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 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
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
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
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
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
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
街景影像覆盖面广,能提供城市级别的交通场景信息,对开展交通研究分析提供了大规模数据源的支持和新的研究方法。为了探究街景影像在交通研究中的应用情况,从Web of Science核心集合数据库筛选了2011—2022年街景图像在交通研究中应用...街景影像覆盖面广,能提供城市级别的交通场景信息,对开展交通研究分析提供了大规模数据源的支持和新的研究方法。为了探究街景影像在交通研究中的应用情况,从Web of Science核心集合数据库筛选了2011—2022年街景图像在交通研究中应用相关的143篇论文,借助CiteSpace文献计量分析软件从年发文量、作者合作图谱、国家与机构合作图谱、关键词共现、关键词聚类和主题突发检测等方面进行归纳分析。在此基础上总结街景影像在交通基础设施、交通安全感知、出行辅助和出行环境感知四方面的应用研究进展,并对未来的研究方向提出展望。文献综述结果表明:(1)街景影像数据已被广泛应用于交通领域不同维度的研究,大多数研究通过卷积神经网络模型提取街景影像信息以反映交通场景特征;(2)由于街景数据采集时间跨度大,致使当前基于街景影像数据的交通方面应用主要集中在空间维度研究,缺乏动态时间维度的分析;(3)街景影像与交通领域知识数据进行融合分析建模是街景影像数据在交通领域应用的发展趋势。展开更多
物理信息深度学习(physics-informed deep learning, PIDL)是一种将深度学习与物理学先验知识相结合的新兴范式,该范式在智能交通领域,尤其在交通状态估计应用中,展现出了巨大潜力。为进一步优化物理信息深度学习模型在交通状态估计问...物理信息深度学习(physics-informed deep learning, PIDL)是一种将深度学习与物理学先验知识相结合的新兴范式,该范式在智能交通领域,尤其在交通状态估计应用中,展现出了巨大潜力。为进一步优化物理信息深度学习模型在交通状态估计问题上的准确度与收敛速度,构建了一个结合Aw-Rascle宏观交通流模型的物理信息自适应深度学习模型(physics-informed adaptive deep learning with Aw-Rascle, PIAdapDL-AR),依据有限与局部的交通检测数据,实时准确估计全局交通流状态。主要的改进包括两部分,一是在PIDL框架中的物理部分引入高阶Aw-Rascle交通流模型作为物理约束条件,引导并规范神经网络的训练过程;二是在神经网络部分融合自适应激活函数,替代固定的非线性激活函数,以动态优化神经网络性能。基于NGSIM数据集生成模拟的固定检测器数据和移动检测器数据,进行实验以验证模型有效性。实验结果表明:在不同覆盖率的固定检测数据场景下,PIAdapDL-AR的相对误差相比于基线模型PIDL-LWR降低了34.38%~45.24%;在不同渗透率的移动检测数据场景下,PIAdapDL-AR的相对误差相比于PIDL-LWR降低了18.33%~34.95%;融合自适应激活函数的PIAdapDL-AR的收敛速度优于配置固定激活函数的PIDL-AR,且收敛速度和估计精度均随着自适应激活函数中比例因子的增大而提升。展开更多
传统电动汽车充电负荷建模通常采用对电动汽车个体进行抽样模拟的方式,未能从分析机理的角度描述电动汽车群体相互作用形成的宏观运行状态。为此,提出一种基于半动态交通均衡模型和组合荷电状态(combined states of the charge,CSOC)概...传统电动汽车充电负荷建模通常采用对电动汽车个体进行抽样模拟的方式,未能从分析机理的角度描述电动汽车群体相互作用形成的宏观运行状态。为此,提出一种基于半动态交通均衡模型和组合荷电状态(combined states of the charge,CSOC)概率计算的电动汽车充电负荷概率分布计算方法。首先,分析电动汽车的交通特性和充电特性,并提出一种可行路径集构建方法;然后,引入交通均衡理论进行电动汽车空间分布建模,建立考虑随机效用的半动态交通均衡模型,实现宏观交通流均衡分配。进一步地,从理论层面分析电动汽车群的荷电状态变化,建立基于CSOC的充电负荷概率分布计算模型。最后,分别在13节点路网和实际大路网中验证所提方法的有效性,并分析了电动汽车渗透率和路网结构对充电负荷概率分布的影响。展开更多
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.
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.
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.
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.
文摘街景影像覆盖面广,能提供城市级别的交通场景信息,对开展交通研究分析提供了大规模数据源的支持和新的研究方法。为了探究街景影像在交通研究中的应用情况,从Web of Science核心集合数据库筛选了2011—2022年街景图像在交通研究中应用相关的143篇论文,借助CiteSpace文献计量分析软件从年发文量、作者合作图谱、国家与机构合作图谱、关键词共现、关键词聚类和主题突发检测等方面进行归纳分析。在此基础上总结街景影像在交通基础设施、交通安全感知、出行辅助和出行环境感知四方面的应用研究进展,并对未来的研究方向提出展望。文献综述结果表明:(1)街景影像数据已被广泛应用于交通领域不同维度的研究,大多数研究通过卷积神经网络模型提取街景影像信息以反映交通场景特征;(2)由于街景数据采集时间跨度大,致使当前基于街景影像数据的交通方面应用主要集中在空间维度研究,缺乏动态时间维度的分析;(3)街景影像与交通领域知识数据进行融合分析建模是街景影像数据在交通领域应用的发展趋势。
文摘物理信息深度学习(physics-informed deep learning, PIDL)是一种将深度学习与物理学先验知识相结合的新兴范式,该范式在智能交通领域,尤其在交通状态估计应用中,展现出了巨大潜力。为进一步优化物理信息深度学习模型在交通状态估计问题上的准确度与收敛速度,构建了一个结合Aw-Rascle宏观交通流模型的物理信息自适应深度学习模型(physics-informed adaptive deep learning with Aw-Rascle, PIAdapDL-AR),依据有限与局部的交通检测数据,实时准确估计全局交通流状态。主要的改进包括两部分,一是在PIDL框架中的物理部分引入高阶Aw-Rascle交通流模型作为物理约束条件,引导并规范神经网络的训练过程;二是在神经网络部分融合自适应激活函数,替代固定的非线性激活函数,以动态优化神经网络性能。基于NGSIM数据集生成模拟的固定检测器数据和移动检测器数据,进行实验以验证模型有效性。实验结果表明:在不同覆盖率的固定检测数据场景下,PIAdapDL-AR的相对误差相比于基线模型PIDL-LWR降低了34.38%~45.24%;在不同渗透率的移动检测数据场景下,PIAdapDL-AR的相对误差相比于PIDL-LWR降低了18.33%~34.95%;融合自适应激活函数的PIAdapDL-AR的收敛速度优于配置固定激活函数的PIDL-AR,且收敛速度和估计精度均随着自适应激活函数中比例因子的增大而提升。
文摘传统电动汽车充电负荷建模通常采用对电动汽车个体进行抽样模拟的方式,未能从分析机理的角度描述电动汽车群体相互作用形成的宏观运行状态。为此,提出一种基于半动态交通均衡模型和组合荷电状态(combined states of the charge,CSOC)概率计算的电动汽车充电负荷概率分布计算方法。首先,分析电动汽车的交通特性和充电特性,并提出一种可行路径集构建方法;然后,引入交通均衡理论进行电动汽车空间分布建模,建立考虑随机效用的半动态交通均衡模型,实现宏观交通流均衡分配。进一步地,从理论层面分析电动汽车群的荷电状态变化,建立基于CSOC的充电负荷概率分布计算模型。最后,分别在13节点路网和实际大路网中验证所提方法的有效性,并分析了电动汽车渗透率和路网结构对充电负荷概率分布的影响。