A data fusion approach for real-time traffic state estimation in urban signalized links

Real-time estimation of the traffic state in urban signalized links is valuable information for modern traffic control and management. In recent years, with the development of in-vehicle and communication technologies, connected vehicle data has been increasingly used in literature and practice. In this work, a novel data fusion approach is proposed for the high-resolution (second-by-second) estimation of queue length, vehicle accumulation, and outflow in urban signalized links. Required data includes input flow from a fixed detector at the upstream end of the link as well as location and speed of the connected vehicles. A probability-based approach is derived to compensate the error associated with low penetration rates while estimating the queue tail location, which renders the proposed methodology more robust to varying penetration rates of connected vehicles. A well-defined nonlinear function based on traffic flow theory is developed to attain the number of vehicles inside the queue based on queue tail location and average speed of connected vehicles. The overall scheme is thoroughly tested and demonstrated in a realistic microscopic simulation environment for three types of links with different penetration rates of connected vehicles. In order to test the efficiency of the proposed methodology in case that data are available at higher sampling times, the estimation procedure is also demonstrated for different time resolutions. The results demonstrate the efficiency and accuracy of the approach for high-resolution estimation, even in the presence of measurement noise.     

Performability evaluation of the ERTMS/ETCS – Level 3

Level 3 of the ERTMS/ETCS improves the capacity of railways by replacing fixed-block signalling, which prevents a train to enter a block occupied by another train, with moving block signalling, which allows a train to proceed as long as it receives radio messages ensuring that the track ahead is clear of other trains. If messages are lost, a train must stop for safety reasons within a given deadline, even though the track ahead is clear, making the availability of the communication link crucial for successful operation.We combine analytic evaluation of failures due to burst noise and connection losses with numerical solution of a non-Markovian model representing also failures due to handovers between radio stations. In so doing, we show that handovers experienced by a pair of chasing trains periodically affect the availability of the radio link, making behavior of the overall communication system recurrent over the hyper-period of periodic message releases and periodic arrivals at cell borders. As a notable aspect, non-Markovian transient analysis within two hyper-periods is sufficient to derive an upper bound on the first-passage time distribution to an emergency brake, permitting to achieve a trade-off between railway throughput and stop probability. A sensitivity analysis is performed with respect to train speed and headway distance, permitting to gain insight into the consequences of system-level design choices.     

A phase-based smoothing method for accurate traffic speed estimation with floating car data

In this paper, a novel freeway traffic speed estimation method based on probe data is presented. In contrast to other traffic speed estimators, it only requires velocity data from probes and does not depend on any additional data inputs such as density or flow information. In the first step the method determines the three traffic phases free flow, synchronized flow, and Wide Moving Jam (WMJ) described by Kerner et al. in space and time. Subsequently, reported data is processed with respect to the prevailing traffic phase in order to estimate traffic velocities. This two-step approach allows incorporating empirical features of phase fronts into the estimation procedure. For instance, downstream fronts of WMJs always propagate upstream with approximately constant velocity, and downstream fronts of synchronized flow phases usually stick to bottlenecks. The second step assures the validity of measured velocities is limited to the extent of its assigned phase. Effectively, velocity information in space-time can be estimated more distinctively and the result is therefore more accurate even if the input data density is low.The accuracy of the proposed Phase-Based Smoothing Method (PSM) is evaluated using real floating car data collected during two traffic congestions on the German freeway A99 and compared to the performance of the Generalized Adaptive Smoothing Method (GASM) as well as a naive algorithm. The quantitative and qualitative results show that the PSM reconstructs the congestion pattern more accurately than the other two. A subsequent analysis of the computational efficiency and sensitivity demonstrates its practical suitability.     

Estimation of flow and density using probe vehicles with spacing measurement equipment

Probe vehicles provide some of the most useful data for road traffic monitoring because they can acquire wide-ranging and spatiotemporally detailed information at a relatively low cost compared with traditional fixed-point observation. However, current GPS-equipped probe vehicles cannot directly provide us volume-related variables such as flow and density. In this paper, we propose a new probe vehicle-based estimation method for obtaining volume-related variables by assuming that a probe vehicle can measure the spacing to its leading one. This assumption can be realized by utilizing key technologies in advanced driver assistance systems that are expected to spread in the near future. We developed a method of estimating the flow, density, and speed from the probe vehicle data without exogenous assumptions on traffic flow characteristics, such as a fundamental diagram. In order to quantify the characteristics of the method, we performed a field experiment at a real-world urban expressway by employing prototypes of the probe vehicles with spacing measurement equipment. The result showed that the proposed method could accurately estimate the 5 min and hourly traffic volumes with probe vehicle penetration rate of 3.5% and 0.2%, respectively.     

Congestion mechanism and demand adjustment strategies for double-cell system with bottlenecks

In this paper, ramp systems on the Beijing 3rd ring road are described as double-cell ramp systems with a bottleneck. By analyzing empirical data for the Beijing 3rd ring road, we found that the initial states have an important impact on the final convergence states of the ramp systems. Then, we studied the dynamic process of the ramp systems, determined the congestion mechanism, and then designed a ramp control method based on the obtained mechanism. Under a feasible demand, double-cell ramp systems exhibit two typical cases, including an upstream-bottleneck system (in which the bottleneck cell is upstream) and a downstream-bottleneck system (in which the bottleneck cell is downstream). Then, a cell transmission model is used to analyze the dynamic evolution processes, starting from different initial states, and determine the congestion mechanism for each case. It is proven that the two systems have different possible equilibrium sets and congestion mechanisms. In an upstream-bottleneck system, the downstream always converges to the uncongested equilibrium, while the upstream bottleneck cell may experience congestion under certain initial states. In a downstream-bottleneck system, the congestion starts downstream, and then gradually propagates upstream. Furthermore, based on the different congestion mechanisms, two demand adjustment strategies are proposed, which redistribute the stationary feasible demand. The simulation results indicate that both systems can converge to uncongested equilibriums after demand adjustment. The ramp demand adjustment methods provide a scientific basis for urban traffic system management.     

基于信道状态信息的船舶驾驶台人员检测及活跃度评价方法

船舶驾驶台人员包括按照规定要求的常规值班人员和特殊情况下额外的瞭望人员或船长、引航员等,驾驶台人员活跃度是判断其工作状态的重要指标之一。传统的基于计算机视觉的人员检测方法在面对船舶驾驶台遮挡物多、夜间或恶劣天气下光线不足等问题时,精度明显降低。为解决该问题,提出了1种基于普通商用Wi-Fi设备的活跃度感知方法。由于船体材质、结构特点以及变化的运动状态导致动态多径多、信号噪声强,对Wi-Fi设备造成干扰,为此设计了值班高关联度数据（duty high correlation data,DHCD）选择模块及基于信道状态信息（channel state information,CSI）的多层级特征提取模块。DHCD选择模块分析驾驶台人员不同航行、值班情况下的CSI特点,对比0~5人在驾驶台内值班、工作时的信道变化,利用模糊C-means聚类算法提取CSI中对值班人员行为反应最灵敏的信道,去除对信号噪声反应敏感的信道信息;通过多层级特征提取模块计算去噪后CSI数据的幅值与相位离散度、多链路融合离散度、变异指数等多层特征,作为活跃度评价基础参数。依据驾驶台值班要求设计了驾驶台人员活跃度评价模块,采用支持向量机算法判断驾驶台人员数量,采用客观赋权法得到基础参数权重,结合人数信息与权重信息评价驾驶台人员活跃度。实验结果表明：使用DHCD选择模块和多层级模块处理后的多层级特征将驾驶台人员数量检测精度提升至89.6%,对比直接使用原始数据时检测精度提升7.1%。在夜间、雨雾天气等光照不足情况下,基于计算机视觉方法的检测精度会由光线充足时的96.2%降至60.3%,而该方法监测精度不会降低。因此,基于CSI的驾驶台人员活跃度检测方法丰富了驾驶台人员检测算法,能有效识别船舶驾驶台人员是否符合安全值班的基本要求。     

基于状态观测器的城市快速路交通事故识别

针对城市快速路网中交通事故频发的现象,为及时准确地对事故进行识别,提出一种基于宏观交通流模型的状态观测器估计算法.根据利用交通仿真软件Paramics的实验数据,并结合元胞传输模型(CTM)理论分析事故发生前后,事故路段及其上下游路段的交通流密度分布特征.同时基于路网的交通流模型构建了城市快速路事故的状态观测器估计模型,模型通过估计密度的变化规律,并结合交通状态分布特征来对事故进行识别.以京通快速路为例,通过对观测器估计误差进行计算,得出了实验路段平均百分比误差(MPE)的均值为11.56%,模型估计精度为88.44%.该方法能较为准确的对事故进行识别,为快速路中的交通事故识别提供有效的参考.     

准静态条件下三维脱轨系数临界值简化计算方法

根据轮轨系统坐标系间的变换关系, 在准静态条件下建立了轮轨接触斑三维受力分析模型, 推导了考虑轮对摇头角与轮轨蠕滑力的三维脱轨系数计算公式, 得到了脱轨临界状态时三维脱轨系数临界值的计算方法; 以LMA车轮踏面与CHN60钢轨廓形为例, 分析了轮对摇头角与摩擦因数对三维脱轨系数临界值的影响规律, 并与Nadal脱轨系数临界值进行了对比; 为简化三维脱轨系数的计算方法, 根据Shen-Hedrick-Elkins蠕滑模型讨论了不同轮对摇头角、摩擦因数与垂向力条件下Kalker线性合成蠕滑力与3倍库伦摩擦力间的比值关系; 分析了横向蠕滑力与纵向蠕滑力的比值随轮对摇头角与摩擦因数的变化规律, 提出了一种准静态条件的三维脱轨系数简化计算方法, 并与精确公式计算结果进行了对比。分析结果表明: 与三维脱轨系数临界值相比, 当轮对摇头角在1.5°以内时, 纵向蠕滑力在切向力中的占比要明显大于横向蠕滑力, 造成Nadal脱轨系数临界值具有一定的保守性, 但在轮对摇头角较大时, 横向蠕滑力在切向力中的占比达到了90%以上, Nadal与三维脱轨系数临界值计算结果基本相同; 车轮脱轨临界状态下轮轨接触斑内已达到纯滑动状态, 横向蠕滑力和纵向蠕滑力的比值基本不受摩擦因数影响, 并与轮对摇头角存在强线性关系; 与精确公式相比, 三维脱轨系数简化计算方法的误差在±5%以内, 可以满足工程应用的要求。     

城市道路网络路段状态相关性与道路间距分析

为优化城市道路网络布局, 以若干个城市高德地图实时路况的交通出行数据为基础, 研究了主干路、次干路、支路等不同等级道路的路段状态相关性, 设置了考虑相关性的道路间距, 计算了城市路网密度与等级级配。分析结果表明: 主干路和主干路、主干路和次干路、主干路和支路相关单元的平行距离均服从对数正态分布, 平行距离的85%分位值分别为2.4、1.0、0.9km; 主干路间距取为2.4~2.5km, 主干路间设置2条次干路, 次干路与主干路的间距为0.8~1.2km, 支路间距为0.2km或0.3km; 次干路和支路对应平行距离的85%分位值比较接近, 实际路网中次干路和支路的功能区分尚不明确。所得路网结构布局符合相关性、路网密度与等级级配的要求, 可为路网规划提供参考。     

船舶状态评估及预算系统

  目的  在船舶航行期间,需要通过分析船舶和主机的运行参数来客观判断主机当前的工作情况,从而准确评估主机的能效状态。  方法  以状态良好的船舶运行记录为样本,结合主成分分析法和BP神经网络算法,构建船舶的航行状态识别模型和主机油耗模型,并在船舶航行期间对船舶实时运行参数进行分析,得出船舶主机在当前工况下的油耗量正常值。以某30万吨级远洋散货船为例开展模型计算验证,将正常油耗值与实际油耗值进行对比,以二者的残差值为依据,进而评估当前的主机能效状态。  结果  计算结果显示,航行状态识别模型的正确率为98.05%,油耗模型的平均误差为3.47%,2种模型的可靠性均较高。  结论  研究成果可为智能船舶的能效管理提供一定的参考。