Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address ...Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.展开更多
Road traffic flow forecasting provides critical information for the operational management of road mobility challenges, and models are used to generate the forecast. This paper uses a random process to present a novel...Road traffic flow forecasting provides critical information for the operational management of road mobility challenges, and models are used to generate the forecast. This paper uses a random process to present a novel traffic modelling framework for aggregate traffic on urban roads. The main idea is that road traffic flow is random, even for the recurrent flow, such as rush hour traffic, which is predisposed to congestion. Therefore, the structure of the aggregate traffic flow model for urban roads should correlate well with the essential variables of the observed random dynamics of the traffic flow phenomena. The novelty of this paper is the developed framework, based on the Poisson process, the kinematics of urban road traffic flow, and the intermediate modelling approach, which were combined to formulate the model. Empirical data from an urban road in Ghana was used to explore the model’s fidelity. The results show that the distribution from the model correlates well with that of the empirical traffic, providing a strong validation of the new framework and instilling confidence in its potential for significantly improved forecasts and, hence, a more hopeful outlook for real-world traffic management.展开更多
The increase in population and vehicles exacerbates traffic congestion and management difficulties.Therefore,achieving accurate and efficient traffic flow prediction is crucial for urban transportation.For that reason...The increase in population and vehicles exacerbates traffic congestion and management difficulties.Therefore,achieving accurate and efficient traffic flow prediction is crucial for urban transportation.For that reason,we propose a graph federated learning-based digital twin traffic flow prediction method(GFLDT)by integrating the benefits of collaborative intelligence and computation of intelligent IoT.Specifically,we construct a digital twin network for predicting traffic flow,which is divided into client twin and global twin.Based on this,we adopt the concept of graph federated learning to learn the temporal dependence of traffic flow using local data from client twins,and the spatial dependence of traffic flow using global information from global twins.In addition,we validate on a real traffic dataset,and the results show that through collaborative training of the client twins and the global twins,GFLDT achieves accurate traffic flow prediction while protecting data security.展开更多
As an effective strategy to address urban traffic congestion,traffic flow prediction has gained attention from Federated-Learning(FL)researchers due FL’s ability to preserving data privacy.However,existing methods fa...As an effective strategy to address urban traffic congestion,traffic flow prediction has gained attention from Federated-Learning(FL)researchers due FL’s ability to preserving data privacy.However,existing methods face challenges:some are too simplistic to capture complex traffic patterns effectively,and others are overly complex,leading to excessive communication overhead between cloud and edge devices.Moreover,the problem of single point failure limits their robustness and reliability in real-world applications.To tackle these challenges,this paper proposes a new method,CMBA-FL,a Communication-Mitigated and Blockchain-Assisted Federated Learning model.First,CMBA-FL improves the client model’s ability to capture temporal traffic patterns by employing the Encoder-Decoder framework for each edge device.Second,to reduce the communication overhead during federated learning,we introduce a verification method based on parameter update consistency,avoiding unnecessary parameter updates.Third,to mitigate the risk of a single point of failure,we integrate consensus mechanisms from blockchain technology.To validate the effectiveness of CMBA-FL,we assess its performance on two widely used traffic datasets.Our experimental results show that CMBA-FL reduces prediction error by 11.46%,significantly lowers communication overhead,and improves security.展开更多
A novel deceleration traffic flow model is established based on the oscillatory congested states and the slow-tostart rule.The novel model considers human overreaction and mechanical restrictions as limited decelerati...A novel deceleration traffic flow model is established based on the oscillatory congested states and the slow-tostart rule.The novel model considers human overreaction and mechanical restrictions as limited deceleration capacity,effectively avoiding the unrealistic deceleration behavior found in most existing traffic flow models.In order to consider that the acceleration of a stationary vehicle is slower than that of a moving vehicle due to reasons such as driver inattention,the slow-to-start rule is introduced.In actual traffic,the driver will take different deceleration measures according to local traffic conditions,divided into ordinary and emergency deceleration.The deceleration setting in the deceleration model with only ordinary deceleration is modified.Computer simulations show that the novel model can achieve smooth,comfortable acceleration and deceleration behavior.Introducing the slow-to-start rule can realize the first-order transition from free flow to synchronized flow.The oscillatory congested states enable a first-order transition from synchronized flow to wide moving jam.Under periodic boundary conditions,the novel model can reproduce three traffic flow phases(free flow,synchronized flow,and wide moving jam)and two first-order transitions between three phases.In addition,the novel model can reproduce empirical results such as linear synchronized flow and headway distribution of free flow below 1 s.Under open boundary conditions,different congested patterns caused by on-ramps are analyzed.Compared with the classic deceleration model,this model can better reproduce the phenomenon and characteristics of actual traffic flow and provide more accurate decision support for daily traffic management of expressways.展开更多
Air traffic flow management has been a major means for balancing air traffic demandand airport or airspace capacity to reduce congestion and flight delays.However,unpredictable fac-tors,such as weather and equipment m...Air traffic flow management has been a major means for balancing air traffic demandand airport or airspace capacity to reduce congestion and flight delays.However,unpredictable fac-tors,such as weather and equipment malfunctions,can cause dynamic changes in airport and sectorcapacity,resulting in significant alterations to optimized flight schedules and the calculated pre-departure slots.Therefore,taking into account capacity uncertainties is essential to create a moreresilient flight schedule.This paper addresses the flight pre-departure sequencing issue and intro-duces a capacity uncertainty model for optimizing flight schedule at the airport network level.The goal of the model is to reduce the total cost of flight delays while increasing the robustnessof the optimized schedule.A chance-constrained model is developed to address the capacity uncer-tainty of airports and sectors,and the significance of airports and sectors in the airport network isconsidered when setting the violation probability.The performance of the model is evaluated usingreal flight data by comparing them with the results of the deterministic model.The development ofthe model based on the characteristics of this special optimization mechanism can significantlyenhance its performance in addressing the pre-departure flight scheduling problem at the airportnetwork level.展开更多
Traffic flow prediction is a key component of intelligent transportation systems,particularly in datascarce regions where traditional models relying on complete datasets often fail to provide accurate forecasts.These ...Traffic flow prediction is a key component of intelligent transportation systems,particularly in datascarce regions where traditional models relying on complete datasets often fail to provide accurate forecasts.These regions are characterized by limited sensor coverage and sparse data collection,pose significant challenges for existing prediction methods.To address this,we propose a novel transfer learning framework called transfer learning with deep knowledge distillation(TL-DKD),which combines graph neural network(GNN)with deep knowledge distillation to enable effective knowledge transfer from data-rich to data-scarce domains.Our contributions are three-fold:(1)We introduce,for the first time,a unique integration of deep knowledge distillation and transfer learning,enhancing feature adaptability across diverse traffic datasets while addressing data scarcity.(2)We design an encoder-decoder architecture where the encoder retains generalized spatiotemporal patterns fromsource domains,and the decoder finetunes predictions for target domains,ensuring minimal information loss during transfer.(3)Extensive experiments on five real-world datasets(METR-LA,PeMS-Bay,PeMS03/04/08)demonstrate the framework’s robustness.The TL-DKD model achieves significant improvements in prediction accuracy,especially in data-scarce scenarios.For example,the PEMSD4 dataset in multi-region experiments,it achieves a mean absolute error(MAE)of 20.08,a mean absolute percentage error(MAPE)of 13.59%,and a root mean squared error(RMSE)of 31.75 for 30-min forecasts.Additionally,noise-augmented experiments show improved adaptability under perturbed data conditions.These results highlight the framework’s practical impact,offering a scalable solution for accurate traffic predictions in resource-constrained environments.展开更多
Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating In...Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.展开更多
This paper focuses on the problem of traffic flow forecasting,with the aim of forecasting future traffic conditions based on historical traffic data.This problem is typically tackled by utilizing spatio-temporal graph...This paper focuses on the problem of traffic flow forecasting,with the aim of forecasting future traffic conditions based on historical traffic data.This problem is typically tackled by utilizing spatio-temporal graph neural networks to model the intricate spatio-temporal correlations among traffic data.Although these methods have achieved performance improvements,they often suffer from the following limitations:These methods face challenges in modeling high-order correlations between nodes.These methods overlook the interactions between nodes at different scales.To tackle these issues,in this paper,we propose a novel model named multi-scale dynamic hypergraph convolutional network(MSDHGCN)for traffic flow forecasting.Our MSDHGCN can effectively model the dynamic higher-order relationships between nodes at multiple time scales,thereby enhancing the capability for traffic forecasting.Experiments on two real-world datasets demonstrate the effectiveness of the proposed method.展开更多
Accurate traffic flow prediction(TFP)is vital for efficient and sustainable transportation management and the development of intelligent traffic systems.However,missing data in real-world traffic datasets poses a sign...Accurate traffic flow prediction(TFP)is vital for efficient and sustainable transportation management and the development of intelligent traffic systems.However,missing data in real-world traffic datasets poses a significant challenge to maintaining prediction precision.This study introduces REPTF-TMDI,a novel method that combines a Reduced Error Pruning Tree Forest(REPTree Forest)with a newly proposed Time-based Missing Data Imputation(TMDI)approach.The REP Tree Forest,an ensemble learning approach,is tailored for time-related traffic data to enhance predictive accuracy and support the evolution of sustainable urbanmobility solutions.Meanwhile,the TMDI approach exploits temporal patterns to estimate missing values reliably whenever empty fields are encountered.The proposed method was evaluated using hourly traffic flow data from a major U.S.roadway spanning 2012-2018,incorporating temporal features(e.g.,hour,day,month,year,weekday),holiday indicator,and weather conditions(temperature,rain,snow,and cloud coverage).Experimental results demonstrated that the REPTF-TMDI method outperformed conventional imputation techniques across various missing data ratios by achieving an average 11.76%improvement in terms of correlation coefficient(R).Furthermore,REPTree Forest achieved improvements of 68.62%in RMSE and 70.52%in MAE compared to existing state-of-the-art models.These findings highlight the method’s ability to significantly boost traffic flow prediction accuracy,even in the presence of missing data,thereby contributing to the broader objectives of sustainable urban transportation systems.展开更多
With the development of intelligent and interconnected traffic system,a convergence of traffic stream is anticipated in the foreseeable future,where both connected automated vehicle(CAV)and human driven vehicle(HDV)wi...With the development of intelligent and interconnected traffic system,a convergence of traffic stream is anticipated in the foreseeable future,where both connected automated vehicle(CAV)and human driven vehicle(HDV)will coexist.In order to examine the effect of CAV on the overall stability and energy consumption of such a heterogeneous traffic system,we first take into account the interrelated perception of distance and speed by CAV to establish a macroscopic dynamic model through utilizing the full velocity difference(FVD)model.Subsequently,adopting the linear stability theory,we propose the linear stability condition for the model through using the small perturbation method,and the validity of the heterogeneous model is verified by comparing with the FVD model.Through nonlinear theoretical analysis,we further derive the KdV-Burgers equation,which captures the propagation characteristics of traffic density waves.Finally,by numerical simulation experiments through utilizing a macroscopic model of heterogeneous traffic flow,the effect of CAV permeability on the stability of density wave in heterogeneous traffic flow and the energy consumption of the traffic system is investigated.Subsequent analysis reveals emergent traffic phenomena.The experimental findings demonstrate that as CAV permeability increases,the ability to dampen the propagation of fluctuations in heterogeneous traffic flow gradually intensifies when giving system perturbation,leading to enhanced stability of the traffic system.Furthermore,higher initial traffic density renders the traffic system more susceptible to congestion,resulting in local clustering effect and stop-and-go traffic phenomenon.Remarkably,the total energy consumption of the heterogeneous traffic system exhibits a gradual decline with CAV permeability increasing.Further evidence has demonstrated the positive influence of CAV on heterogeneous traffic flow.This research contributes to providing theoretical guidance for future CAV applications,aiming to enhance urban road traffic efficiency and alleviate congestion.展开更多
A significant obstacle in intelligent transportation systems(ITS)is the capacity to predict traffic flow.Recent advancements in deep neural networks have enabled the development of models to represent traffic flow acc...A significant obstacle in intelligent transportation systems(ITS)is the capacity to predict traffic flow.Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately.However,accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors.This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory(Conv-BiLSTM)with attention mechanisms.Prior studies neglected to include data pertaining to factors such as holidays,weather conditions,and vehicle types,which are interconnected and significantly impact the accuracy of forecast outcomes.In addition,this research incorporates recurring monthly periodic pattern data that significantly enhances the accuracy of forecast outcomes.The experimental findings demonstrate a performance improvement of 21.68%when incorporating the vehicle type feature.展开更多
VPNs are vital for safeguarding communication routes in the continually changing cybersecurity world.However,increasing network attack complexity and variety require increasingly advanced algorithms to recognize and c...VPNs are vital for safeguarding communication routes in the continually changing cybersecurity world.However,increasing network attack complexity and variety require increasingly advanced algorithms to recognize and categorizeVPNnetwork data.We present a novelVPNnetwork traffic flowclassificationmethod utilizing Artificial Neural Networks(ANN).This paper aims to provide a reliable system that can identify a virtual private network(VPN)traffic fromintrusion attempts,data exfiltration,and denial-of-service assaults.We compile a broad dataset of labeled VPN traffic flows from various apps and usage patterns.Next,we create an ANN architecture that can handle encrypted communication and distinguish benign from dangerous actions.To effectively process and categorize encrypted packets,the neural network model has input,hidden,and output layers.We use advanced feature extraction approaches to improve the ANN’s classification accuracy by leveraging network traffic’s statistical and behavioral properties.We also use cutting-edge optimizationmethods to optimize network characteristics and performance.The suggested ANN-based categorization method is extensively tested and analyzed.Results show the model effectively classifies VPN traffic types.We also show that our ANN-based technique outperforms other approaches in precision,recall,and F1-score with 98.79%accuracy.This study improves VPN security and protects against new cyberthreats.Classifying VPNtraffic flows effectively helps enterprises protect sensitive data,maintain network integrity,and respond quickly to security problems.This study advances network security and lays the groundwork for ANN-based cybersecurity solutions.展开更多
As one of the core modules for air traffic flow management,Air Traffic Flow Prediction(ATFP)in the Multi-Airport System(MAS)is a prerequisite for demand and capacity balance in the complex meteorological environment.D...As one of the core modules for air traffic flow management,Air Traffic Flow Prediction(ATFP)in the Multi-Airport System(MAS)is a prerequisite for demand and capacity balance in the complex meteorological environment.Due to the challenge of implicit interaction mechanism among traffic flow,airspace capacity and weather impact,the Weather-aware ATFP(Wa-ATFP)is still a nontrivial issue.In this paper,a novel Multi-faceted Spatio-Temporal Graph Convolutional Network(MSTGCN)is proposed to address the Wa-ATFP within the complex operations of MAS.Firstly,a spatio-temporal graph is constructed with three different nodes,including airport,route,and fix to describe the topology structure of MAS.Secondly,a weather-aware multi-faceted fusion module is proposed to integrate the feature of air traffic flow and the auxiliary features of capacity and weather,which can effectively address the complex impact of severe weather,e.g.,thunderstorms.Thirdly,to capture the latent connections of nodes,an adaptive graph connection constructor is designed.The experimental results with the real-world operational dataset in Guangdong-Hong Kong-Macao Greater Bay Area,China,validate that the proposed approach outperforms the state-of-the-art machine-learning and deep-learning based baseline approaches in performance.展开更多
Traffic flow prediction plays a key role in the construction of intelligent transportation system.However,due to its complex spatio-temporal dependence and its uncertainty,the research becomes very challenging.Most of...Traffic flow prediction plays a key role in the construction of intelligent transportation system.However,due to its complex spatio-temporal dependence and its uncertainty,the research becomes very challenging.Most of the existing studies are based on graph neural networks that model traffic flow graphs and try to use fixed graph structure to deal with the relationship between nodes.However,due to the time-varying spatial correlation of the traffic network,there is no fixed node relationship,and these methods cannot effectively integrate the temporal and spatial features.This paper proposes a novel temporal-spatial dynamic graph convolutional network(TSADGCN).The dynamic time warping algorithm(DTW)is introduced to calculate the similarity of traffic flow sequence among network nodes in the time dimension,and the spatiotemporal graph of traffic flow is constructed to capture the spatiotemporal characteristics and dependencies of traffic flow.By combining graph attention network and time attention network,a spatiotemporal convolution block is constructed to capture spatiotemporal characteristics of traffic data.Experiments on open data sets PEMSD4 and PEMSD8 show that TSADGCN has higher prediction accuracy than well-known traffic flow prediction algorithms.展开更多
Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system. However, the traffic data's complexity and fluctuation, as well as the noise produce...Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system. However, the traffic data's complexity and fluctuation, as well as the noise produced during collecting information and summarizing original data of traffic flow, cause large errors in the traffic flow forecasting results. This article suggests a solution to the above mentioned issues and proposes a fully connected time-gated neural network based on wavelet reconstruction(WT-FCTGN). To eliminate the potential noise and strengthen the potential traffic trend in the data, we adopt the methods of wavelet reconstruction and periodic data introduction to preprocess the data. The model introduces fully connected time-series blocks to model all the information including time sequence information and fluctuation information in the flow of traffic, and establishes the time gate block to comprehend the periodic characteristics of the flow of traffic and predict its flow. The performance of the WT-FCTGN model is validated on the public Pe MS data set. The experimental results show that the WT-FCTGN model has higher accuracy, and its mean absolute error(MAE), mean absolute percentage error(MAPE) and root mean square error(RMSE) are obviously lower than those of the other algorithms. The robust experimental results prove that the WT-FCTGN model has good anti-noise ability.展开更多
Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel efficiency.To improve prediction accuracy,a crucial...Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel efficiency.To improve prediction accuracy,a crucial issue is how to model spatiotemporal dependency in urban traffic data.In recent years,many studies have adopted spatiotemporal neural networks to extract key information from traffic data.However,most models ignore the semantic spatial similarity between long-distance areas when mining spatial dependency.They also ignore the impact of predicted time steps on the next unpredicted time step for making long-term predictions.Moreover,these models lack a comprehensive data embedding process to represent complex spatiotemporal dependency.This paper proposes a multi-scale persistent spatiotemporal transformer(MSPSTT)model to perform accurate long-term traffic flow prediction in cities.MSPSTT adopts an encoder-decoder structure and incorporates temporal,periodic,and spatial features to fully embed urban traffic data to address these issues.The model consists of a spatiotemporal encoder and a spatiotemporal decoder,which rely on temporal,geospatial,and semantic space multi-head attention modules to dynamically extract temporal,geospatial,and semantic characteristics.The spatiotemporal decoder combines the context information provided by the encoder,integrates the predicted time step information,and is iteratively updated to learn the correlation between different time steps in the broader time range to improve the model’s accuracy for long-term prediction.Experiments on four public transportation datasets demonstrate that MSPSTT outperforms the existing models by up to 9.5%on three common metrics.展开更多
This paper introduces an innovative approach to the synchronized demand-capacity balance with special focus on sector capacity uncertainty within a centrally controlled collaborative air traffic flow management(ATFM)f...This paper introduces an innovative approach to the synchronized demand-capacity balance with special focus on sector capacity uncertainty within a centrally controlled collaborative air traffic flow management(ATFM)framework.Further with previous study,the uncertainty in capacity is considered as a non-negligible issue regarding multiple reasons,like the impact of weather,the strike of air traffic controllers(ATCOs),the military use of airspace and the spatiotemporal distribution of nonscheduled flights,etc.These recessive factors affect the outcome of traffic flow optimization.In this research,the focus is placed on the impact of sector capacity uncertainty on demand and capacity balancing(DCB)optimization and ATFM,and multiple options,such as delay assignment and rerouting,are intended for regulating the traffic flow.A scenario optimization method for sector capacity in the presence of uncertainties is used to find the approximately optimal solution.The results show that the proposed approach can achieve better demand and capacity balancing and determine perfect integer solutions to ATFM problems,solving large-scale instances(24 h on seven capacity scenarios,with 6255 flights and 8949 trajectories)in 5-15 min.To the best of our knowledge,our experiment is the first to tackle large-scale instances of stochastic ATFM problems within the collaborative ATFM framework.展开更多
Traffic flow forecasting plays a crucial role and is the key technology to realize dynamic traffic guidance and active traffic control in intelligent traffic systems(ITS).Aiming at the complex local and global spatial...Traffic flow forecasting plays a crucial role and is the key technology to realize dynamic traffic guidance and active traffic control in intelligent traffic systems(ITS).Aiming at the complex local and global spatial-temporal dynamic characteristics of traffic flow,this paper proposes a new traffic flow forecasting model spatial-temporal attention graph neural network(STA-GNN)by combining at-tention mechanism(AM)and spatial-temporal convolutional network.The model learns the hidden dynamic local spatial correlations of the traffic network by combining the dynamic adjacency matrix constructed by the graph learning layer with the graph convolutional network(GCN).The local tem-poral correlations of traffic flow at different scales are extracted by stacking multiple convolutional kernels in temporal convolutional network(TCN).And the global spatial-temporal dependencies of long-time sequences of traffic flow are captured by the spatial-temporal attention mechanism(STAtt),which enhances the global spatial-temporal modeling and the representational ability of model.The experimental results on two datasets,METR-LA and PEMS-BAY,show the proposed STA-GNN model outperforms the common baseline models in forecasting accuracy.展开更多
Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion with...Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion within elevator systems. Many passengers experience dissatisfaction with prolonged wait times, leading to impatience and frustration among building occupants. The widespread adoption of neural networks and deep learning technologies across various fields and industries represents a significant paradigm shift, and unlocking new avenues for innovation and advancement. These cutting-edge technologies offer unprecedented opportunities to address complex challenges and optimize processes in diverse domains. In this study, LSTM (Long Short-Term Memory) network technology is leveraged to analyze elevator traffic flow within a typical office building. By harnessing the predictive capabilities of LSTM, the research aims to contribute to advancements in elevator group control design, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments. The findings of this research have the potential to reference the development of intelligent elevator management systems, capable of dynamically adapting to fluctuating passenger demand and optimizing elevator usage in real-time. By enhancing the efficiency and functionality of vertical transportation systems, the research contributes to creating more sustainable, accessible, and user-friendly living environments for individuals across diverse demographics.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.62472149,62376089,62202147)Hubei Provincial Science and Technology Plan Project(2023BCB04100).
文摘Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.
文摘Road traffic flow forecasting provides critical information for the operational management of road mobility challenges, and models are used to generate the forecast. This paper uses a random process to present a novel traffic modelling framework for aggregate traffic on urban roads. The main idea is that road traffic flow is random, even for the recurrent flow, such as rush hour traffic, which is predisposed to congestion. Therefore, the structure of the aggregate traffic flow model for urban roads should correlate well with the essential variables of the observed random dynamics of the traffic flow phenomena. The novelty of this paper is the developed framework, based on the Poisson process, the kinematics of urban road traffic flow, and the intermediate modelling approach, which were combined to formulate the model. Empirical data from an urban road in Ghana was used to explore the model’s fidelity. The results show that the distribution from the model correlates well with that of the empirical traffic, providing a strong validation of the new framework and instilling confidence in its potential for significantly improved forecasts and, hence, a more hopeful outlook for real-world traffic management.
基金supported by the National Natural Science Foundation of China(U23A20272,U22A2069,62272146)Natural Science Foundation of Henan(252300421237).
文摘The increase in population and vehicles exacerbates traffic congestion and management difficulties.Therefore,achieving accurate and efficient traffic flow prediction is crucial for urban transportation.For that reason,we propose a graph federated learning-based digital twin traffic flow prediction method(GFLDT)by integrating the benefits of collaborative intelligence and computation of intelligent IoT.Specifically,we construct a digital twin network for predicting traffic flow,which is divided into client twin and global twin.Based on this,we adopt the concept of graph federated learning to learn the temporal dependence of traffic flow using local data from client twins,and the spatial dependence of traffic flow using global information from global twins.In addition,we validate on a real traffic dataset,and the results show that through collaborative training of the client twins and the global twins,GFLDT achieves accurate traffic flow prediction while protecting data security.
基金supported by the National Natural Science Foundation of China under Grant No.U20A20182.
文摘As an effective strategy to address urban traffic congestion,traffic flow prediction has gained attention from Federated-Learning(FL)researchers due FL’s ability to preserving data privacy.However,existing methods face challenges:some are too simplistic to capture complex traffic patterns effectively,and others are overly complex,leading to excessive communication overhead between cloud and edge devices.Moreover,the problem of single point failure limits their robustness and reliability in real-world applications.To tackle these challenges,this paper proposes a new method,CMBA-FL,a Communication-Mitigated and Blockchain-Assisted Federated Learning model.First,CMBA-FL improves the client model’s ability to capture temporal traffic patterns by employing the Encoder-Decoder framework for each edge device.Second,to reduce the communication overhead during federated learning,we introduce a verification method based on parameter update consistency,avoiding unnecessary parameter updates.Third,to mitigate the risk of a single point of failure,we integrate consensus mechanisms from blockchain technology.To validate the effectiveness of CMBA-FL,we assess its performance on two widely used traffic datasets.Our experimental results show that CMBA-FL reduces prediction error by 11.46%,significantly lowers communication overhead,and improves security.
基金supported by the National Natural Science Foundation of China(Grant No.71671109)the National Key Research and Development Program of China(Grant No.2020YFB1600500)the Key Research and Development Program of Heilongjiang Province,China(Grant No.GZ20220089)。
文摘A novel deceleration traffic flow model is established based on the oscillatory congested states and the slow-tostart rule.The novel model considers human overreaction and mechanical restrictions as limited deceleration capacity,effectively avoiding the unrealistic deceleration behavior found in most existing traffic flow models.In order to consider that the acceleration of a stationary vehicle is slower than that of a moving vehicle due to reasons such as driver inattention,the slow-to-start rule is introduced.In actual traffic,the driver will take different deceleration measures according to local traffic conditions,divided into ordinary and emergency deceleration.The deceleration setting in the deceleration model with only ordinary deceleration is modified.Computer simulations show that the novel model can achieve smooth,comfortable acceleration and deceleration behavior.Introducing the slow-to-start rule can realize the first-order transition from free flow to synchronized flow.The oscillatory congested states enable a first-order transition from synchronized flow to wide moving jam.Under periodic boundary conditions,the novel model can reproduce three traffic flow phases(free flow,synchronized flow,and wide moving jam)and two first-order transitions between three phases.In addition,the novel model can reproduce empirical results such as linear synchronized flow and headway distribution of free flow below 1 s.Under open boundary conditions,different congested patterns caused by on-ramps are analyzed.Compared with the classic deceleration model,this model can better reproduce the phenomenon and characteristics of actual traffic flow and provide more accurate decision support for daily traffic management of expressways.
基金supported by the National Natural Science Foundation of China(Nos.U2033203,U1833126,61773203,61304190)。
文摘Air traffic flow management has been a major means for balancing air traffic demandand airport or airspace capacity to reduce congestion and flight delays.However,unpredictable fac-tors,such as weather and equipment malfunctions,can cause dynamic changes in airport and sectorcapacity,resulting in significant alterations to optimized flight schedules and the calculated pre-departure slots.Therefore,taking into account capacity uncertainties is essential to create a moreresilient flight schedule.This paper addresses the flight pre-departure sequencing issue and intro-duces a capacity uncertainty model for optimizing flight schedule at the airport network level.The goal of the model is to reduce the total cost of flight delays while increasing the robustnessof the optimized schedule.A chance-constrained model is developed to address the capacity uncer-tainty of airports and sectors,and the significance of airports and sectors in the airport network isconsidered when setting the violation probability.The performance of the model is evaluated usingreal flight data by comparing them with the results of the deterministic model.The development ofthe model based on the characteristics of this special optimization mechanism can significantlyenhance its performance in addressing the pre-departure flight scheduling problem at the airportnetwork level.
基金supported by the National Natural Science Foundation of China(Grant No.52002031)the Shaanxi Province Key R&D Plan Project(No.2024GX-YBXM-002).
文摘Traffic flow prediction is a key component of intelligent transportation systems,particularly in datascarce regions where traditional models relying on complete datasets often fail to provide accurate forecasts.These regions are characterized by limited sensor coverage and sparse data collection,pose significant challenges for existing prediction methods.To address this,we propose a novel transfer learning framework called transfer learning with deep knowledge distillation(TL-DKD),which combines graph neural network(GNN)with deep knowledge distillation to enable effective knowledge transfer from data-rich to data-scarce domains.Our contributions are three-fold:(1)We introduce,for the first time,a unique integration of deep knowledge distillation and transfer learning,enhancing feature adaptability across diverse traffic datasets while addressing data scarcity.(2)We design an encoder-decoder architecture where the encoder retains generalized spatiotemporal patterns fromsource domains,and the decoder finetunes predictions for target domains,ensuring minimal information loss during transfer.(3)Extensive experiments on five real-world datasets(METR-LA,PeMS-Bay,PeMS03/04/08)demonstrate the framework’s robustness.The TL-DKD model achieves significant improvements in prediction accuracy,especially in data-scarce scenarios.For example,the PEMSD4 dataset in multi-region experiments,it achieves a mean absolute error(MAE)of 20.08,a mean absolute percentage error(MAPE)of 13.59%,and a root mean squared error(RMSE)of 31.75 for 30-min forecasts.Additionally,noise-augmented experiments show improved adaptability under perturbed data conditions.These results highlight the framework’s practical impact,offering a scalable solution for accurate traffic predictions in resource-constrained environments.
文摘Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.
基金the National Key Research and Development Program of China(No.2021ZD0112400)。
文摘This paper focuses on the problem of traffic flow forecasting,with the aim of forecasting future traffic conditions based on historical traffic data.This problem is typically tackled by utilizing spatio-temporal graph neural networks to model the intricate spatio-temporal correlations among traffic data.Although these methods have achieved performance improvements,they often suffer from the following limitations:These methods face challenges in modeling high-order correlations between nodes.These methods overlook the interactions between nodes at different scales.To tackle these issues,in this paper,we propose a novel model named multi-scale dynamic hypergraph convolutional network(MSDHGCN)for traffic flow forecasting.Our MSDHGCN can effectively model the dynamic higher-order relationships between nodes at multiple time scales,thereby enhancing the capability for traffic forecasting.Experiments on two real-world datasets demonstrate the effectiveness of the proposed method.
文摘Accurate traffic flow prediction(TFP)is vital for efficient and sustainable transportation management and the development of intelligent traffic systems.However,missing data in real-world traffic datasets poses a significant challenge to maintaining prediction precision.This study introduces REPTF-TMDI,a novel method that combines a Reduced Error Pruning Tree Forest(REPTree Forest)with a newly proposed Time-based Missing Data Imputation(TMDI)approach.The REP Tree Forest,an ensemble learning approach,is tailored for time-related traffic data to enhance predictive accuracy and support the evolution of sustainable urbanmobility solutions.Meanwhile,the TMDI approach exploits temporal patterns to estimate missing values reliably whenever empty fields are encountered.The proposed method was evaluated using hourly traffic flow data from a major U.S.roadway spanning 2012-2018,incorporating temporal features(e.g.,hour,day,month,year,weekday),holiday indicator,and weather conditions(temperature,rain,snow,and cloud coverage).Experimental results demonstrated that the REPTF-TMDI method outperformed conventional imputation techniques across various missing data ratios by achieving an average 11.76%improvement in terms of correlation coefficient(R).Furthermore,REPTree Forest achieved improvements of 68.62%in RMSE and 70.52%in MAE compared to existing state-of-the-art models.These findings highlight the method’s ability to significantly boost traffic flow prediction accuracy,even in the presence of missing data,thereby contributing to the broader objectives of sustainable urban transportation systems.
基金Project supported by the Fundamental Research Funds for Central Universities,China(Grant No.2022YJS065)the National Natural Science Foundation of China(Grant Nos.72288101 and 72371019).
文摘With the development of intelligent and interconnected traffic system,a convergence of traffic stream is anticipated in the foreseeable future,where both connected automated vehicle(CAV)and human driven vehicle(HDV)will coexist.In order to examine the effect of CAV on the overall stability and energy consumption of such a heterogeneous traffic system,we first take into account the interrelated perception of distance and speed by CAV to establish a macroscopic dynamic model through utilizing the full velocity difference(FVD)model.Subsequently,adopting the linear stability theory,we propose the linear stability condition for the model through using the small perturbation method,and the validity of the heterogeneous model is verified by comparing with the FVD model.Through nonlinear theoretical analysis,we further derive the KdV-Burgers equation,which captures the propagation characteristics of traffic density waves.Finally,by numerical simulation experiments through utilizing a macroscopic model of heterogeneous traffic flow,the effect of CAV permeability on the stability of density wave in heterogeneous traffic flow and the energy consumption of the traffic system is investigated.Subsequent analysis reveals emergent traffic phenomena.The experimental findings demonstrate that as CAV permeability increases,the ability to dampen the propagation of fluctuations in heterogeneous traffic flow gradually intensifies when giving system perturbation,leading to enhanced stability of the traffic system.Furthermore,higher initial traffic density renders the traffic system more susceptible to congestion,resulting in local clustering effect and stop-and-go traffic phenomenon.Remarkably,the total energy consumption of the heterogeneous traffic system exhibits a gradual decline with CAV permeability increasing.Further evidence has demonstrated the positive influence of CAV on heterogeneous traffic flow.This research contributes to providing theoretical guidance for future CAV applications,aiming to enhance urban road traffic efficiency and alleviate congestion.
文摘A significant obstacle in intelligent transportation systems(ITS)is the capacity to predict traffic flow.Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately.However,accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors.This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory(Conv-BiLSTM)with attention mechanisms.Prior studies neglected to include data pertaining to factors such as holidays,weather conditions,and vehicle types,which are interconnected and significantly impact the accuracy of forecast outcomes.In addition,this research incorporates recurring monthly periodic pattern data that significantly enhances the accuracy of forecast outcomes.The experimental findings demonstrate a performance improvement of 21.68%when incorporating the vehicle type feature.
文摘VPNs are vital for safeguarding communication routes in the continually changing cybersecurity world.However,increasing network attack complexity and variety require increasingly advanced algorithms to recognize and categorizeVPNnetwork data.We present a novelVPNnetwork traffic flowclassificationmethod utilizing Artificial Neural Networks(ANN).This paper aims to provide a reliable system that can identify a virtual private network(VPN)traffic fromintrusion attempts,data exfiltration,and denial-of-service assaults.We compile a broad dataset of labeled VPN traffic flows from various apps and usage patterns.Next,we create an ANN architecture that can handle encrypted communication and distinguish benign from dangerous actions.To effectively process and categorize encrypted packets,the neural network model has input,hidden,and output layers.We use advanced feature extraction approaches to improve the ANN’s classification accuracy by leveraging network traffic’s statistical and behavioral properties.We also use cutting-edge optimizationmethods to optimize network characteristics and performance.The suggested ANN-based categorization method is extensively tested and analyzed.Results show the model effectively classifies VPN traffic types.We also show that our ANN-based technique outperforms other approaches in precision,recall,and F1-score with 98.79%accuracy.This study improves VPN security and protects against new cyberthreats.Classifying VPNtraffic flows effectively helps enterprises protect sensitive data,maintain network integrity,and respond quickly to security problems.This study advances network security and lays the groundwork for ANN-based cybersecurity solutions.
基金supported by the National Key Research and Development Program of China(No.2022YFB2602402)the National Natural Science Foundation of China(Nos.U2033215 and U2133210).
文摘As one of the core modules for air traffic flow management,Air Traffic Flow Prediction(ATFP)in the Multi-Airport System(MAS)is a prerequisite for demand and capacity balance in the complex meteorological environment.Due to the challenge of implicit interaction mechanism among traffic flow,airspace capacity and weather impact,the Weather-aware ATFP(Wa-ATFP)is still a nontrivial issue.In this paper,a novel Multi-faceted Spatio-Temporal Graph Convolutional Network(MSTGCN)is proposed to address the Wa-ATFP within the complex operations of MAS.Firstly,a spatio-temporal graph is constructed with three different nodes,including airport,route,and fix to describe the topology structure of MAS.Secondly,a weather-aware multi-faceted fusion module is proposed to integrate the feature of air traffic flow and the auxiliary features of capacity and weather,which can effectively address the complex impact of severe weather,e.g.,thunderstorms.Thirdly,to capture the latent connections of nodes,an adaptive graph connection constructor is designed.The experimental results with the real-world operational dataset in Guangdong-Hong Kong-Macao Greater Bay Area,China,validate that the proposed approach outperforms the state-of-the-art machine-learning and deep-learning based baseline approaches in performance.
基金supported by the National Natural Science Foundation of China(Grant:62176086).
文摘Traffic flow prediction plays a key role in the construction of intelligent transportation system.However,due to its complex spatio-temporal dependence and its uncertainty,the research becomes very challenging.Most of the existing studies are based on graph neural networks that model traffic flow graphs and try to use fixed graph structure to deal with the relationship between nodes.However,due to the time-varying spatial correlation of the traffic network,there is no fixed node relationship,and these methods cannot effectively integrate the temporal and spatial features.This paper proposes a novel temporal-spatial dynamic graph convolutional network(TSADGCN).The dynamic time warping algorithm(DTW)is introduced to calculate the similarity of traffic flow sequence among network nodes in the time dimension,and the spatiotemporal graph of traffic flow is constructed to capture the spatiotemporal characteristics and dependencies of traffic flow.By combining graph attention network and time attention network,a spatiotemporal convolution block is constructed to capture spatiotemporal characteristics of traffic data.Experiments on open data sets PEMSD4 and PEMSD8 show that TSADGCN has higher prediction accuracy than well-known traffic flow prediction algorithms.
基金The Science and Technology Research and Development Program Project of China Railway Group Ltd provided funding for this study(Project Nos.2020-Special-02 and 2021Special-08)。
文摘Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system. However, the traffic data's complexity and fluctuation, as well as the noise produced during collecting information and summarizing original data of traffic flow, cause large errors in the traffic flow forecasting results. This article suggests a solution to the above mentioned issues and proposes a fully connected time-gated neural network based on wavelet reconstruction(WT-FCTGN). To eliminate the potential noise and strengthen the potential traffic trend in the data, we adopt the methods of wavelet reconstruction and periodic data introduction to preprocess the data. The model introduces fully connected time-series blocks to model all the information including time sequence information and fluctuation information in the flow of traffic, and establishes the time gate block to comprehend the periodic characteristics of the flow of traffic and predict its flow. The performance of the WT-FCTGN model is validated on the public Pe MS data set. The experimental results show that the WT-FCTGN model has higher accuracy, and its mean absolute error(MAE), mean absolute percentage error(MAPE) and root mean square error(RMSE) are obviously lower than those of the other algorithms. The robust experimental results prove that the WT-FCTGN model has good anti-noise ability.
基金the National Natural Science Foundation of China under Grant No.62272087Science and Technology Planning Project of Sichuan Province under Grant No.2023YFG0161.
文摘Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel efficiency.To improve prediction accuracy,a crucial issue is how to model spatiotemporal dependency in urban traffic data.In recent years,many studies have adopted spatiotemporal neural networks to extract key information from traffic data.However,most models ignore the semantic spatial similarity between long-distance areas when mining spatial dependency.They also ignore the impact of predicted time steps on the next unpredicted time step for making long-term predictions.Moreover,these models lack a comprehensive data embedding process to represent complex spatiotemporal dependency.This paper proposes a multi-scale persistent spatiotemporal transformer(MSPSTT)model to perform accurate long-term traffic flow prediction in cities.MSPSTT adopts an encoder-decoder structure and incorporates temporal,periodic,and spatial features to fully embed urban traffic data to address these issues.The model consists of a spatiotemporal encoder and a spatiotemporal decoder,which rely on temporal,geospatial,and semantic space multi-head attention modules to dynamically extract temporal,geospatial,and semantic characteristics.The spatiotemporal decoder combines the context information provided by the encoder,integrates the predicted time step information,and is iteratively updated to learn the correlation between different time steps in the broader time range to improve the model’s accuracy for long-term prediction.Experiments on four public transportation datasets demonstrate that MSPSTT outperforms the existing models by up to 9.5%on three common metrics.
文摘This paper introduces an innovative approach to the synchronized demand-capacity balance with special focus on sector capacity uncertainty within a centrally controlled collaborative air traffic flow management(ATFM)framework.Further with previous study,the uncertainty in capacity is considered as a non-negligible issue regarding multiple reasons,like the impact of weather,the strike of air traffic controllers(ATCOs),the military use of airspace and the spatiotemporal distribution of nonscheduled flights,etc.These recessive factors affect the outcome of traffic flow optimization.In this research,the focus is placed on the impact of sector capacity uncertainty on demand and capacity balancing(DCB)optimization and ATFM,and multiple options,such as delay assignment and rerouting,are intended for regulating the traffic flow.A scenario optimization method for sector capacity in the presence of uncertainties is used to find the approximately optimal solution.The results show that the proposed approach can achieve better demand and capacity balancing and determine perfect integer solutions to ATFM problems,solving large-scale instances(24 h on seven capacity scenarios,with 6255 flights and 8949 trajectories)in 5-15 min.To the best of our knowledge,our experiment is the first to tackle large-scale instances of stochastic ATFM problems within the collaborative ATFM framework.
基金Supported by the Key R&D Program of Gansu Province(No.23YFGA0063)the National Natural Science Foundation of China(No.62363022,61663021)+1 种基金the Natural Science Foundation of Gansu Province(No.22JR5RA226,23JRRA886)the Gansu Provincial De-partment of Education:Industrial Support Plan Project(No.2023CYZC-35).
文摘Traffic flow forecasting plays a crucial role and is the key technology to realize dynamic traffic guidance and active traffic control in intelligent traffic systems(ITS).Aiming at the complex local and global spatial-temporal dynamic characteristics of traffic flow,this paper proposes a new traffic flow forecasting model spatial-temporal attention graph neural network(STA-GNN)by combining at-tention mechanism(AM)and spatial-temporal convolutional network.The model learns the hidden dynamic local spatial correlations of the traffic network by combining the dynamic adjacency matrix constructed by the graph learning layer with the graph convolutional network(GCN).The local tem-poral correlations of traffic flow at different scales are extracted by stacking multiple convolutional kernels in temporal convolutional network(TCN).And the global spatial-temporal dependencies of long-time sequences of traffic flow are captured by the spatial-temporal attention mechanism(STAtt),which enhances the global spatial-temporal modeling and the representational ability of model.The experimental results on two datasets,METR-LA and PEMS-BAY,show the proposed STA-GNN model outperforms the common baseline models in forecasting accuracy.
文摘Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion within elevator systems. Many passengers experience dissatisfaction with prolonged wait times, leading to impatience and frustration among building occupants. The widespread adoption of neural networks and deep learning technologies across various fields and industries represents a significant paradigm shift, and unlocking new avenues for innovation and advancement. These cutting-edge technologies offer unprecedented opportunities to address complex challenges and optimize processes in diverse domains. In this study, LSTM (Long Short-Term Memory) network technology is leveraged to analyze elevator traffic flow within a typical office building. By harnessing the predictive capabilities of LSTM, the research aims to contribute to advancements in elevator group control design, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments. The findings of this research have the potential to reference the development of intelligent elevator management systems, capable of dynamically adapting to fluctuating passenger demand and optimizing elevator usage in real-time. By enhancing the efficiency and functionality of vertical transportation systems, the research contributes to creating more sustainable, accessible, and user-friendly living environments for individuals across diverse demographics.
