To effectively solve the traffic data problems such as data invalidation in the process of the acquisition of road traffic states,a road traffic states estimation algorithm based on matching of the regional traffic at...To effectively solve the traffic data problems such as data invalidation in the process of the acquisition of road traffic states,a road traffic states estimation algorithm based on matching of the regional traffic attracters was proposed in this work.First of all,the road traffic running states were divided into several different modes.The concept of the regional traffic attracters of the target link was put forward for effective matching.Then,the reference sequences of characteristics of traffic running states with the contents of the target link's traffic running states and regional traffic attracters under different modes were established.In addition,the current and historical regional traffic attracters of the target link were matched through certain matching rules,and the historical traffic running states of the target link corresponding to the optimal matching were selected as the initial recovery data,which were processed with Kalman filter to obtain the final recovery data.Finally,some typical expressways in Beijing were adopted for the verification of this road traffic states estimation algorithm.The results prove that this traffic states estimation approach based on matching of the regional traffic attracters is feasible and can achieve a high accuracy.展开更多
The rapid development of 5G mobile communication and portable traffic detection technologies enhances highway transportation systems in detail and at a vehicle level. Besides the advantage of no disturbance to the reg...The rapid development of 5G mobile communication and portable traffic detection technologies enhances highway transportation systems in detail and at a vehicle level. Besides the advantage of no disturbance to the regular traffic operation, these ubiquitous sensing technologies have the potential for unprecedented data collection at any temporal and spatial position. While as a typical distributed parameter system, the freeway traffic dynamics are determined by the current system states and the boundary traffic demand-supply. Using the three-step extended Kalman filtering, this paper simultaneously estimates the real-time traffic state and the boundary flux of freeway traffic with the distributed speed detector networks organized at any location of interest. In order to assess the effectiveness of the proposed approach, a freeway segment from Interstate 80 East (I-80E) in Alameda, Emeryville, and Northern California is selected. Experimental results show that the proposed method has the potential of using only speed detecting data to monitor the state of urban freeway transportation systems without access to the traditional measurement data, such as the boundary flows.展开更多
In order to make full use of heterogeneous multi-sensor data to serve urban intelligent transportation systems, a real-time urban traffic state fusion model was proposed, named federated evidence fusion model. The mod...In order to make full use of heterogeneous multi-sensor data to serve urban intelligent transportation systems, a real-time urban traffic state fusion model was proposed, named federated evidence fusion model. The model improves conventional D-S evidence theory in temporal domain, such that it can satisfy the requirement of real-time processing and utilize traffic detection information more efficaciously. The model frame and computational procedures are given. In addition, a generalized reliability weight matrix of evidence is also presented to increase the accuracy of estimation. After that, a simulation test is presented to explain the advantage of the proposed method in comparison with conventional D-S evidence theory. Besides, the validity of the model is proven by the use of the data of loop detectors and GPS probe vehicles collected from an urban link in Shanghai. Results of the experiment show that the proposed approach can well embody and track traffic state at character level in real-time conditions.展开更多
This paper presents a microscopic traffic simulation-based method for urban traffic state estimation using Assisted Global Positioning System (A-GPS) mobile phones. In this approach, real-time location data are collec...This paper presents a microscopic traffic simulation-based method for urban traffic state estimation using Assisted Global Positioning System (A-GPS) mobile phones. In this approach, real-time location data are collected by A-GPS mobile phones to track vehicles traveling on urban roads. In addition, tracking data obtained from individual mobile probes are aggregated to provide estimations of average road link speeds along rolling time periods. Moreover, the estimated average speeds are classified to different traffic condition levels, which are prepared for displaying a real-time traffic map on mobile phones. Simulation results demonstrate the effectiveness of the proposed method, which are fundamental for the subsequent development of a system demonstrator.展开更多
Accurate traffic state estimations(TSEs)within road networks are crucial for enhancing intelligent transportation systems and developing effective traffic management strategies.Traditional TSE methods often assume hom...Accurate traffic state estimations(TSEs)within road networks are crucial for enhancing intelligent transportation systems and developing effective traffic management strategies.Traditional TSE methods often assume homogeneous traffic,where all vehicles are considered identical,which does not accurately reflect the complexities of real traffic conditions that often exhibit heterogeneous characteristics.In this study,we address the limitations of conventional models by introducing a novel TSE model designed for precise estimations of heterogeneous traffic flows.We develop a comprehensive traffic feature index system tailored for heterogeneous traffic that includes four elements:basic traffic parameters,heterogeneous vehicle speeds,heterogeneous vehicle flows,and mixed flow rates.This system aids in capturing the unique traffic characteristics of different vehicle types.Our proposed high-dimensional fuzzy TSE model,termed HiF-TSE,integrates three main processes:feature selection,which eliminates redundant traffic features using Spearman correlation coefficients;dimension reduction,which utilizes the T-distributed stochastic neighbor embedding machine learning algorithm to reduce high-dimensional traffic feature data;and FCM clustering,which applies the fuzzy C-means algorithm to classify the simplified data into distinct clusters.The HiF-TSE model significantly reduces computational demands and enhances efficiency in TSE processing.We validate our model through a real-world case study,demonstrating its ability to adapt to variations in vehicle type compositions within heterogeneous traffic and accurately represent the actual traffic state.展开更多
The state-space neural network and extended Kalman filter model is used to directly predict the optimal timing plan that corresponds to futuristic traffic conditions in real time with the purposes of avoiding the lagg...The state-space neural network and extended Kalman filter model is used to directly predict the optimal timing plan that corresponds to futuristic traffic conditions in real time with the purposes of avoiding the lagging of the signal timing plans to traffic conditions. Utilizing the traffic conditions in current and former intervals, the network topology of the state-space neural network （SSNN）, which is derived from the geometry of urban arterial routes, is used to predict the optimal timing plan corresponding to the traffic conditions in the next time interval. In order to improve the effectiveness of the SSNN, the extended Kalman filter （EKF） is proposed to train the SSNN instead of conventional approaches. Raw traffic data of the Guangzhou Road, Nanjing and the optimal signal timing plan generated by a multi-objective optimization genetic algorithm are applied to test the performance of the proposed model. The results indicate that compared with the SSNN and the BP neural network, the proposed model can closely match the optimal timing plans in futuristic states with higher efficiency.展开更多
The realization of road traffic prediction not only provides real-time and effective information for travelers, but also helps them select the optimal route to reduce travel time. Road traffic prediction offers traffi...The realization of road traffic prediction not only provides real-time and effective information for travelers, but also helps them select the optimal route to reduce travel time. Road traffic prediction offers traffic guidance for travelers and relieves traffic jams. In this paper, a real-time road traffic state prediction based on autoregressive integrated moving average （ARIMA） and the Kalman filter is proposed. First, an ARIMA model of road traffic data in a time series is built on the basis of historical road traffic data. Second, this ARIMA model is combined with the Kalman filter to construct a road traffic state prediction algorithm, which can acquire the state, measurement, and updating equations of the Kalman filter. Third, the optimal parameters of the algorithm are discussed on the basis of historical road traffic data. Finally, four road segments in Beijing are adopted for case studies. Experimental results show that the real-time road traffic state prediction based on ARIMA and the Kalman filter is feasible and can achieve high accuracy.展开更多
The accurate estimation of expressway traffic state can provide decision-making for both travelers and traffic managers.The speed is one of the most representative parameter of the traffic state.So the expressway spee...The accurate estimation of expressway traffic state can provide decision-making for both travelers and traffic managers.The speed is one of the most representative parameter of the traffic state.So the expressway speed spatial distribution can be taken as the expressway traffic state equivalent.In this paper,an algorithm based on virtual speed sensors(VSS)is presented to estimate the expressway traffic state(the speed spatial distribution).To gain the spatial distribution of expressway traffic state,virtual speed sensors are defined between adjacent traffic flow sensors.Then,the speed data extracted from traffic flow sensors in time series are mapped to space series to design virtual speed sensors.Then the speed of virtual speed sensors can be calculated with the weight matrix which is related with the speed of virtual speed sensors and the speed data extracted from traffic flow sensors and the speed data extracted from traffic flow sensors in time series.Finally,the expressway traffic state(the speed spatial distribution)can be gained.The acquisition of average travel speed of the expressway is taken for application of this traffic state estimation algorithm.One typical expressway in Beijing is adopted for the experiment analysis.The results prove that this traffic state estimation approach based on VSS is feasible and can achieve a high accuracy.展开更多
Real-time traffic state(e.g.,speed)prediction is an essential component for traffic control and management in an urban road network.How to build an effective large-scale traffic state prediction system is a challengin...Real-time traffic state(e.g.,speed)prediction is an essential component for traffic control and management in an urban road network.How to build an effective large-scale traffic state prediction system is a challenging but highly valuable problem.This study focuses on the construction of an effective solution designed for spatiotemporal data to predict the traffic state of large-scale traffic systems.In this study,we first summarize the three challenges faced by large-scale traffic state prediction,i.e.,scale,granularity,and sparsity.Based on the domain knowledge of traffic engineering,the propagation of traffic states along the road network is theoretically analyzed,which are elaborated in aspects of the temporal and spatial propagation of traffic state,traffic state experience replay,and multi-source data fusion.A deep learning architecture,termed as Deep Traffic State Prediction(DeepTSP),is therefore proposed to address the current challenges in traffic state prediction.Experiments demonstrate that the proposed DeepTSP model can effectively predict large-scale traffic states.展开更多
The accurate and timely traffic state prediction has become increasingly important for the traffic participants,especially for the traffic managements. In this paper,the traffic state is described by Micro-LOS,and a d...The accurate and timely traffic state prediction has become increasingly important for the traffic participants,especially for the traffic managements. In this paper,the traffic state is described by Micro-LOS,and a direct prediction method is introduced. The development of the proposed method is based on Maximum Entropy (ME) models trained for multiple modes. In the Multimode Maximum Entropy (MME) framework,the different features like temporal and spatial features of traffic systems,regional traffic state are integrated simultaneously,and the different state behaviors based on 14 traffic modes defined by average speed according to the date-time division are also dealt with. The experiments based on the real data in Beijing expressway prove that the MME models outperforms the already existing model in both effectiveness and robustness.展开更多
On-line estimation of the state of traffic based on data sampled by electronic detectors is important for intelligent traffic management and control. Because a nonlinear feature exists in the traffic state, and becaus...On-line estimation of the state of traffic based on data sampled by electronic detectors is important for intelligent traffic management and control. Because a nonlinear feature exists in the traffic state, and because particle filters have good characteristics when it comes to solving the nonlinear problem, a genetic resampling particle filter is proposed to estimate the state of freeway traffic. In this paper, a freeway section of the northern third ring road in the city of Beijing in China is considered as the experimental object. By analysing the traffic-state characteristics of the freeway, the traffic is modeled based on the second-order validated macroscopic traffic flow model. In order to solve the particle degeneration issue in the performance of the particle filter, a genetic mechanism is introduced into the resampling process. The realization of a genetic particle filter for freeway traffic-state estimation is discussed in detail, and the filter estimation performance is validated and evaluated by the achieved experimental data.展开更多
The measurements on actual traffic have revealed the existence of meta-stable states with high flow. Such nonlinear phenomena have not been observed in the classic Nagel-Schreckenberg traffic flow model. Here we just ...The measurements on actual traffic have revealed the existence of meta-stable states with high flow. Such nonlinear phenomena have not been observed in the classic Nagel-Schreckenberg traffic flow model. Here we just add a constraint to the classic model by introducing a velocity-dependent randomization. Two typical randomization strategies are adopted in this paper. It is shown that the Matthew effect is a necessary condition to induce traffic meta-stable states, thus shedding a light on the prerequisites for the emergence of hysteresis loop in the fundamental diagrams.展开更多
In a large-volume,high-density traffic background,air traffic manifests fluid-like microscopical characteristics.The characteristics are formed by the micro tailing actions between individual aircraft.Aircraft headway...In a large-volume,high-density traffic background,air traffic manifests fluid-like microscopical characteristics.The characteristics are formed by the micro tailing actions between individual aircraft.Aircraft headway refers to the time interval between successive flying aircraft in air traffic flow,which is one of the most important characteristics of air traffic flow.The variation in aircraft headway reveals the air traffic control behaviour.In this paper,we study the characteristics of air traffic control behaviours by analyzing radar tracks in a terminal maneuvering area.The headway in arrival traffic flow is measured after the determination of aircraft trailing relationships.The headway evolutionary characteristics for different control decisions and the headway evolutionary characteristics in different phase-states are discussed,and some interesting findings are gotten.This work may be helpful for scholars and managers in understanding the intrinsic nature of air traffic flow and in the development of intelligent assistant decision systems for air traffic management.展开更多
A time-space(TS)traffic diagram is one of the most important tools for traffic visualization and analysis.Recently,it has been empirically shown that using parallelogram cells to construct a TS diagram outperforms usi...A time-space(TS)traffic diagram is one of the most important tools for traffic visualization and analysis.Recently,it has been empirically shown that using parallelogram cells to construct a TS diagram outperforms using rectangular cells due to its incorporation of traffic wave speed.However,it is not realistic to immediately change the fundamental method of TS diagram construction that has been well embedded in various systems.To quickly make the existing TS diagram incorporate traffic wave speed and exhibit more realistic traffic patterns,the paper proposes an area-weighted transformation method that directly transforms rectangular-cell-based TS(rTS)diagrams into parallelogram-cell-based TS(pTS)diagrams,avoiding tracing back the raw data of speed to make the transformation.Two five-hour trajectory datasets from Japanese highway segments are used to demonstrate the effectiveness of the proposed methods.The travel time-based comparison involves assessing the disparities between actual travel times and those computed using rTS diagrams,as well as travel times derived directly from pTS diagrams based on rTS diagrams.The results show that travel times calculated from pTS diagrams converted from rTS diagrams are closer to the actual values,especially in congested conditions,demonstrating superior performance in parallelogram representation.The proposed transformation method has promising prospects for practical applications,making the widely-existing TS diagrams show more realistic traffic patterns.展开更多
The goal of quality-of-service （QoS） multicast routing is to establish a multicast tree which satisfies certain constraints on bandwidth, delay and other metrics. The network state information maintained at every no...The goal of quality-of-service （QoS） multicast routing is to establish a multicast tree which satisfies certain constraints on bandwidth, delay and other metrics. The network state information maintained at every node is often im- precise in a dynamic environment because of non-negligible propagation delay of state messages, periodic updates due to overhead concern, and hierarchical state aggregation. The existing QoS multicast routing algorithms do not provide satisfactory performance with imprecise state information. We propose a distributed QoS multicast routing scheme based on traffic lights, called QMRI algorithm, which can probe multiple feasible tree branches, and select the optimal or near-optimal branch through the UR or TL mode for constructing a multicast tree with QoS guarantees if it exists. The scheme is designed to work with imprecise state information. The proposed algorithm considers not only the QoS requirements but also the cost optimality of the multicast tree. The correctness proof and the complexity analysis about the QMRI algorithm are also given. In addition, we develop NS2 so that it is able to simulate the imprecise network state information. Extensive simulations show that our algorithm achieves high call-admission ratio and low-cost multicast trees with modest message overhead.展开更多
This paper applies both the neural network and adaptive neuro-fuzzy inference system for forecasting short-term chaotic traffic volumes and compares the results. The architecture of the neural network consists of the ...This paper applies both the neural network and adaptive neuro-fuzzy inference system for forecasting short-term chaotic traffic volumes and compares the results. The architecture of the neural network consists of the input vector, one hidden layer and output layer. Bayesian regularization is employed to obtain the effective number of neurons in the hidden layer. The input variables and target of the adaptive neuro-fuzzy inference system are the same as those of the neural network. The data clustering technique is used to group data points so that the membership functions will be more tailored to the input data, which in turn greatly reduces the number of fuzzy rules. Numerical results indicate that these two models have almost the same accuracy, while the adaptive neuro-fuzzy inference system takes more time to train. It is also shown that although the effective number of neurons in the hidden layer is less than half the number of the input elements, the neural network can have satisfactory performance.展开更多
基金Projects(D07020601400707,D101106049710005)supported by the Beijing Science Foundation Plan Project,ChinaProjects(2006AA11Z231,2012AA112401)supported by the National High Technology Research and Development Program of China(863 Program)Project(61104164)supported by the National Natural Science Foundation of China
文摘To effectively solve the traffic data problems such as data invalidation in the process of the acquisition of road traffic states,a road traffic states estimation algorithm based on matching of the regional traffic attracters was proposed in this work.First of all,the road traffic running states were divided into several different modes.The concept of the regional traffic attracters of the target link was put forward for effective matching.Then,the reference sequences of characteristics of traffic running states with the contents of the target link's traffic running states and regional traffic attracters under different modes were established.In addition,the current and historical regional traffic attracters of the target link were matched through certain matching rules,and the historical traffic running states of the target link corresponding to the optimal matching were selected as the initial recovery data,which were processed with Kalman filter to obtain the final recovery data.Finally,some typical expressways in Beijing were adopted for the verification of this road traffic states estimation algorithm.The results prove that this traffic states estimation approach based on matching of the regional traffic attracters is feasible and can achieve a high accuracy.
文摘The rapid development of 5G mobile communication and portable traffic detection technologies enhances highway transportation systems in detail and at a vehicle level. Besides the advantage of no disturbance to the regular traffic operation, these ubiquitous sensing technologies have the potential for unprecedented data collection at any temporal and spatial position. While as a typical distributed parameter system, the freeway traffic dynamics are determined by the current system states and the boundary traffic demand-supply. Using the three-step extended Kalman filtering, this paper simultaneously estimates the real-time traffic state and the boundary flux of freeway traffic with the distributed speed detector networks organized at any location of interest. In order to assess the effectiveness of the proposed approach, a freeway segment from Interstate 80 East (I-80E) in Alameda, Emeryville, and Northern California is selected. Experimental results show that the proposed method has the potential of using only speed detecting data to monitor the state of urban freeway transportation systems without access to the traditional measurement data, such as the boundary flows.
文摘In order to make full use of heterogeneous multi-sensor data to serve urban intelligent transportation systems, a real-time urban traffic state fusion model was proposed, named federated evidence fusion model. The model improves conventional D-S evidence theory in temporal domain, such that it can satisfy the requirement of real-time processing and utilize traffic detection information more efficaciously. The model frame and computational procedures are given. In addition, a generalized reliability weight matrix of evidence is also presented to increase the accuracy of estimation. After that, a simulation test is presented to explain the advantage of the proposed method in comparison with conventional D-S evidence theory. Besides, the validity of the model is proven by the use of the data of loop detectors and GPS probe vehicles collected from an urban link in Shanghai. Results of the experiment show that the proposed approach can well embody and track traffic state at character level in real-time conditions.
文摘This paper presents a microscopic traffic simulation-based method for urban traffic state estimation using Assisted Global Positioning System (A-GPS) mobile phones. In this approach, real-time location data are collected by A-GPS mobile phones to track vehicles traveling on urban roads. In addition, tracking data obtained from individual mobile probes are aggregated to provide estimations of average road link speeds along rolling time periods. Moreover, the estimated average speeds are classified to different traffic condition levels, which are prepared for displaying a real-time traffic map on mobile phones. Simulation results demonstrate the effectiveness of the proposed method, which are fundamental for the subsequent development of a system demonstrator.
基金supported by the National Key R&D Program of China(Grant No.2023YFB4302702)the Fundamental Research Funds for the Central Universities(Grant No.2023JKF02ZK08).
文摘Accurate traffic state estimations(TSEs)within road networks are crucial for enhancing intelligent transportation systems and developing effective traffic management strategies.Traditional TSE methods often assume homogeneous traffic,where all vehicles are considered identical,which does not accurately reflect the complexities of real traffic conditions that often exhibit heterogeneous characteristics.In this study,we address the limitations of conventional models by introducing a novel TSE model designed for precise estimations of heterogeneous traffic flows.We develop a comprehensive traffic feature index system tailored for heterogeneous traffic that includes four elements:basic traffic parameters,heterogeneous vehicle speeds,heterogeneous vehicle flows,and mixed flow rates.This system aids in capturing the unique traffic characteristics of different vehicle types.Our proposed high-dimensional fuzzy TSE model,termed HiF-TSE,integrates three main processes:feature selection,which eliminates redundant traffic features using Spearman correlation coefficients;dimension reduction,which utilizes the T-distributed stochastic neighbor embedding machine learning algorithm to reduce high-dimensional traffic feature data;and FCM clustering,which applies the fuzzy C-means algorithm to classify the simplified data into distinct clusters.The HiF-TSE model significantly reduces computational demands and enhances efficiency in TSE processing.We validate our model through a real-world case study,demonstrating its ability to adapt to variations in vehicle type compositions within heterogeneous traffic and accurately represent the actual traffic state.
基金The National Natural Science Foundation of China (No.50422283)the Soft Science Research Project of Ministry of Housing and Urban-Rural Development of China (No.2008-K5-14)
文摘The state-space neural network and extended Kalman filter model is used to directly predict the optimal timing plan that corresponds to futuristic traffic conditions in real time with the purposes of avoiding the lagging of the signal timing plans to traffic conditions. Utilizing the traffic conditions in current and former intervals, the network topology of the state-space neural network （SSNN）, which is derived from the geometry of urban arterial routes, is used to predict the optimal timing plan corresponding to the traffic conditions in the next time interval. In order to improve the effectiveness of the SSNN, the extended Kalman filter （EKF） is proposed to train the SSNN instead of conventional approaches. Raw traffic data of the Guangzhou Road, Nanjing and the optimal signal timing plan generated by a multi-objective optimization genetic algorithm are applied to test the performance of the proposed model. The results indicate that compared with the SSNN and the BP neural network, the proposed model can closely match the optimal timing plans in futuristic states with higher efficiency.
基金Project supported by the National Science &Technology Pillar Program(No.2014BAG01B02)
文摘The realization of road traffic prediction not only provides real-time and effective information for travelers, but also helps them select the optimal route to reduce travel time. Road traffic prediction offers traffic guidance for travelers and relieves traffic jams. In this paper, a real-time road traffic state prediction based on autoregressive integrated moving average （ARIMA） and the Kalman filter is proposed. First, an ARIMA model of road traffic data in a time series is built on the basis of historical road traffic data. Second, this ARIMA model is combined with the Kalman filter to construct a road traffic state prediction algorithm, which can acquire the state, measurement, and updating equations of the Kalman filter. Third, the optimal parameters of the algorithm are discussed on the basis of historical road traffic data. Finally, four road segments in Beijing are adopted for case studies. Experimental results show that the real-time road traffic state prediction based on ARIMA and the Kalman filter is feasible and can achieve high accuracy.
基金supported by the Beijing Science Foundation Plan Projects(Grant No.D07020601400707,D101106049710005)the National Hi-Tech Research and Development Program of China("863"Project)(Grant No.2006AA11Z231)the National Natural Science Foundation of China(Grant No.61104164)
文摘The accurate estimation of expressway traffic state can provide decision-making for both travelers and traffic managers.The speed is one of the most representative parameter of the traffic state.So the expressway speed spatial distribution can be taken as the expressway traffic state equivalent.In this paper,an algorithm based on virtual speed sensors(VSS)is presented to estimate the expressway traffic state(the speed spatial distribution).To gain the spatial distribution of expressway traffic state,virtual speed sensors are defined between adjacent traffic flow sensors.Then,the speed data extracted from traffic flow sensors in time series are mapped to space series to design virtual speed sensors.Then the speed of virtual speed sensors can be calculated with the weight matrix which is related with the speed of virtual speed sensors and the speed data extracted from traffic flow sensors and the speed data extracted from traffic flow sensors in time series.Finally,the expressway traffic state(the speed spatial distribution)can be gained.The acquisition of average travel speed of the expressway is taken for application of this traffic state estimation algorithm.One typical expressway in Beijing is adopted for the experiment analysis.The results prove that this traffic state estimation approach based on VSS is feasible and can achieve a high accuracy.
基金supported by the Distinguished Young Scholar Project(No.71922007)of the National Natural Science Foundation of China,and supported in part by the Jiangsu Provincial Key Laboratory of Networked Collective Intelligence under Grant BM2017002part of a project that has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No.101025896.
文摘Real-time traffic state(e.g.,speed)prediction is an essential component for traffic control and management in an urban road network.How to build an effective large-scale traffic state prediction system is a challenging but highly valuable problem.This study focuses on the construction of an effective solution designed for spatiotemporal data to predict the traffic state of large-scale traffic systems.In this study,we first summarize the three challenges faced by large-scale traffic state prediction,i.e.,scale,granularity,and sparsity.Based on the domain knowledge of traffic engineering,the propagation of traffic states along the road network is theoretically analyzed,which are elaborated in aspects of the temporal and spatial propagation of traffic state,traffic state experience replay,and multi-source data fusion.A deep learning architecture,termed as Deep Traffic State Prediction(DeepTSP),is therefore proposed to address the current challenges in traffic state prediction.Experiments demonstrate that the proposed DeepTSP model can effectively predict large-scale traffic states.
基金supported by Beijing Science Foundation Plan Project(Grant No.D07020601400707)the National High Technology Re-search and Development Program of China(Grant NO.2006AA11Z231)
文摘The accurate and timely traffic state prediction has become increasingly important for the traffic participants,especially for the traffic managements. In this paper,the traffic state is described by Micro-LOS,and a direct prediction method is introduced. The development of the proposed method is based on Maximum Entropy (ME) models trained for multiple modes. In the Multimode Maximum Entropy (MME) framework,the different features like temporal and spatial features of traffic systems,regional traffic state are integrated simultaneously,and the different state behaviors based on 14 traffic modes defined by average speed according to the date-time division are also dealt with. The experiments based on the real data in Beijing expressway prove that the MME models outperforms the already existing model in both effectiveness and robustness.
基金Project supported by the National High Technology Research and Development Program of China (Grant No. 2011AA110303)
文摘On-line estimation of the state of traffic based on data sampled by electronic detectors is important for intelligent traffic management and control. Because a nonlinear feature exists in the traffic state, and because particle filters have good characteristics when it comes to solving the nonlinear problem, a genetic resampling particle filter is proposed to estimate the state of freeway traffic. In this paper, a freeway section of the northern third ring road in the city of Beijing in China is considered as the experimental object. By analysing the traffic-state characteristics of the freeway, the traffic is modeled based on the second-order validated macroscopic traffic flow model. In order to solve the particle degeneration issue in the performance of the particle filter, a genetic mechanism is introduced into the resampling process. The realization of a genetic particle filter for freeway traffic-state estimation is discussed in detail, and the filter estimation performance is validated and evaluated by the achieved experimental data.
文摘The measurements on actual traffic have revealed the existence of meta-stable states with high flow. Such nonlinear phenomena have not been observed in the classic Nagel-Schreckenberg traffic flow model. Here we just add a constraint to the classic model by introducing a velocity-dependent randomization. Two typical randomization strategies are adopted in this paper. It is shown that the Matthew effect is a necessary condition to induce traffic meta-stable states, thus shedding a light on the prerequisites for the emergence of hysteresis loop in the fundamental diagrams.
基金supported by the National Nature Science Foundation of China(No.71801215)the Fundamental Research Fund for the Central Universities (No. 3122016C009).
文摘In a large-volume,high-density traffic background,air traffic manifests fluid-like microscopical characteristics.The characteristics are formed by the micro tailing actions between individual aircraft.Aircraft headway refers to the time interval between successive flying aircraft in air traffic flow,which is one of the most important characteristics of air traffic flow.The variation in aircraft headway reveals the air traffic control behaviour.In this paper,we study the characteristics of air traffic control behaviours by analyzing radar tracks in a terminal maneuvering area.The headway in arrival traffic flow is measured after the determination of aircraft trailing relationships.The headway evolutionary characteristics for different control decisions and the headway evolutionary characteristics in different phase-states are discussed,and some interesting findings are gotten.This work may be helpful for scholars and managers in understanding the intrinsic nature of air traffic flow and in the development of intelligent assistant decision systems for air traffic management.
基金National Natural Science Foundation of China(71871010).
文摘A time-space(TS)traffic diagram is one of the most important tools for traffic visualization and analysis.Recently,it has been empirically shown that using parallelogram cells to construct a TS diagram outperforms using rectangular cells due to its incorporation of traffic wave speed.However,it is not realistic to immediately change the fundamental method of TS diagram construction that has been well embedded in various systems.To quickly make the existing TS diagram incorporate traffic wave speed and exhibit more realistic traffic patterns,the paper proposes an area-weighted transformation method that directly transforms rectangular-cell-based TS(rTS)diagrams into parallelogram-cell-based TS(pTS)diagrams,avoiding tracing back the raw data of speed to make the transformation.Two five-hour trajectory datasets from Japanese highway segments are used to demonstrate the effectiveness of the proposed methods.The travel time-based comparison involves assessing the disparities between actual travel times and those computed using rTS diagrams,as well as travel times derived directly from pTS diagrams based on rTS diagrams.The results show that travel times calculated from pTS diagrams converted from rTS diagrams are closer to the actual values,especially in congested conditions,demonstrating superior performance in parallelogram representation.The proposed transformation method has promising prospects for practical applications,making the widely-existing TS diagrams show more realistic traffic patterns.
文摘The goal of quality-of-service （QoS） multicast routing is to establish a multicast tree which satisfies certain constraints on bandwidth, delay and other metrics. The network state information maintained at every node is often im- precise in a dynamic environment because of non-negligible propagation delay of state messages, periodic updates due to overhead concern, and hierarchical state aggregation. The existing QoS multicast routing algorithms do not provide satisfactory performance with imprecise state information. We propose a distributed QoS multicast routing scheme based on traffic lights, called QMRI algorithm, which can probe multiple feasible tree branches, and select the optimal or near-optimal branch through the UR or TL mode for constructing a multicast tree with QoS guarantees if it exists. The scheme is designed to work with imprecise state information. The proposed algorithm considers not only the QoS requirements but also the cost optimality of the multicast tree. The correctness proof and the complexity analysis about the QMRI algorithm are also given. In addition, we develop NS2 so that it is able to simulate the imprecise network state information. Extensive simulations show that our algorithm achieves high call-admission ratio and low-cost multicast trees with modest message overhead.
文摘This paper applies both the neural network and adaptive neuro-fuzzy inference system for forecasting short-term chaotic traffic volumes and compares the results. The architecture of the neural network consists of the input vector, one hidden layer and output layer. Bayesian regularization is employed to obtain the effective number of neurons in the hidden layer. The input variables and target of the adaptive neuro-fuzzy inference system are the same as those of the neural network. The data clustering technique is used to group data points so that the membership functions will be more tailored to the input data, which in turn greatly reduces the number of fuzzy rules. Numerical results indicate that these two models have almost the same accuracy, while the adaptive neuro-fuzzy inference system takes more time to train. It is also shown that although the effective number of neurons in the hidden layer is less than half the number of the input elements, the neural network can have satisfactory performance.
