Short-term train arrival delay prediction:a data-driven approach

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.

Risk assessment of high-speed railway CTC system based on improved game theory and cloud model

Purpose-In order to solve the problem of inaccurate calculation of index weights,subjectivity and uncertainty of index assessment in the risk assessment process,this study aims to propose a scientific and reasonable centralized traffic control(CTC)system risk assessment method.Design/methodologylapproach-First,system-theoretic process analysis(STPA)is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis.Then,to enhance the accuracy of weight calculation,the fuzzy analytical hierarchy process(FAHP),fuzzy decision-making trial and evaluation laboratory(FDEMATEL)and entropy weight method are employed to calculate the subjective weight,relative weight and objective weight of each index.These three types of weights are combined using game theory to obtain the combined weight for each index.To reduce subjectivity and uncertainty in the assessment process,the backward cloud generator method is utilized to obtain the numerical character(NC)of the cloud model for each index.The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system.This cloud model is used to obtain the CTC system's comprehensive risk assessment.The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud.Finally,this process yields the risk assessment results for the CTC system.Findings-The cloud model can handle the subjectivity and fuzziness in the risk assessment process well.The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.Originality/value-This study provides a cloud model-based method for risk assessment of CTC systems,which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment,achieving effective risk assessment of CTC systems.It can provide a reference and theoretical basis for risk management of the CTC system.

Key technologies and applications of intelligent dispatching command for high-speed railway in China

Purpose–The intelligent Central Traffic Control(CTC)system plays a vital role in establishing an intelligent high-speed railway(HSR)system.As the core of HSR transportation command,the intelligent CTC system is a new HSR dispatching command system that integrates the widely used CTC in China with the practical service requirements of intelligent dispatching.This paper aims to propose key technologies and applications for intelligent dispatching command in HSR in China.Design/methodology/approach–This paper first briefly introduces the functions and configuration of the intelligent CTC system.Some new servers,terminals and interfaces are introduced,which are plan adjustment server/terminal,interface for automatic train operation(ATO),interface for Dynamic Monitoring System of Train Control Equipment(DMS),interface for Power Supervisory Control and Data Acquisition(PSCADA),interface for Disaster Monitoring,etc.Findings–The key technologies applied in the intelligent CTC system include automatic adjustment of train operation plans,safety control of train routes and commands,traffic information data platform,integrated simulation of traffic dispatching and ATO function.These technologies have been applied in the Beijing-Zhangjiakou HSR,which commenced operations at the end of 2019.Implementing these key intelligent functions has improved the train dispatching command capacity,ensured the safe operation of intelligent HSR,reduced the labor intensity of dispatching operators and enhanced the intelligence level of China’s dispatching system.Originality/value–This paper provides further challenges and research directions for the intelligent dispatching command of HSR.To achieve the objectives,new measures need to be conducted,including the development of advanced technologies for intelligent dispatching command,coping with new requirements with the development of China’s railway signaling system,the integration of traffic dispatching and train control and the application of AI and data-driven modeling and methods.

A train timetable rescheduling approach based on multi-train tracking optimization of high-speed railways

Purpose–This paper aims to propose a train timetable rescheduling(TTR)approach from the perspective of multi-train tracking optimization based on the mutual spatiotemporal information in the high-speed railway signaling system.Design/methodology/approach–Firstly,a single-train trajectory optimization(STTO)model is constructed based on train dynamics and operating conditions.The train kinematics parameters,including acceleration,speed and time at each position,are calculated to predict the arrival times in the train timetable.A STTO algorithm is developed to optimize a single-train time-efficient driving strategy.Then,a TTR approach based on multi-train tracking optimization(TTR-MTTO)is proposed with mutual information.The constraints of temporary speed restriction(TSR)and end of authority are decoupled to calculate the tracking trajectory of the backward tracking train.The multi-train trajectories at each position are optimized to generate a timeefficient train timetable.Findings–The numerical experiment is performed on the Beijing-Tianjin high-speed railway line and CR400AF.The STTO algorithm predicts the train’s planned arrival time to calculate the total train delay(TTD).As for the TSR scenario,the proposed TTR-MTTO can reduce TTD by 60.60%compared with the traditional TTR approach with dispatchers’experience.Moreover,TTR-MTTO can optimize a time-efficient train timetable to help dispatchers reschedule trains more reasonably.Originality/value–With the cooperative relationship and mutual information between train rescheduling and control,the proposed TTR-MTTO approach can automatically generate a time-efficient train timetable to reduce the total train delay and the work intensity of dispatchers.

车站封锁下基于问题知识的高速铁路列车运行实时调整方法

针对突发事件导致的车站封锁情况,本文以列车运行图为问题对象,以进化计算框架为基础,提出基于问题知识的运行图实时调整方法,通过减小列车总晚点时间,保证高铁运营的安全高效和旅客的满意舒适.首先,基于调整列车发车次序的运行图调整策略提出排列编码方法,用于减少解空间的无效搜索.之后,根据“紧追踪”的列车运行追踪方式,设计启发式解码方法消除所有行车作业约束,提升算法求解效率.最后,将调度员调整运行图的经验作为问题知识,用于初始化进化计算的初始种群,由此提出基于问题知识的启发式种群初始化方法,加快算法前期的收敛速度并提高求解方案质量.以京津高速线为例,在北京南站设置车站封锁下20~150 min不同封锁时长的9个典型场景,选择加强精英保留遗传算法和差分进化算法,分别应用实整数编码和排列编码,与随机种群初始化和启发式种群初始化的不同组合进行仿真实验.仿真结果表明,相较于实整数编码难以获取可行解,2种进化算法应用排列编码方法后,能在9 s的平均时间内给出列车总晚点时间最小的调整方案.在启发式种群初始化的改进下,2种进化算法能更快地收敛于近似最优解.选取加强精英保留遗传算法应用排列编码和启发式种群初始化的改进变体,作为本文最优改进进化算法.针对CPLEX无法在10 min获得最优解的7个场景,该改进进化算法都能在20 s内给出近似最优解.

Dynamic grouping of heterogeneous agents for exploration and strike missions

The ever-changing environment and complex combat missions create new demands for the formation of mission groups of unmanned combat agents.This study aims to address the problem of dynamic construction of mission groups under new requirements.Agents are heterogeneous,and a group formation method must dynamically form new groups in circumstances where missions are constantly being explored.In our method,a group formation strategy that combines heuristic rules and response threshold models is proposed to dynamically adjust the members of the mission group and adapt to the needs of new missions.The degree of matching between the mission requirements and the group’s capabilities,and the communication cost of group formation are used as indicators to evaluate the quality of the group.The response threshold method and the ant colony algorithm are selected as the comparison algorithms in the simulations.The results show that the grouping scheme obtained by the proposed method is superior to those of the comparison methods.