MAE:is about 1.02 min,RMSE:is about 1.52 min).Despite superior overall performance with full data,the model excels at predicting delays exceeding 15 minutes when trained exclusively on late arrivals.For enhanced adaptability to real-world train operations,training with full data is recommended.Originality/value-This paper introduces a novel neural network model,CBLA-net,for predicting train delay times.It innovatively compares and analyzes the model’s performance using both full data and delay data formats.Additionally,the evaluation of the network’s predictive capabilities considers different scenarios,providing a comprehensive demonstration of the model’s predictive performance.展开更多
Many real-world networks are demonstrated to either have layered network structures in themselves or interconnect with other networks,forming multilayer network structures.In this survey,we give a brief review of rece...Many real-world networks are demonstrated to either have layered network structures in themselves or interconnect with other networks,forming multilayer network structures.In this survey,we give a brief review of recent progress in traffic dynamics on multilayer networks.First,we introduce several typical multilayer network models.Then,we present some mainstream performance indicators,such as network capacity,average transmission time,etc.Moreover,we discuss some optimization strategies for improving the transmission performance.Finally,we provide some open issues that could be further explored in the future.展开更多
Many epidemics or viruses in real life spread by taking advantage of other dynamic processes, e.g., the computer virus propagates with the transmission of packets. In this paper, we survey the recent progress in the s...Many epidemics or viruses in real life spread by taking advantage of other dynamic processes, e.g., the computer virus propagates with the transmission of packets. In this paper, we survey the recent progress in the study of Traffic-Driven Epidemic Spreading (TDES) on complex networks. First, we introduce several typical TDES models. Then, we analyze the key factors which have significant impact on the epidemic threshold, such as the traffic congestion and routing protocols. Furthermore, we discuss the control of the TDES by focusing on the network structure optimization and the immunization strategies. Finally, we put some issues that need to be further explored in the future.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 62203468in part by the Technological Research and Development Program of China State Railway Group Co.,Ltd.under Grant Q2023X011+1 种基金in part by the Young Elite Scientist Sponsorship Program by China Association for Science and Technology(CAST)under Grant 2022QNRC001in part by the Youth Talent Program Supported by China Railway Society,and in part by the Research Program of China Academy of Railway Sciences Corporation Limited under Grant 2023YJ112.
文摘Purpose-To optimize train operations,dispatchers currently rely on experience for quick adjustments when delays occur.However,delay predictions often involve imprecise shifts based on known delay times.Real-time and accurate train delay predictions,facilitated by data-driven neural network models,can significantly reduce dispatcher stress and improve adjustment plans.Leveraging current train operation data,these models enable swift and precise predictions,addressing challenges posed by train delays in high-speed rail networks during unforeseen events.Design/methodology/approach-This paper proposes CBLA-net,a neural network architecture for predicting late arrival times.It combines CNN,Bi-LSTM,and attention mechanisms to extract features,handle time series data,and enhance information utilization.Trained on operational data from the Beijing-Tianjin line,it predicts the late arrival time of a target train at the next station using multidimensional input data from the target and preceding trains.Findings-This study evaluates our model’s predictive performance using two data approaches:one considering full data and another focusing only on late arrivals.Results show precise and rapid predictions.Training with full data achieves aMAEof approximately 0.54 minutes and a RMSEof 0.65 minutes,surpassing the model trained solely on delay data(MAE:is about 1.02 min,RMSE:is about 1.52 min).Despite superior overall performance with full data,the model excels at predicting delays exceeding 15 minutes when trained exclusively on late arrivals.For enhanced adaptability to real-world train operations,training with full data is recommended.Originality/value-This paper introduces a novel neural network model,CBLA-net,for predicting train delay times.It innovatively compares and analyzes the model’s performance using both full data and delay data formats.Additionally,the evaluation of the network’s predictive capabilities considers different scenarios,providing a comprehensive demonstration of the model’s predictive performance.
基金the National Natural Science Foundation of China(Grant No.61304154).
文摘Many real-world networks are demonstrated to either have layered network structures in themselves or interconnect with other networks,forming multilayer network structures.In this survey,we give a brief review of recent progress in traffic dynamics on multilayer networks.First,we introduce several typical multilayer network models.Then,we present some mainstream performance indicators,such as network capacity,average transmission time,etc.Moreover,we discuss some optimization strategies for improving the transmission performance.Finally,we provide some open issues that could be further explored in the future.
基金the National Natural Science Foundation of China (Grant No. 61304154).
文摘Many epidemics or viruses in real life spread by taking advantage of other dynamic processes, e.g., the computer virus propagates with the transmission of packets. In this paper, we survey the recent progress in the study of Traffic-Driven Epidemic Spreading (TDES) on complex networks. First, we introduce several typical TDES models. Then, we analyze the key factors which have significant impact on the epidemic threshold, such as the traffic congestion and routing protocols. Furthermore, we discuss the control of the TDES by focusing on the network structure optimization and the immunization strategies. Finally, we put some issues that need to be further explored in the future.