Proactive traffic responsive control based on state-space neural network and extended Kalman filter

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.

Road traffic states estimation algorithm based on matching of regional traffic attracters

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.

A genetic resampling particle filter for freeway traffic-state estimation

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.

Real-Time Urban Traffic State Estimation with A-GPS Mobile Phones as Probes

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.

A Model of Federated Evidence Fusion for Real-time Urban Traffic State Estimation

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.

Real-Time Traffic State and Boundary Flux Estimation with Distributed Speed Detecting Networks

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.

不同降雨量下基于宏观基本图的边界控制策略

在雨雪等不利天气条件下,城市交通拥堵加剧,北京、天津等大城市在降雨条件下经常发生多路段区域性交通拥堵。因此,根据天津市中心城区和市郊区域路网实际交通数据,以路网交通流宏观基本图模型为研究基础,对比不同降雨量以及不同路网的路网交通流时序和宏观基本图变化规律,分析不同降雨量对天津市中心城区和市郊区域路网交通状态的影响。基于不同降雨量下中心城区和市郊区域路网宏观交通流的变化规律,分别构建路网动态演化模型,并对模型的参数进行标定和有效性验证。针对降雨条件下路网发生的区域性拥堵问题,基于宏观基本图的边界控制分别设计了不同降雨量下中心城区和市郊区域路网控制策略,通过仿真实验分析验证了不同控制策略的效果,并给出了能够缓解中心城区和市郊区域路网拥堵的可行策略。结果表明:在小雨天气条件下,将从市郊区域向中心城区的交通流量的转移比例减小量控制在9%~50%范围之内时,中心城区与市郊区域交通状态更加均衡,路网调控效果更好;在大雨天气条件下,将从市郊区域向中心城区的交通流量的转移比例减小量控制在23%~50%范围之内时,中心城区与市郊区域交通状态更加均衡,路网调控效果更好。这表明该控制策略能够缓解中心城区和市郊区域路网的交通拥堵,保障路网交通系统的稳定运行。

多源数据融合驱动的城市快速路交通状态划分

为提升交通状态划分效果,本文提出一种基于负激励项的改进模糊C均值聚类(BNITFCM)交通状态划分模型。该模型在原有FCM(Fuzzy C-Means)模型基础上考虑了交通流样本点权重以及交通流参数权重对聚类效果的影响,并引入隶属度负激励项、交通流权重负激励项、交通流样本点权重负激励项使聚类结果呈现类内高耦合、类间低耦合的特性。在此基础上,对样本点进行加权处理,用加权欧氏距离描述样本点之间的关系。通过拉格朗日乘子法得出模型的迭代公式并通过该迭代公式对模型进行求解。针对大多交通状态划分方法参数特征维度低的问题,本文以经过多源数据融合获得的速度、速度标准差、流量、密度和道路满载度构建高维特征输入。以数值仿真实验检验了BNIT-FCM模型在分类准确性方面的表现,结果表明,BNIT-FCM模型较FCM模型和改进模糊隶属度FCM模型(IFMD-FCM)的ARI(Adjusted Rand Index)分别提升了4.17%和3.56%。以深圳市北环大道卡口和浮动车数据的交通流为研究对象,实验结果表明,BNIT-FCM模型对比FCM模型以及IFMD-FCM模型的轮廓系数分别提升了4.12%和4.07%;同时,BNIT-FCM模型采用多源融合数据的速度及其标准差比单独采用卡口数据和单独采用浮动车数据的速度及其标准差的轮廓系数分别提升了29.67%和54.13%。

基于轨迹数据的快速路交织区拥堵演变特征研究

对快速路交织区拥堵演变过程中宏观交通流参数与交通状态的时变关系进行研究,可以为快速路交织区交通状态判别提供科学依据。利用YOLO算法从高清视频中提取车辆轨迹数据后,利用卡尔曼滤波对原始轨迹数据进行降噪平滑处理;对快速路交织区拥堵演变过程中速度、流量、密度等宏观交通流参数与交通状态进行时变分析,揭示快速路交织区宏观交通流参数在拥堵演变过程中的时变特征。结果表明,在快速路交织区交通状态判别时,融合平均行程速度和交通流密度等指标,可以有效提高交通状态判别精度。

街景影像在城市交通研究中的应用:回顾、分析和展望

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。

基于物理信息自适应深度学习的交通状态估计

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。

基于SVM的交通状态识别方法研究

为了及时准确地识别城市主干路交通状态,使用无人机悬停于观测路段上方进行交通数据采集,对采集的交通视频数据进行分帧、裁剪,建立交通状态图像数据集并进行预处理。针对PCA算法易受光照影响这一问题,融合LBP算法提取图像特征,并通过粒子群算法优化SVM算法,搭建交通状态识别模型。利用已建立的交通状态图像数据集训练LBP-PCA-SVM和PCA-SVM模型,对所得结果进行对比分析。结果表明,LBP-PCA-SVM模型相比PCA-SVM模型识别性能较高,并且能够满足实时性要求。

基于地图API技术的道路拥堵点交通运行状态分析

以东阳市老城区为例,提出应用地图API技术进行道路拥堵点交通运行状态分析的一般流程。首先,按照一定时间间隔分别截取研究范围的实时路况地图,根据路况颜色和持续时间识别道路拥堵点;其次,将研究范围划分成一定数量的网格,利用地图API技术批量获取各网格中心至拥堵点的预估通行时间,评估当前时刻下拥堵点的可达性;最后,获取堵点周边道路各时段的平均行程速度,评估道路运行状态的时间变化特征。通过地图API进行道路堵点识别和运行状态分析,能够大大减少研究者的前期工作量,为后续研究分析提供相对准确的基础数据。

基于半动态交通均衡的电动汽车充电负荷概率分布建模

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。

船舶流量智能交通检测系统设计

以避免船舶AIS数据内噪声对船舶流量检测干扰,准确、实时实现船舶流量智能交通检测,设计船舶流量智能交通检测系统。采集模块采集海上航线船舶AIS数据,使用逻辑分析模块内的船舶交通流状态空间模型,获取当前海上航线船舶交通流空间状态,卡尔曼滤波算法将上一个航线船舶交通流空间状态作为观测向量,同时抑制船舶交通流空间状态观测向量内干扰噪声,建立船舶流量智能交通检测模型,不断更新海上航线船舶流量空间状态转移矩阵,推理当前时刻船舶交通流量空间状态,得到船舶流量智能交通检测结果。实验表明,该方法具备较强的船舶AIS数据获取能力,可准确获得海上航线船舶流量状态,并有效检测不同时刻船舶流量,应用效果较好。

基于车辆运行状态的高速公路交通安全管控技术研究

为解决高速公路因驾驶操作不当、疲劳驾驶、能见度不良等因素导致的交通事故频发问题,本文通过获取高速公路事故多发路段的车辆运行状态和行车环境信息,提出基于车辆运行状态的高速公路交通安全估测模型,对异常行驶车辆的有效判定和识别,并利用路侧安全管控技术,构建基于车辆运行状态的预警管控系统,通过声光警示信息实现对异常车辆进行定向预警功能.结论表明:本文交通安全估测模型,异常车辆识别准确率达72%,借助声光预警技术,可让异常车辆及时恢复正常行驶状态,避免交通事故的发生.

基于多维数据的高速公路交通预测算法

目前高速公路交通流的算法较多,但很多算法要基于车辆的GPS信息,或者单一的卡口信息,结果往往滞后或者不准确,我们基于浙江省高速公路温州段上的传感器(卡口与雷达)数据进行梳理,形成一套数据相互补充、相互验证的交通流短时预测算法,提高了交通状态预测,已用于实战。本文首先确定了数据的来源与格式,其次介绍了数据之间的相互补全,最后从短时交通状态演化特性的剖析到深度学习的交通状态预测,最终得到了温州高速路网的交通状态预测结果。

基于Echo State Neural Networks的短期交通流预测算法

在城市交通环境,交通流的正确预测是比较困难,因为多个十字路口,这使得预置的交通控制模型之间的相互作用和intertwinement不能保持始终高性能在所有的交通情况。

基于机器学习的城市道路交通拥塞状态识别方法

传统的交通拥塞状态识别方法往往需要手动选择,提取特征,对于大规模和高维度的交通数据来说效率低下,难以动态地适应城市交通状态的变化,导致城市道路交通拥塞状态识别效果不佳。为此,提出一种基于机器学习的城市道路交通拥塞状态识别方法。该方法以城市道路交通视频图像作为基础数据,基于机器学习方法结合深度学习技术,自动从数据中学习特征,提高特征提取效率;通过设置交通参数与拥堵临界点后,建立城市道路交通拥塞强度评价的Logistic回归模型,通过该模型来评价当前城市道路交通视频图像内交通拥塞强度,然后将城市道路交通拥塞强度评价结果输入到机器学习算法的支持向量机模型内,再使用麻雀算法对支持向量机模型进行改进,得到最佳的支持向量机模型参数,运用该最佳参数训练支持向量机模型后,输出城市道路交通拥塞状态识别结果。实验结果表明:该方法可有效评价不同类型城市道路交通拥塞强度,并可利用机器学习算法中的支持向量机模型输出城市道路交通拥塞状态,应用效果较佳。

非机动车在交叉路口直行二次过街交通信号灯状态鉴定

道路交通信号灯状态鉴定是交通事故痕迹物证综合鉴定项目。交通事故中车辆在事故路口发生事故,其对应的道路交通信号灯状态是交通事故责任认定的重要依据。非机动车在交叉路口直行遇二次过街信号灯,应根据视频中的起始时刻和其他信号灯状态,结合交通信号配时,通过分析计算,分段判断起始时刻目标交通信号灯的状态。