A lattice Boltzmann model with 5-bit lattice for traffic flows is proposed. Using the Chapman-Enskog expansion and multi-scale technique, we obtain the higher-order moments of equilibrium distribution function. A simp...A lattice Boltzmann model with 5-bit lattice for traffic flows is proposed. Using the Chapman-Enskog expansion and multi-scale technique, we obtain the higher-order moments of equilibrium distribution function. A simple traffic light problem is simulated by using the present lattice Boltzmann model, and the result agrees well with analytical solution.展开更多
Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems(ITS),playing a pivotal role in mitigating congestion,enhancing route optimization,and improving the utilization efficie...Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems(ITS),playing a pivotal role in mitigating congestion,enhancing route optimization,and improving the utilization efficiency of roadway infrastructure.However,existingmethods struggle in complex traffic scenarios due to static spatio-temporal embedding,restricted multi-scale temporal modeling,and weak representation of local spatial interactions.This study proposes Bi-STAT+,an enhanced bidirectional spatio-temporal attention framework to address existing limitations through three principal contributions:(1)an adaptive spatio-temporal embedding module that dynamically adjusts embeddings to capture complex traffic variations;(2)frequency-domain analysis in the temporal dimension for simultaneous high-frequency details and low-frequency trend extraction;and(3)an agent attention mechanism in the spatial dimension that enhances local feature extraction through dynamic weight allocation.Extensive experiments were performed on four distinct datasets,including two publicly benchmark datasets(PEMS04 and PEMS08)and two private datasets collected from Baotou and Chengdu,China.The results demonstrate that Bi-STAT+consistently outperforms existing methods in terms of MAE,RMSE,and MAPE,while maintaining strong robustness against missing data and noise.Furthermore,the results highlight that prediction accuracy improves significantly with higher sampling rates,providing crucial insights for optimizing real-world deployment scenarios.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
Long-term traffic flow prediction is a crucial component of intelligent transportation systems within intelligent networks,requiring predictive models that balance accuracy with low-latency and lightweight computation...Long-term traffic flow prediction is a crucial component of intelligent transportation systems within intelligent networks,requiring predictive models that balance accuracy with low-latency and lightweight computation to optimize trafficmanagement and enhance urban mobility and sustainability.However,traditional predictivemodels struggle to capture long-term temporal dependencies and are computationally intensive,limiting their practicality in real-time.Moreover,many approaches overlook the periodic characteristics inherent in traffic data,further impacting performance.To address these challenges,we introduce ST-MambaGCN,a State-Space-Based Spatio-Temporal Graph Convolution Network.Unlike conventionalmodels,ST-MambaGCN replaces the temporal attention layer withMamba,a state-space model that efficiently captures long-term dependencies with near-linear computational complexity.The model combines Chebyshev polynomial-based graph convolutional networks(GCN)to explore spatial correlations.Additionally,we incorporate a multi-temporal feature capture mechanism,where the final integrated features are generated through the Hadamard product based on learnable parameters.This mechanism explicitly models shortterm,daily,and weekly traffic patterns to enhance the network’s awareness of traffic periodicity.Extensive experiments on the PeMS04 and PeMS08 datasets demonstrate that ST-MambaGCN significantly outperforms existing benchmarks,offering substantial improvements in both prediction accuracy and computational efficiency for long-term traffic flow prediction.展开更多
In order to estimate traffic flow a Bayesian network BN model using prior link flows is proposed.This model sets link flows as parents of the origin-destination OD flows. Under normal distribution assumptions the mode...In order to estimate traffic flow a Bayesian network BN model using prior link flows is proposed.This model sets link flows as parents of the origin-destination OD flows. Under normal distribution assumptions the model considers the level of total traffic flow the variability of link flows and the violation of the conservation law.Using prior link flows the prior distribution of all the variables is determined. By updating some observed link flows the posterior distribution is given.The variances of the posterior distribution normally decrease with the progressive update of the link flows. Based on the posterior distribution point estimations and the corresponding probability intervals are provided. To remove inconsistencies in OD matrices estimation and traffic assignment a combined BN and stochastic user equilibrium model is proposed in which the equilibrium solution is obtained through iterations.Results of the numerical example demonstrate the efficiency of the proposed BN model and the combined method.展开更多
The prediction of regional traffic flows is important for traffic control and management in an intelligent traffic system.With the help of deep neural networks,the convolutional neural network or residual neural netwo...The prediction of regional traffic flows is important for traffic control and management in an intelligent traffic system.With the help of deep neural networks,the convolutional neural network or residual neural network,which can be applied only to regular grids,is adopted to capture the spatial dependence for flow prediction.However,the obtained regions are always irregular considering the road network and administrative boundaries;thus,dividing the city into grids is inaccurate for prediction.In this paper,we propose a new model based on multi-graph convolutional network and gated recurrent unit(MGCN-GRU)to predict traffic flows for irregular regions.Specifically,we first construct heterogeneous inter-region graphs for a city to reflect the rela-tionships among regions.In each graph,nodes represent the irregular regions and edges represent the relationship types between regions.Then,we propose a multi-graph convolutional network to fuse different inter-region graphs and additional attributes.The GRU is further used to capture the temporal dependence and to predict future traffic flows.Experimental results based on three real-world large-scale datasets(public bicycle system dataset,taxi dataset,and dockless bike-sharing dataset)show that our MGCN-GRU model outperforms a variety of existing methods.展开更多
Connected and automated vehicles(CAVs)are expected to reshape traffic flow dynamics and present new challenges and opportunities for traffic flow modeling.While numerous studies have proposed optimal modeling and cont...Connected and automated vehicles(CAVs)are expected to reshape traffic flow dynamics and present new challenges and opportunities for traffic flow modeling.While numerous studies have proposed optimal modeling and control strategies for CAVs with various objectives(e.g.,traffic efficiency and safety),there are uncertainties about the flow dynamics of CAVs in real-world traffic.The uncertainties are especially amplified for mixed traffic flows,consisting of CAVs and human-driven vehicles,where the implications can be significant from the continuum-modeling perspective,which aims to capture macroscopic traffic flow dynamics based on hyperbolic systems of partial differential equations.This paper aims to highlight and discuss some essential problems in continuum modeling of real-world freeway traffic flows in the era of CAVs.We first provide a select review of some existing continuum models for conventional human-driven traffic as well as the recent attempts for incorporating CAVs into the continuum-modeling framework.Wherever applicable,we provide new insights about the properties of existing models and revisit their implications for traffic flows of CAVs using recent empirical observations with CAVs and the previous discussions and debates in the literature.The paper then discusses some major problems inherent to continuum modeling of real-world(mixed)CAV traffic flows modeling by distinguishing between two major research directions:(a)modeling for explaining purposes,where making reproducible inferences about the physical aspects of macroscopic properties is of the primary interest,and(b)modeling for practical purposes,in which the focus is on the reliable predictions for operation and control.The paper proposes some potential solutions in each research direction and recommends some future research topics.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
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
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
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
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
Traffic flow prediction is a crucial element of intelligent transportation systems.However,accu-rate traffic flow prediction is quite challenging because of its highly nonlinear,complex,and dynam-ic characteristics.To...Traffic flow prediction is a crucial element of intelligent transportation systems.However,accu-rate traffic flow prediction is quite challenging because of its highly nonlinear,complex,and dynam-ic characteristics.To address the difficulties in simultaneously capturing local and global dynamic spatiotemporal correlations in traffic flow,as well as the high time complexity of existing models,a multi-head flow attention-based local-global dynamic hypergraph convolution(MFA-LGDHC)pre-diction model is proposed.which consists of multi-head flow attention(MHFA)mechanism,graph convolution network(GCN),and local-global dynamic hypergraph convolution(LGHC).MHFA is utilized to extract the time dependency of traffic flow and reduce the time complexity of the model.GCN is employed to catch the spatial dependency of traffic flow.LGHC utilizes down-sampling con-volution and isometric convolution to capture the local and global spatial dependencies of traffic flow.And dynamic hypergraph convolution is used to model the dynamic higher-order relationships of the traffic road network.Experimental results indicate that the MFA-LGDHC model outperforms current popular baseline models and exhibits good prediction performance.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
Precise traffic flow forecasting is essential for mitigating urban traffic congestion.However,it is difficult for existing methods to adequately capture the dynamic spatio-temporal characteristics and multiscale tempo...Precise traffic flow forecasting is essential for mitigating urban traffic congestion.However,it is difficult for existing methods to adequately capture the dynamic spatio-temporal characteristics and multiscale temporal dependencies of traffic flow.A traffic flow prediction model with multiscale temporal awareness and graph diffusion attention networks(MT-GDAN)is proposed to address these issues.Specifically,a graph diffusion attention module is constructed,which dynamically adjusts and calculates the weights of neighboring nodes in the graph structure using a random graph attention network(GAT)and captures the spatial characteristics of hidden nodes through an adaptive adjacency matrix,thus better exploiting the dynamic spatio-temporal properties of traffic flow.Secondly,a multiscale isometric convolutional network and bi-level routing attention are used to construct a multiscale temporal awareness module.The former extracts local information of traffic flow segments by convolution with different sizes of convolution kernels and then introduces isometric convolution to obtain the global temporal relationship between local features of traffic flow segments;the latter filters irrelevant spatio-temporal features at a coarse regional level and focuses locally on key points to more accurately capture the multiscale temporal dependencies of traffic flows.Experimental results reveal that the MT-GDAN model surpasses the mainstream baseline model in terms of forecasting accuracy and exhibits good prediction performance.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
Accelerating urbanization and the rapid development of intelligent transportation systems have rendered shortterm traffic flow prediction an important research field.Accurate prediction of traffic flow is beneficial f...Accelerating urbanization and the rapid development of intelligent transportation systems have rendered shortterm traffic flow prediction an important research field.Accurate prediction of traffic flow is beneficial for the optimization of traffic planning,improvement of road utilization,reduction of traffic congestion,and reduction in the incidence of traffic accidents.However,data pertaining to traffic flow are typically influenced by a multitude of factors,resulting in data that exhibit a considerable degree of nonlinearity and complexity.To address the issue of noise in raw traffic flow data,this study proposes a hybrid model that combines variational mode decomposition(VMD),a bidirectional long short-term memory network(BiLSTM),and a gated recurrent unit(GRU)for short-term traffic flow prediction.To validate the effectiveness of the model,an experimental validation was conducted based on traffic flow data from UK highways,and the performance of the model was compared with common benchmark models.The experimental results demonstrate that the proposed method yields superior prediction results in terms of mean absolute error,coefficient of determination,and root-mean-square error compared to existing prediction techniques,thereby substantiating its efficacy in short-term traffic flow prediction.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
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
Short⁃term traffic flow prediction plays a crucial role in the planning of intelligent transportation systems.Nowadays,there is a large amount of traffic flow data generated from the monitoring devices of urban road n...Short⁃term traffic flow prediction plays a crucial role in the planning of intelligent transportation systems.Nowadays,there is a large amount of traffic flow data generated from the monitoring devices of urban road networks,which contains road network traffic information with high application value.In this study,an improved spatio⁃temporal attention transformer model(ISTA⁃transformer model)is proposed to provide a more accurate method for predicting multi⁃step short⁃term traffic flow based on monitoring data.By embedding a temporal attention layer and a spatial attention layer in the model,the model learns the relationship between traffic flows at different time intervals and different geographic locations,and realizes more accurate multi⁃step short⁃time flow prediction.Finally,we validate the superiority of the model with monitoring data spanning 15 days from 620 monitoring points in Qingdao,China.In the four time steps of prediction,the MAPE(Mean Absolute Percentage Error)values of ISTA⁃transformers prediction results are 0.22,0.29,0.37,and 0.38,respectively,and its prediction accuracy is usually better than that of six baseline models(Transformer,GRU,CNN,LSTM,Seq2Seq and LightGBM),which indicates that the proposed model in this paper always has a better ability to explain the prediction results with the time steps in the multi⁃step prediction.展开更多
文摘A lattice Boltzmann model with 5-bit lattice for traffic flows is proposed. Using the Chapman-Enskog expansion and multi-scale technique, we obtain the higher-order moments of equilibrium distribution function. A simple traffic light problem is simulated by using the present lattice Boltzmann model, and the result agrees well with analytical solution.
基金partly supported by the Youth Foundation of the Inner Mongolia Natural Science Foundation[grant number 2024QN06017 and 2025MS06022]the Basic Scientific Research Business Fee Project for Universities in Inner Mongolia[grant numbers 2023XKJX019 and 2023XKJX024]the Central Guidance on Local Science and Technology Development Fund through[grant number 2024ZY0084].
文摘Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems(ITS),playing a pivotal role in mitigating congestion,enhancing route optimization,and improving the utilization efficiency of roadway infrastructure.However,existingmethods struggle in complex traffic scenarios due to static spatio-temporal embedding,restricted multi-scale temporal modeling,and weak representation of local spatial interactions.This study proposes Bi-STAT+,an enhanced bidirectional spatio-temporal attention framework to address existing limitations through three principal contributions:(1)an adaptive spatio-temporal embedding module that dynamically adjusts embeddings to capture complex traffic variations;(2)frequency-domain analysis in the temporal dimension for simultaneous high-frequency details and low-frequency trend extraction;and(3)an agent attention mechanism in the spatial dimension that enhances local feature extraction through dynamic weight allocation.Extensive experiments were performed on four distinct datasets,including two publicly benchmark datasets(PEMS04 and PEMS08)and two private datasets collected from Baotou and Chengdu,China.The results demonstrate that Bi-STAT+consistently outperforms existing methods in terms of MAE,RMSE,and MAPE,while maintaining strong robustness against missing data and noise.Furthermore,the results highlight that prediction accuracy improves significantly with higher sampling rates,providing crucial insights for optimizing real-world deployment scenarios.
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supported byNationalNatural Science Foundation of China,GrantNo.62402046the Beijing Forestry University Science and Technology Innovation Project under Grant No.BLX202358.
文摘Long-term traffic flow prediction is a crucial component of intelligent transportation systems within intelligent networks,requiring predictive models that balance accuracy with low-latency and lightweight computation to optimize trafficmanagement and enhance urban mobility and sustainability.However,traditional predictivemodels struggle to capture long-term temporal dependencies and are computationally intensive,limiting their practicality in real-time.Moreover,many approaches overlook the periodic characteristics inherent in traffic data,further impacting performance.To address these challenges,we introduce ST-MambaGCN,a State-Space-Based Spatio-Temporal Graph Convolution Network.Unlike conventionalmodels,ST-MambaGCN replaces the temporal attention layer withMamba,a state-space model that efficiently captures long-term dependencies with near-linear computational complexity.The model combines Chebyshev polynomial-based graph convolutional networks(GCN)to explore spatial correlations.Additionally,we incorporate a multi-temporal feature capture mechanism,where the final integrated features are generated through the Hadamard product based on learnable parameters.This mechanism explicitly models shortterm,daily,and weekly traffic patterns to enhance the network’s awareness of traffic periodicity.Extensive experiments on the PeMS04 and PeMS08 datasets demonstrate that ST-MambaGCN significantly outperforms existing benchmarks,offering substantial improvements in both prediction accuracy and computational efficiency for long-term traffic flow prediction.
基金The National Natural Science Foundation of China(No.51078085,51178110)
文摘In order to estimate traffic flow a Bayesian network BN model using prior link flows is proposed.This model sets link flows as parents of the origin-destination OD flows. Under normal distribution assumptions the model considers the level of total traffic flow the variability of link flows and the violation of the conservation law.Using prior link flows the prior distribution of all the variables is determined. By updating some observed link flows the posterior distribution is given.The variances of the posterior distribution normally decrease with the progressive update of the link flows. Based on the posterior distribution point estimations and the corresponding probability intervals are provided. To remove inconsistencies in OD matrices estimation and traffic assignment a combined BN and stochastic user equilibrium model is proposed in which the equilibrium solution is obtained through iterations.Results of the numerical example demonstrate the efficiency of the proposed BN model and the combined method.
基金the National Natural Science Foundation of China(No.61903109)the Zhejiang Provincial Natural Science Foundation of China(No.LY19F030021)。
文摘The prediction of regional traffic flows is important for traffic control and management in an intelligent traffic system.With the help of deep neural networks,the convolutional neural network or residual neural network,which can be applied only to regular grids,is adopted to capture the spatial dependence for flow prediction.However,the obtained regions are always irregular considering the road network and administrative boundaries;thus,dividing the city into grids is inaccurate for prediction.In this paper,we propose a new model based on multi-graph convolutional network and gated recurrent unit(MGCN-GRU)to predict traffic flows for irregular regions.Specifically,we first construct heterogeneous inter-region graphs for a city to reflect the rela-tionships among regions.In each graph,nodes represent the irregular regions and edges represent the relationship types between regions.Then,we propose a multi-graph convolutional network to fuse different inter-region graphs and additional attributes.The GRU is further used to capture the temporal dependence and to predict future traffic flows.Experimental results based on three real-world large-scale datasets(public bicycle system dataset,taxi dataset,and dockless bike-sharing dataset)show that our MGCN-GRU model outperforms a variety of existing methods.
基金partially funded by the Australian Research Council(ARC)through the Discovery Project(DP210102970)Dr.Zuduo Zheng's Discovery Early Career Researcher Award(DECRADE160100449).
文摘Connected and automated vehicles(CAVs)are expected to reshape traffic flow dynamics and present new challenges and opportunities for traffic flow modeling.While numerous studies have proposed optimal modeling and control strategies for CAVs with various objectives(e.g.,traffic efficiency and safety),there are uncertainties about the flow dynamics of CAVs in real-world traffic.The uncertainties are especially amplified for mixed traffic flows,consisting of CAVs and human-driven vehicles,where the implications can be significant from the continuum-modeling perspective,which aims to capture macroscopic traffic flow dynamics based on hyperbolic systems of partial differential equations.This paper aims to highlight and discuss some essential problems in continuum modeling of real-world freeway traffic flows in the era of CAVs.We first provide a select review of some existing continuum models for conventional human-driven traffic as well as the recent attempts for incorporating CAVs into the continuum-modeling framework.Wherever applicable,we provide new insights about the properties of existing models and revisit their implications for traffic flows of CAVs using recent empirical observations with CAVs and the previous discussions and debates in the literature.The paper then discusses some major problems inherent to continuum modeling of real-world(mixed)CAV traffic flows modeling by distinguishing between two major research directions:(a)modeling for explaining purposes,where making reproducible inferences about the physical aspects of macroscopic properties is of the primary interest,and(b)modeling for practical purposes,in which the focus is on the reliable predictions for operation and control.The paper proposes some potential solutions in each research direction and recommends some future research topics.
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 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 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.
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.
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Supported by the Key R&D Program of Gansu Province(No.23YFGA0063)the Key Talent Project of Gansu Province(No.2024RCXM57,2024RCXM22)the Major Science and Technology Special Program of Gansu Province(No.25ZYJA037).
文摘Traffic flow prediction is a crucial element of intelligent transportation systems.However,accu-rate traffic flow prediction is quite challenging because of its highly nonlinear,complex,and dynam-ic characteristics.To address the difficulties in simultaneously capturing local and global dynamic spatiotemporal correlations in traffic flow,as well as the high time complexity of existing models,a multi-head flow attention-based local-global dynamic hypergraph convolution(MFA-LGDHC)pre-diction model is proposed.which consists of multi-head flow attention(MHFA)mechanism,graph convolution network(GCN),and local-global dynamic hypergraph convolution(LGHC).MHFA is utilized to extract the time dependency of traffic flow and reduce the time complexity of the model.GCN is employed to catch the spatial dependency of traffic flow.LGHC utilizes down-sampling con-volution and isometric convolution to capture the local and global spatial dependencies of traffic flow.And dynamic hypergraph convolution is used to model the dynamic higher-order relationships of the traffic road network.Experimental results indicate that the MFA-LGDHC model outperforms current popular baseline models and exhibits good prediction performance.
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 by Key R&D Program of Gansu Province(No.23YFGA0063)the Key Talent Project of Gansu Province(No.2024RCXM57,2024RCXM22)the Major Science and Technology Special Program of Gansu Province(No.25ZYJA037).
文摘Precise traffic flow forecasting is essential for mitigating urban traffic congestion.However,it is difficult for existing methods to adequately capture the dynamic spatio-temporal characteristics and multiscale temporal dependencies of traffic flow.A traffic flow prediction model with multiscale temporal awareness and graph diffusion attention networks(MT-GDAN)is proposed to address these issues.Specifically,a graph diffusion attention module is constructed,which dynamically adjusts and calculates the weights of neighboring nodes in the graph structure using a random graph attention network(GAT)and captures the spatial characteristics of hidden nodes through an adaptive adjacency matrix,thus better exploiting the dynamic spatio-temporal properties of traffic flow.Secondly,a multiscale isometric convolutional network and bi-level routing attention are used to construct a multiscale temporal awareness module.The former extracts local information of traffic flow segments by convolution with different sizes of convolution kernels and then introduces isometric convolution to obtain the global temporal relationship between local features of traffic flow segments;the latter filters irrelevant spatio-temporal features at a coarse regional level and focuses locally on key points to more accurately capture the multiscale temporal dependencies of traffic flows.Experimental results reveal that the MT-GDAN model surpasses the mainstream baseline model in terms of forecasting accuracy and exhibits good prediction performance.
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 Enterprise Innovation Consortium Project under the Major Special Science and Technology Project of Gansu Province(Grant No.22ZD6GA010).
文摘Accelerating urbanization and the rapid development of intelligent transportation systems have rendered shortterm traffic flow prediction an important research field.Accurate prediction of traffic flow is beneficial for the optimization of traffic planning,improvement of road utilization,reduction of traffic congestion,and reduction in the incidence of traffic accidents.However,data pertaining to traffic flow are typically influenced by a multitude of factors,resulting in data that exhibit a considerable degree of nonlinearity and complexity.To address the issue of noise in raw traffic flow data,this study proposes a hybrid model that combines variational mode decomposition(VMD),a bidirectional long short-term memory network(BiLSTM),and a gated recurrent unit(GRU)for short-term traffic flow prediction.To validate the effectiveness of the model,an experimental validation was conducted based on traffic flow data from UK highways,and the performance of the model was compared with common benchmark models.The experimental results demonstrate that the proposed method yields superior prediction results in terms of mean absolute error,coefficient of determination,and root-mean-square error compared to existing prediction techniques,thereby substantiating its efficacy in short-term traffic flow prediction.
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.
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.
基金Sponsored by National Key Research and Development Program of China(Grant No.2020YEB1600500).
文摘Short⁃term traffic flow prediction plays a crucial role in the planning of intelligent transportation systems.Nowadays,there is a large amount of traffic flow data generated from the monitoring devices of urban road networks,which contains road network traffic information with high application value.In this study,an improved spatio⁃temporal attention transformer model(ISTA⁃transformer model)is proposed to provide a more accurate method for predicting multi⁃step short⁃term traffic flow based on monitoring data.By embedding a temporal attention layer and a spatial attention layer in the model,the model learns the relationship between traffic flows at different time intervals and different geographic locations,and realizes more accurate multi⁃step short⁃time flow prediction.Finally,we validate the superiority of the model with monitoring data spanning 15 days from 620 monitoring points in Qingdao,China.In the four time steps of prediction,the MAPE(Mean Absolute Percentage Error)values of ISTA⁃transformers prediction results are 0.22,0.29,0.37,and 0.38,respectively,and its prediction accuracy is usually better than that of six baseline models(Transformer,GRU,CNN,LSTM,Seq2Seq and LightGBM),which indicates that the proposed model in this paper always has a better ability to explain the prediction results with the time steps in the multi⁃step prediction.
