Abstract:To investigate the decision-making mechanisms of stranded passengers'travel mode choice during unexpected metro service disruptions and to clarify the influence of key situational factors,this study constructs a baseline Multinomial Logit model incorporating mode-specific attributes,based on scenario simulations and Stated Preference(SP)surveys.A stepwise utility-correction approach is ap-plied to quantify both the impact intensity and patterns of influence exerted by factors such as expected recovery time,weather conditions,travel time and purpose,remaining trip distance,crowd behavior,and the availability of alternative routes.Model improvements are evaluated using the Likelihood Ratio Test(LRT),Akaike Information Criterion(AIC),Bayesian Information Criterion(BIC),and predic-tion error rates.The research results indicate that passenger decision-making demonstrates strong sce-nario dependence and a pronounced risk-averse tendency.The effects of situational factors vary consid-erably,with expected recovery time having the greatest impact(LR=183.98,prediction error reduced by 1.94%),followed by weather conditions(LR=102.63,error reduced by 1.16%).Trip purpose and remaining journey distance also show notable influence,while crowd behavior and alternative-route availability do not exhibit significant effects.Preferences shifts across public transport modes display similar patterns of change.however,waiting on-site,though frequently chosen as a passive option,maintains a considerable selection rate but declines significantly when systemic and situational risks overlap.Sensitivity to scenario factors differs markedly across modes,with those offering higher cer-tainty and lower risk proving more attractive in disruption scenarios.

Abstract:China has built and operated the world's largest high-speed railway and urban rail networks.With the continuous expansion of network scale and the growing complexity of operating environ-ments,existing automated train operation control systems face diverse challenges in improving effi-ciency and adaptability,and are increasingly unable to meet the requirements of safe and efficient op-erations under high-density traffic and dynamic conditions.Autonomous operation control for railway transportation,which integrates perception,low-latency and high-reliability communication,autono-mous train control,and intelligent scheduling,is expected to enable safe and efficient train operations in complex environments,thereby facilitating the intelligent development of rail transit.This paper provides a comprehensive review of autonomous operation control technologies for railway transporta-tion.It first summarizes domestic and international research progress and practical applications in this field,clarifies the connotation of autonomy,compares the concepts of autonomy in maritime,road,and railway transportation,and systematically outlines the key technology framework composed of inte-grated perception,low-latency and high-reliability communication,autonomous train control in complex environments,and intelligent train scheduling.The paper then analyzes the application prospects of these key technologies in the rail transit domain and discusses practical implementations through representative case studies.Finally,it identifies the bottlenecks and challenges in real-world deployment and explores future development directions.This paper aims to provide systematic reference and support for both theo-retical research and engineering practice in autonomous operation control of railway transportation.

Abstract:Visual intelligence,as a core branch of AI,seeks to endow machines with human-like capa-bilities for visual understanding and interaction.Since the breakthrough of deep learning in computer vi-sion in 2012,the field has undergone four progressive stages of evolution.The first stage,represented by AlexNet,VGGNet and ResNet,leveraged large annotated datasets such as ImageNet to achieve remarkable success in closed-domain tasks(e.g.,image classification and object detection),but its de-pendence on labeled data highlighted inherent limitations.The second stage witnessed the rise of self-supervised learning,with models such as MoCo,DION and MAE learning powerful visual representa-tions from massive unlabeled data through contrastive,distillation,and masked reconstruction meth-ods.The third stage marked a shift toward multimodal intelligence,where models like CLIP and GPT-4V integrated vision and language,enabling open-vocabulary understanding and advancing to-ward fine-grained,intent-driven reasoning.The current frontier is world models,exemplified by Sora,which aim not only to perceive and describe but also to simulate and predict the physical world,paving the way for embodied intelligence capable of interacting with reality.The current frontier is world models,exemplified by Sora,which aim not only to perceive and describe but also to simulate and predict the physical world,paving the way for embodied intelligence capable of interacting with re-ality.This fundamental transformation from discriminative understanding to generative simulation of the world marks a new consensus:generative modeling is the new deep learning.This survey follows this developmental trajectory,analyzing the core ideas,representative models,and methodological paradigms at each stage,while highlighting ongoing challenges in robustness,reasoning,and general-ization.This survey follows this developmental trajectory,analyzing the core ideas,representative models,and methodological paradigms at each stage,while highlighting ongoing challenges in robust-ness,reasoning,and generalization.

Abstract:To address the challenges of precise governance arising from the spatiotemporal heterogene-ity and diverse patterns of pollutant and carbon emissions from road mobile sources in China,this study develops a machine learning-based analytical framework for emission characteristics,driving fac-tors,and mitigation pathways.First,a transport-carbon-environment dataset is constructed by inte-grating multi-source data to analyze the spatiotemporal distribution characteristics of emissions.Sec-ond,a knowledge-constrained non-negative matrix factorization model is developed to identify distinct emission patterns and their underlying drivers.Finally,a multi-pattern integrated multiple linear re-gression model is applied to evaluate effective pathways for pollution and carbon reduction.The results indicate that in recent years,particulate matter emissions from road mobile sources have shown a de-creasing trend,while carbon dioxide emissions continue to increase,with emissions of NOx,CO,and VOCs remaining substantial.Three primary emission patterns are identified across China:a"CO2-PM Reduction Pattern,"distributed across border and coastal regions and driven mainly by road passenger and freight transport activities and vehicle mileage(37.2%);a"Pollutant Reduction Pattern,"found in municipalities like Beijing and Tianjin and provinces such as Guangdong,mainly influenced by the Gross Transport Product(30.1%);and a"CO2-NOx Pollution Pattern,"located in the central and western regions,affected by multiple factors including railway development and its electrification,as well as the use of light-duty transport vehicles(23.6%).Pathway analysis indicates that accelerating economic development in the transport sector has the most significant impact on reduction,potentially reducing CO2 and NOx by up to 6.7%and 4.5%,respectively.Other significant measures include re-structuring the transport energy system,reducing road freight and vehicle mileage,and promoting the shift from road to rail alongside railway electrification.The proposed framework provides a quantita-tive basis and strategic guidance for coordinated pollution and carbon reduction from road mobile sources in the context of China's dual-carbon goals of carbon peaking and carbon neutrality.

Abstract:Most existing quality evaluation methods for high-speed railway ballastless track construc-tion are established based on traditional construction processes.As ballastless track construction pro-gressively shifts towards intelligent construction,the existing evaluation methods can no longer adequately meet the requirements for quality evaluation of intelligent ballastless track construction.Taking the construction of the CRTS Ⅲ slab ballastless track as an example,this paper analyzes the key quality factors of intelligent construction processes,constructs an evaluation index system,and es-tablishes a comprehensive quality evaluation model for intelligent ballastless track construction based on the Analytic Hierarchy Process(AHP)and the matter-element extension evaluation theory.Field tests are carried out on an intelligent construction test section of a high-speed railway for verification.The results show that the model established in this paper can objectively evaluate the quality of intelli-gent ballastless track construction.Compared with the traditional process,the guided intelligent con-struction process achieves a 32%improvement in the excellent rate.For the intelligent construction of the CRTS Ⅲ slab ballastless track,the construction quality of the track slab is the most important,fol-lowed by the quality of the self-compacting concrete construction,while the construction quality of the base slab has the least impact.The research results can provide a basis for evaluating the quality of in-telligent construction of CRTS Ⅲ slab ballastless tracks for high-speed railways and offer a reference for the quality management of ballastless track construction in China's high-speed railways.

Abstract:To meet the stringent requirements for low latency,high reliability,and intelligence posed by emerging applications such as autonomous driving and the Industrial Internet,this study examines the limitations of traditional network architectures in heterogeneous resource integration,service adaptation,and intelligent decision-making.A smart computing integration network architecture based on a"three-layer,three-domain"framework is proposed,and its de-velopment prospects are discussed in relation to the deep integration of heterogeneous networks,computing resource scheduling,and network-native intelligence.The research results demon-strate that the proposed smart computing integration network architecture enables a paradigm shift from"passive connection"to"active service"through key technologies such as unified re-source representation,computing-network demand analysis,and agile resource scheduling,thereby achieving comprehensive coordination between computing and networking.The research findings provide theoretical references and technical pathways for the construction of intelligent,efficient,and reliable emerging network infrastructures.

Abstract:This study addresses lane-change obstacle avoidance for autonomous vehicles under sudden road hazards and proposes SafeLC-DelayDDPG,a vehicle control algorithm based on Deep Reinforce-ment Learning(DRL).The task is formulated as a Markov Decision Process(MDP),and a structured hybrid state space is constructed by integrating local observations,lane-level semantic information,and the ego vehicle's global states to enhance environmental perception and risk sensitivity.The ac-tion space consists of continuous front-wheel steering angle and longitudinal acceleration.The reward function is centered on a two-dimensional time-to-collision(2D-TTC)metric,balancing safety,efficiency,comfort,and traffic-rule compliance,and employs a TTC-conditioned dynamic weighting mechanism that prioritizes safety under high risk and efficiency under low risk.Furthermore,delayed policy updates and target policy smoothing are introduced,and the Critic network loss is refined to mitigate the training instability and Q-value overestimation issues inherent in Deep Deterministic Policy Gradient(DDPG).The proposed method is validated through traffic simulations across diverse scenarios.Experi-mental results show that,compared with multiple baseline algorithms,SafeLC-DelayDDPG achieves su-perior safety and efficiency:during training,the first-attempt and consecutive obstacle-avoidance success rates improve by up to 17.9%and 60.5%,respectively;the safety metric by up to 7.6%;and the average speed by up to 2.1%.In cross-scenario tests,the first-attempt and consecutive success rates improve by up to 13.3%and 44.1%,the safety metric by up to 9.8%,and the average speed by up to 0.6%.

Abstract:To investigate the variation patterns of the temperature field and thermal stress in seamless CRTS Ⅲ slab tracks on simply supported beam bridges under high-temperature conditions,an indi-rect heat-stress coupling analysis method is employed.A finite element model of the coupled structural system of the heat-ballastless track is established to analyze the distribution and evolution of the tempera-ture field,as well as the longitudinal force,stress,and displacement distribution between track structure layers under high temperatures.The results indicate that the temperature of each structural layer of the bal-lastless track exhibits a wave-like variation with ambient temperature.Over the course of a day,the maxi-mum and minimum temperatures,53.1℃and 26.4℃,respectively,occur at the top of the track slab.The amplitude of the temperature time-history curves decreases with increasing vertical depth of the track struc-ture,and the peak temperature shows a time lag.In summer high-temperature conditions,the temperature gradient of the track slab approaches zero around 11:00 and 21:00;the positive gradient peaks at 85.5℃/m at 15:00,while the negative gradient peaks at-43.8℃/m at 03:00.As the depth of the track structure in-creases,the temperature gradient gradually decreases.Under single-day high-temperature conditions,the rail longitudinal force and track structure displacement reach their maximum around 18:00 every day,repre-senting the most unfavorable state,while the longitudinal stress in the track slab and self-compacting con-crete layer peaks between 14:00 and 16:00.These findings provide a t theoretical reference for monitoring and maintenance of track structures in high temperature regions during summer.

Abstract:Current research on optimizing and controlling traction energy consumption in urban rail transit has largely focused on localized improvements within single systems,making it difficult to coordinate inter-system coupling and achieve global energy efficiency.To address this problem,this study proposes a multi-system collaborative optimization method that integrates power supply,vehicles,and opera-tions.By analyzing the circulation paths of traction energy consumption and the inter-dependencies among decision variables,a four-layer global optimization framework is developed.The first layer opti-mizes single-train driving curves according to specified interval running times,identifying energy-minimal trajectories under constraints such as punctuality,comfort,and speed limits.The second layer optimizes inter-station running times by adjusting train travel durations between adjacent stations,thereby minimizing overall line energy consumption while ensuring that the total turnaround time equals the given value.The third layer focuses on optimizing multi-train energy-efficient timetables.By adjust-ing departure intervals and dwell times,this layer reduces total traction energy consumption while balanc-ing passengers'average waiting times through multi-train coordination.The fourth layer addresses threshold optimization of energy storage and inverter devices.The results of the first three layers of the train scheduling and control optimization process are used to inform the adoption of a grid-voltage-based control strategy.Simulation studies are undertaken in actual line operations.Simulation results demon-strate that the proposed global optimization reduces system traction energy consumption by 13.24%,while increasing passengers'average waiting time by only 12 seconds.These results verify the effective-ness of the proposed coupled energy-saving optimization method and provide a theoretical foundation for the comprehensive optimization and control of traction energy consumption in urban rail transit.

Abstract:The transformation and upgrading of the railway transportation production system represent a core issue and critical challenge currently faced by railway transportation enterprises in their transition to-ward modern logistics enterprises.To support the construction of a modern logistics-oriented railway transportation production system,the study first analyzes key aspects including top-level design,theo-retical architecture,critical technologies,and implementation evaluation indicators.Subsequently,the key scientific and technological bottlenecks that need to be overcome in building the new system are dis-cussed,proposing a key technological architecture oriented to modern logistics.Finally,based on this ar-chitecture,a simulation prototype system for railway production organization capable of optimization,evaluation,and simulation is designed to validate the feasibility and effectiveness of the proposed theo-retical framework and technical pathways.The research demonstrates that this study offers certain theo-retical reference value and practical significance for enhancing the operational efficiency and service qual-ity of railway logistics,as well as promoting the transformation and development of transportation pro-duction.Additionally,the developed prototype system can provide a reusable development paradigm and decision-support tool for subsequent system optimization and engineering applications.

Abstract:To address the issues of low detection accuracy,slow detection speed,and frequent missed or false detections in railway track foreign object intrusion detection,this study proposes a lightweight railway track foreign object intrusion detection algorithm based on shallow feature fusion(YOLO-LSF).First,building on the YOLOv8n feature extraction network,the C2f module is improved based on GhostConv to construct the C2f_Ghost module,thereby reducing both the parameter count and computational cost of the model.Second,the MLCA attention mechanism is introduced at the end of the backbone network to en-hance the feature representation of the target area and optimize the feature extraction efficiency of the model.Third,deformable convolution DCNv2 is employed to replace some ordinary convolutions in the C2f module of YOLOv8n,constructing the C2f_DCNv2 module and further strengthening the model's feature extraction capacity.Finally,shallow feature information from the backbone network is integrated into the neck network,effectivelt mitigating detail loss caused by multiple convolution operations and en-hancing the model′s ability to detect distant foreign objects(small targets).Experimental results show that on a self-constructed railway track foreign object intrusion detection dataset,compared with the original YOLOv8n algorithm,the YOLO-LSF algorithm achieves an improvement of 5.2%in average precision,3.37%in FPS,a reduction of 20.1%in the number of parameters,and a decrease of 22.2%in computa-tional complexity.These results verify that the proposed algorithm significantly enhances detection accu-racy and speed in complex environments while reducing the likelihood of missed and false detections.

Abstract:With the continuous expansion and technological upgrading of China's high-speed railway systems,the electromagnetic complexity of the operating environment has significantly increased.In particular,the frequent appearance of strong external electromagnetic interference sources has posed unprecedented challenges to the stable operation of the system.The traditional electromagnetic com-patibility(EMC)framework,which focuses primarily on improving device immunity,shows evident limitations when dealing with random and strong external electromagnetic interference,such as de-layed response and poor adaptability.These shortcomings make it difficult to meet the current high de-mands for system functional stability,mission continuity,and rapid recovery.To address these chal-lenges,this paper systematically reviews the current research progress in the field of EMC for high-speed railways in China,with a particular focus on key technical aspects such as interference source modeling,coupling path identification,disturbance immunity evaluation of critical equipment,and system-level protection strategies.On this basis,a new research framework for electromagnetic safety is proposed,with train control capability retention as the core objective.This framework aims to shift from"passive immunity"to"intelligent defense and autonomous recovery,"and it includes essential components such as intelligent interference identification and perception,multi-physical-field coupled network modeling,electromagnetic interference risk propagation mechanisms,active protection tech-nologies,and experimental validation methods.Research findings indicate that electromagnetic safety,as an extension of the traditional EMC system,represents a key pathway to ensuring the high safety and high reliability of high-speed railway systems under complex electromagnetic environments.The outcomes of this study can provide theoretical foundations and technical references for the future design and engineering implementation of electromagnetic protection in high-speed railway systems.

Abstract:For the problem of energy-efficient operation in urban rail transit,existing studies can be grouped into two main strands:Classical methods and technology-driven approaches.The classical strand reviews optimization of train speed profiles,train timetables,and their joint optimization,show-ing that these methods have developed into mature modeling and solution frameworks capable of reduc-ing traction energy consumption and peak power while enhancing the utilization of regenerative braking energy under safety and service constraints.The second strand summarizes technology-driven progress and discusses the enabling effects and challenges of emerging technologies across three domains:Power supply,control,and operations.On the power side,efforts focus on advancing"source-grid-storage-train"integration,coordinating reversible converters,energy storage,and renewable sources to achieve load shifting and peak suppression.On the control side,autonomous operation and virtual coupling shift the problem toward a dynamic perspective under multi-train coordination.On the opera-tions side,cross-line through services,multi-route patterns,and flexible train formations upgrade ca-pacity supply and reduce inefficient traction.The findings suggest that classical methods already pro-vide well-established frameworks for safe and effective energy saving,while new technology-driven approaches move beyond train-level offline optimization and progressively drive research toward system-level coordination that spans power supply,control,and operations.Future research should place greater emphasis on developing unified benchmark and evaluation systems,while also advancing multi-level coordination and integrated optimization across diverse domains.

Abstract:In response to the challenge of extracting information from railway signal system station yard diagrams,this study proposes a primitive detection model,YOLO11-AT,based on an improved YOLO11.By constructing a detection model that integrates object detection and keypoint detection,it achieves automatic extraction of primitives and keypoints.First,an Attentional Scale Sequence Fu-sion(ASF)module is incorporated into the neck network to fuse multi-scale features,thereby enhanc-ing detection performance for small targets.Second,a Task-Aligned Dynamic Detection Head(TADDH)is implemented in the head network,which improves feature interaction between classifica-tion and localization tasks through task alignment,reduces feature conflicts,and increases detection accuracy for densely distributed targets.Finally,Slicing Aided Hyper Inference(SAHI)is applied to improve detection accuracy on high-resolution station yard images.Experiments are conducted on a constructed dataset containing multi-style station yard diagrams to validate the proposed method.The results show that,compared with YOLO11s-pose,the proposed YOLO11-AT improves precision,recall,mAP0.5,and mAP0.5-kp by 9%,2.2%,4.2%,and 3.2%,respectively,while reducing the number of parameters by 4.3%.Compared with existing mainstream detection models,YOLO11-AT achieves a better balance between detection accuracy and efficiency.The results indicate that the pro-posed method is adaptable to various styles of station yard diagrams and provide a feasible solution for automated information extraction from station yard drawings.

Abstract:Due to constantly interacting targets,rapidly shifting environments,and the inherent hetero-geneity of multi-sensor data,dynamic traffic scenarios impose stringent demands on the perceptual ro-bustness and decision reliability of intelligent systems.Multi-modal learning emerges as a critical solu-tion to overcome bottlenecks in dynamic scenario understanding by fusing heterogeneous modalities.This paper offers a systematic review on multimodal robust learning for dynamic traffic scenarios.First,we clarify the definition of multimodal dynamic traffic scenarios,and analyzes the types and dy-namic characteristics of multi-source modalities(e.g.,optical,radio frequency,and acoustic).Next,we lay out the fundamental principles of multi-modal learning,paying particular attention to key tech-niques that enhance robustness across data-level processing,model architectures,and training strate-gies.Furthermore,we delve into core challenges currently confronting the field and future research di-rections.The review highlights current challenging of data imperfections,model limitations,and the absence of evaluation benchmarks,and chart future directions toward three aspects:technological inno-vation,technology integration,and collaborative industry efforts.Our aim is to provide a systematic reference for both theoretical research and practical deployment of multimodal robust learning in dy-namic traffic scenarios,facilitate the evolution of intelligent transportation systems from being"avail-able in limited scenarios"to achieving"reliability across all conditions,"and provide critical technical support for the widespread adoption of intelligent transportation solutions.

Abstract:Time series anomaly detection,however,faces numerous challenges due to the complexity of data characteristics,algorithmic requirements,and diverse application scenarios.To address this,this paper presents a comprehensive survey of time series anomaly detection.First,the paper system-atically analyzes the complexity and challenges of time series anomaly detection tasks from three di-mensions:data characteristics,algorithm requirements,and application scenarios.Second,it catego-rizes anomalies in time series into point anomalies,subsequence anomalies,and inter-variable correla-tion anomalies,providing a detailed exposition of the definitions and detection methods for each type.Third,the paper reviews and analyzes the use of traditional statistical methods,machine learning tech-niques,and deep learning approaches in time series anomaly detection,evaluating their applicability and limitations.Subsequently,it compiles widely used time series anomaly detection datasets,analyz-ing the application scenarios and unique features of each dataset.Finally,it discusses future research directions in time series anomaly detection from five perspectives:anomaly localization,anomaly clas-sification,precursor forecasting,interpretability,and integration with large-scale models.The review highlights that current challenges,including data scarcity,anomaly diversity,and concept drift,re-main unresolved.Future anomaly detection research is expected to evolve toward more granular tasks such as anomaly localization and prediction.

Abstract:Aiming at the problems of low accuracy and high model complexity in foreign object detec-tion caused by the large scale variations and variable shape of foreign object targets in the complex envi-ronment of transmission lines,an improved foreign object detection model DLS-YOLOv8n is pro-posed.Firstly,the Bottleneck structure in the C2f module of the backbone network is replaced by the Deformable Convolution Bottleneck module to strengthen the model's feature extraction ability of for-eign object targets with variable shapes and improve the detection accuracy;Secondly,a Light Bi-directional Feature Pyramid Network is proposed to replace the neck network of the original model,which reduces the number of model parameters and computational complexity while improving the detection accuracy of the network on small targets;Thirdly,a parameter free attention mechanism SimAM is added before the model detection head to enhance the model's attention to targets in complex environments.Finally,to validate the performance of the DLS-YOLOv8n model,abla-tion experiments and multiple comparative experiments are conducted on a power line foreign ob-ject dataset.Experimental results show that the proposed algorithm achieves an mAP of 97.1％on the dataset of foreign objects in transmission lines,with a model parameter number of 2.07 M and a computational complexity of 6.9 G.Compared with the original YOLOv8n model,the mAP is increased by 1.6％,and the parameter number and computational complexity are reduced by 31％and 14％,respectively.Compared with one-stage detection models such as(Single Shot MultiBox Detector,SSD),YOLOv5s,and YOLOv7-tiny,the proposed model achieves the highest detec-tion accuracy while maintaining the lowest complexity.The research findings can provide valuable reference and insights for the field of transmission line inspection.

Abstract:To address the common issues in existing unsupervised anomaly detection methods,such as insufficient feature extraction and the inability to effectively focus on anomalous regions,which lead to degraded detection performance,we propose an unsupervised anomaly detection method based on a general vision model and attention enhancement.First,the proposed method utilizes a pre-trained general vision model,the Vision Transformer(ViT),to extract features from input images.Second,to further enhance the model's focus on abnormal regions,we incorporate the Convolutional Block Attention Module(CBAM),which adaptively adjusts feature weights during the feature extraction stage to more precisely capture local anomalous information.Additionally,extensive experiments are conducted on the MVTec industrial dataset and a self-made cable anomaly dataset to comprehensively evaluate the detection performance of the proposed method.The experimental results demonstrate that the proposed method outperforms multiple state-of-the-art approaches in unsupervised anomaly detec-tion tasks.Specifically,on the cable anomaly dataset,the proposed method achieves an Image-wise AUROC(Image-wise Area Under ROC)and F1-Score of 88.1％and 80.8％,respectively,outper-forming the baseline Fastflow algorithm by 11.7％and 7.8％.

Abstract:To address the high failure rate,low maintenance efficiency,and challenges in predicting the health status of railway turnouts,this study proposes a predictive method based on the integration of a One-dimensional Convolutional Neural Network(1D-CNN)and a Transformer model,using the S700K turnout machine as the research object.First,1D-CNN is employed to extract features from the raw data,generating 10 feature sets after training.Then,through feature evaluation,the five most representative feature sets for assessing turnout health are selected.These features,along with the health label values derived from the turnout power curve,are used to train the Transformer model,yielding the predicted health index.Finally,to evaluate the health status of the turnout system,Fisher's optimal segmentation algorithm is used to classify health stages,determining the optimal num-ber of health levels as three.Guidance is provided for maintenance work at different health stages.The research results indicate that the combined 1D-CNN and Transformer model exhibits superior predic-tive performance and generalization ability.Compared to commonly used models such as Gated Recur-rent Unit(GRU)and Long Short-Term Memory(LSTM),the Transformer model achieves better performance in processing long time-series data.The proposed hybrid model significantly improves the accuracy of turnout health status prediction,reducing Mean Absolute Error(MAE)and Root Mean Square Error(RMSE)by 31.2％and 30.5％,respectively,compared to the 1D-CNN and LSTM model combination.

Abstract:In reconfigurable intelligent surface(RIS)-assisted millimeter-wave(mmWave)wireless communication systems for high-speed railways,the RIS-assisted Doppler shift compensation tech-niques are investigated to address the frequency shift caused by the Doppler effect of electromagnetic waves in highly dynamic,high-speed environments.First,based on a time-varying 3D Saleh-Valenzuela mm Wave channel model,an expression for the Doppler shift is derived,and an optimal RIS phase shift matrix is designed with the objective of compensating for the Doppler shift in the cas-caded transmission link.Next,considering errors in vehicle speed estimation,the speed estimation er-ror and its Probability Density Function(PDF)are analyzed,from which the residual Doppler error is computed,and its impact on the study is discussed.Finally,to evaluate the performance of the com-pensated system,analytical expressions for Spectral Efficiency(SE)and outage probability(OP)are derived,and simulation experiments are conducted to validate the effectiveness of the approach.Simu-lation results show that minimizing the Doppler shift,instead of maximizing received power,can com-pletely eliminate the Doppler shift.When the number of RIS is 322 and the base station antenna height is 50 m,the SE increases by 5.87 bit/(s·Hz)when the train approaches the RIS.Additionally,when the height of the base station antenna is 30 m,50 m and 70 m,the OP varies between 5× 10-3 and 6×10-3.