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
A traveling wave solution to the Aw-Rascle traffic flow model that includes the relaxation and diffusion terms is investigated. The model can be approximated by the well-known Kortweg-de Vries （KdV） equation. A nume...A traveling wave solution to the Aw-Rascle traffic flow model that includes the relaxation and diffusion terms is investigated. The model can be approximated by the well-known Kortweg-de Vries （KdV） equation. A numerical simulation is conducted by the first-order accurate Lax-Friedrichs scheme, which is known for its ability to capture the entropy solution to hyperbolic conservation laws. Periodic boundary conditions are applied to simulate a lengthy propagation, where the profile of the derived KdV solution is taken as the initial condition to observe the change of the profile. The simulation shows good agreement between the approximated KdV solution and the numerical solution.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
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
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(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 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
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
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
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
Based on the gap acceptance theory, the mixed traffic flow composed of r representative types of vehicles 1, 2,…, r vehicles is analyzed with probability theory. Capacity model of the minor mixed traffic flow ...Based on the gap acceptance theory, the mixed traffic flow composed of r representative types of vehicles 1, 2,…, r vehicles is analyzed with probability theory. Capacity model of the minor mixed traffic flow crossing m major lanes with M3 distributed headway on the unsignalized intersection is set up, and it is an extension of capacity model for one minor lane vehicle type crossing one major lane traffic flow.展开更多
Aiming at the real-time fluctuation and nonlinear characteristics of the expressway short-term traffic flow forecasting the parameter projection pursuit regression PPPR model is applied to forecast the expressway traf...Aiming at the real-time fluctuation and nonlinear characteristics of the expressway short-term traffic flow forecasting the parameter projection pursuit regression PPPR model is applied to forecast the expressway traffic flow where the orthogonal Hermite polynomial is used to fit the ridge functions and the least square method is employed to determine the polynomial weight coefficient c.In order to efficiently optimize the projection direction a and the number M of ridge functions of the PPPR model the chaos cloud particle swarm optimization CCPSO algorithm is applied to optimize the parameters. The CCPSO-PPPR hybrid optimization model for expressway short-term traffic flow forecasting is established in which the CCPSO algorithm is used to optimize the optimal projection direction a in the inner layer while the number M of ridge functions is optimized in the outer layer.Traffic volume weather factors and travel date of the previous several time intervals of the road section are taken as the input influencing factors. Example forecasting and model comparison results indicate that the proposed model can obtain a better forecasting effect and its absolute error is controlled within [-6,6] which can meet the application requirements of expressway traffic flow forecasting.展开更多
In order to describe the compressibility of traffic flows and determine the compression factors, the Mach number of gas dynamics is introduced, and the concept and the formula of the compression factor are obtained. A...In order to describe the compressibility of traffic flows and determine the compression factors, the Mach number of gas dynamics is introduced, and the concept and the formula of the compression factor are obtained. According to the concept of the compression factor and its differential equation, a stop-wave model is built. The theoretical value and the observed one are obtained by the survey data in Changchun city. The relative error between the two values is 20. 3%. The accuracy is improved 39% compared with the result from the traditional stop-wave model. The results show that the traffic flow is compressible, and the methods of research on gas compressibility is also applicable to the traffic flow. The stop-wave model obtained by the compression factor can better describe the phenomenon of the stop wave at a signalized intersection when compared with the traditional stop-wave model.展开更多
Based on Gaussian mixture models（GMM）, speed, flow and occupancy are used together in the cluster analysis of traffic flow data. Compared with other clustering and sorting techniques, as a structural model, the GMM ...Based on Gaussian mixture models（GMM）, speed, flow and occupancy are used together in the cluster analysis of traffic flow data. Compared with other clustering and sorting techniques, as a structural model, the GMM is suitable for various kinds of traffic flow parameters. Gap statistics and domain knowledge of traffic flow are used to determine a proper number of clusters. The expectation-maximization （E-M） algorithm is used to estimate parameters of the GMM model. The clustered traffic flow pattems are then analyzed statistically and utilized for designing maximum likelihood classifiers for grouping real-time traffic flow data when new observations become available. Clustering analysis and pattern recognition can also be used to cluster and classify dynamic traffic flow patterns for freeway on-ramp and off-ramp weaving sections as well as for other facilities or things involving the concept of level of service, such as airports, parking lots, intersections, interrupted-flow pedestrian facilities, etc.展开更多
The effect of the aggregation interval on vehicular traffic flow heteroscedasticity is investigated using real-world traffic flow data collected from the motorway system in the United Kingdom. 30 traffic flow series a...The effect of the aggregation interval on vehicular traffic flow heteroscedasticity is investigated using real-world traffic flow data collected from the motorway system in the United Kingdom. 30 traffic flow series are generated using 30 aggregation intervals ranging from 1 to 30 min at 1 min increment, and autoregressive integrated moving average （AR/MA） models are constructed and applied in these series, generating 30 residual series. Through applying the portmanteau Q-test and the Lagrange multiplier （LM） test in the residual series from the ARIMA models, the heteroscedasticity in traffic flow series is investigated. Empirical results show that traffic flow is heteroscedastJc across these selected aggregation intervals, and longer aggregation intervals tend to cancel out the noise in the traffic flow data and hence reduce the heteroscedasticity in traffic flow series. The above findings can be utilized in the development of reliable and robust traffic management and control systems.展开更多
Two methods based on a slight modification of the regular traffic assignmentalgorithms are proposed to directly compute turn flows instead of estimating them from link flows orobtaining them by expanding the networks....Two methods based on a slight modification of the regular traffic assignmentalgorithms are proposed to directly compute turn flows instead of estimating them from link flows orobtaining them by expanding the networks. The first one is designed on the path-turn incidencerelationship, and it is similar to the computational procedure of link flows. It applies to thetraffic assignment algorithms that can provide detailed path structures. The second utilizes thelink-turn incidence relationship and the conservation of flow on links, a law deriving from thisrelationship. It is actually an improved version of Dial's logit assignment algorithm. The proposedapproaches can avoid the shortcomings both of the estimation methods, e. g. Furness's model andFrator's model, and of the network-expanding method in precision, stability and computation scale.Finally, they are validated by numerical examples.展开更多
In order to solve serious urban transport problems, according to the proved chaotic characteristic of traffic flow, a non linear chaotic model to analyze the time series of traffic flow is proposed. This model recons...In order to solve serious urban transport problems, according to the proved chaotic characteristic of traffic flow, a non linear chaotic model to analyze the time series of traffic flow is proposed. This model reconstructs the time series of traffic flow in the phase space firstly, and the correlative information in the traffic flow is extracted richly, on the basis of it, a predicted equation for the reconstructed information is established by using chaotic theory, and for the purpose of obtaining the optimal predicted results, recognition and optimization to the model parameters are done by using genetic algorithm. Practical prediction research of urban traffic flow shows that this model has famous predicted precision, and it can provide exact reference for urban traffic programming and control.展开更多
In this paper, we study the continuum modeling of traffic dynamics for two-lane freeways. A new dynamics model is proposed, which contains the speed gradient-based momentum equations derived from a car-following theor...In this paper, we study the continuum modeling of traffic dynamics for two-lane freeways. A new dynamics model is proposed, which contains the speed gradient-based momentum equations derived from a car-following theory suited to two-lane traffic flow. The conditions for securing the linear stability of the new model are presented. Numerical tests are can＇ied out and some nonequilibrium phenomena are observed, such as small disturbance instability, stop-and-go waves, local clusters and phase transition.展开更多
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Project supported by the National Natural Science Foundation of China (Nos. 11072141 and 11272199)the National Basic Research Program of China (No. 2012CB725404)+1 种基金the University Research Committee, HKU SPACE Research FundFaculty of Engineering Top-up Grant of the University of Hong Kong (No. 201007176059)
文摘A traveling wave solution to the Aw-Rascle traffic flow model that includes the relaxation and diffusion terms is investigated. The model can be approximated by the well-known Kortweg-de Vries （KdV） equation. A numerical simulation is conducted by the first-order accurate Lax-Friedrichs scheme, which is known for its ability to capture the entropy solution to hyperbolic conservation laws. Periodic boundary conditions are applied to simulate a lengthy propagation, where the profile of the derived KdV solution is taken as the initial condition to observe the change of the profile. The simulation shows good agreement between the approximated KdV solution and the numerical solution.
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(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 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.
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 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(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.
文摘Based on the gap acceptance theory, the mixed traffic flow composed of r representative types of vehicles 1, 2,…, r vehicles is analyzed with probability theory. Capacity model of the minor mixed traffic flow crossing m major lanes with M3 distributed headway on the unsignalized intersection is set up, and it is an extension of capacity model for one minor lane vehicle type crossing one major lane traffic flow.
基金The National Natural Science Foundation of China(No.71101014,50679008)Specialized Research Fund for the Doctoral Program of Higher Education(No.200801411105)the Science and Technology Project of the Department of Communications of Henan Province(No.2010D107-4)
文摘Aiming at the real-time fluctuation and nonlinear characteristics of the expressway short-term traffic flow forecasting the parameter projection pursuit regression PPPR model is applied to forecast the expressway traffic flow where the orthogonal Hermite polynomial is used to fit the ridge functions and the least square method is employed to determine the polynomial weight coefficient c.In order to efficiently optimize the projection direction a and the number M of ridge functions of the PPPR model the chaos cloud particle swarm optimization CCPSO algorithm is applied to optimize the parameters. The CCPSO-PPPR hybrid optimization model for expressway short-term traffic flow forecasting is established in which the CCPSO algorithm is used to optimize the optimal projection direction a in the inner layer while the number M of ridge functions is optimized in the outer layer.Traffic volume weather factors and travel date of the previous several time intervals of the road section are taken as the input influencing factors. Example forecasting and model comparison results indicate that the proposed model can obtain a better forecasting effect and its absolute error is controlled within [-6,6] which can meet the application requirements of expressway traffic flow forecasting.
基金The National Basic Research Program of China (973Pro-gram)(No.2006CB705505)
文摘In order to describe the compressibility of traffic flows and determine the compression factors, the Mach number of gas dynamics is introduced, and the concept and the formula of the compression factor are obtained. According to the concept of the compression factor and its differential equation, a stop-wave model is built. The theoretical value and the observed one are obtained by the survey data in Changchun city. The relative error between the two values is 20. 3%. The accuracy is improved 39% compared with the result from the traditional stop-wave model. The results show that the traffic flow is compressible, and the methods of research on gas compressibility is also applicable to the traffic flow. The stop-wave model obtained by the compression factor can better describe the phenomenon of the stop wave at a signalized intersection when compared with the traditional stop-wave model.
基金The US National Science Foundation (No. CMMI-0408390,CMMI-0644552)the American Chemical Society Petroleum Research Foundation (No.PRF-44468-G9)+3 种基金the Research Fellowship for International Young Scientists (No.51050110143)the Fok Ying-Tong Education Foundation (No.114024)the Natural Science Foundation of Jiangsu Province (No.BK2009015)the Postdoctoral Science Foundation of Jiangsu Province (No.0901005C)
文摘Based on Gaussian mixture models（GMM）, speed, flow and occupancy are used together in the cluster analysis of traffic flow data. Compared with other clustering and sorting techniques, as a structural model, the GMM is suitable for various kinds of traffic flow parameters. Gap statistics and domain knowledge of traffic flow are used to determine a proper number of clusters. The expectation-maximization （E-M） algorithm is used to estimate parameters of the GMM model. The clustered traffic flow pattems are then analyzed statistically and utilized for designing maximum likelihood classifiers for grouping real-time traffic flow data when new observations become available. Clustering analysis and pattern recognition can also be used to cluster and classify dynamic traffic flow patterns for freeway on-ramp and off-ramp weaving sections as well as for other facilities or things involving the concept of level of service, such as airports, parking lots, intersections, interrupted-flow pedestrian facilities, etc.
基金The National Natural Science Foundation of China(No.71101025)the National Key Technology R&D Program of China during the 12th Five-Year Plan Period(No.2011BAK21B01)+1 种基金the Doctoral Programs Foundation of the Ministry of Education of China(No.20100092110037)the Fundamental Research Funds for the Central Universities
文摘The effect of the aggregation interval on vehicular traffic flow heteroscedasticity is investigated using real-world traffic flow data collected from the motorway system in the United Kingdom. 30 traffic flow series are generated using 30 aggregation intervals ranging from 1 to 30 min at 1 min increment, and autoregressive integrated moving average （AR/MA） models are constructed and applied in these series, generating 30 residual series. Through applying the portmanteau Q-test and the Lagrange multiplier （LM） test in the residual series from the ARIMA models, the heteroscedasticity in traffic flow series is investigated. Empirical results show that traffic flow is heteroscedastJc across these selected aggregation intervals, and longer aggregation intervals tend to cancel out the noise in the traffic flow data and hence reduce the heteroscedasticity in traffic flow series. The above findings can be utilized in the development of reliable and robust traffic management and control systems.
文摘Two methods based on a slight modification of the regular traffic assignmentalgorithms are proposed to directly compute turn flows instead of estimating them from link flows orobtaining them by expanding the networks. The first one is designed on the path-turn incidencerelationship, and it is similar to the computational procedure of link flows. It applies to thetraffic assignment algorithms that can provide detailed path structures. The second utilizes thelink-turn incidence relationship and the conservation of flow on links, a law deriving from thisrelationship. It is actually an improved version of Dial's logit assignment algorithm. The proposedapproaches can avoid the shortcomings both of the estimation methods, e. g. Furness's model andFrator's model, and of the network-expanding method in precision, stability and computation scale.Finally, they are validated by numerical examples.
文摘In order to solve serious urban transport problems, according to the proved chaotic characteristic of traffic flow, a non linear chaotic model to analyze the time series of traffic flow is proposed. This model reconstructs the time series of traffic flow in the phase space firstly, and the correlative information in the traffic flow is extracted richly, on the basis of it, a predicted equation for the reconstructed information is established by using chaotic theory, and for the purpose of obtaining the optimal predicted results, recognition and optimization to the model parameters are done by using genetic algorithm. Practical prediction research of urban traffic flow shows that this model has famous predicted precision, and it can provide exact reference for urban traffic programming and control.
文摘In this paper, we study the continuum modeling of traffic dynamics for two-lane freeways. A new dynamics model is proposed, which contains the speed gradient-based momentum equations derived from a car-following theory suited to two-lane traffic flow. The conditions for securing the linear stability of the new model are presented. Numerical tests are can＇ied out and some nonequilibrium phenomena are observed, such as small disturbance instability, stop-and-go waves, local clusters and phase transition.
