Quasi-dynamic estimation of o–d flows from traffic counts: Formulation,statistical validation and performance analysis on real data

The paper proposes a “quasi-dynamic” framework for estimation of origin–destination (o–d) flow from traffic counts, under the assumption that o–d shares are constant across a reference period, whilst total flows leaving each origin vary for each sub-period within the reference period. The advantage of this approach over conventional within-day dynamic estimators is that of reducing drastically the number of unknowns given the same set of observed time-varying traffic counts. Obviously, the gain in accuracy depends on how realistic is the underlying assumption that total demand levels vary more rapidly over time than o–d shares. Firstly, the paper proposes a theoretical specification of the quasi-dynamic estimator. Subsequently, it proposes empirical and statistical tests to check the quasi-dynamic assumption and then compares the performances of the quasi-dynamic estimator of o–d flows with both classical off-line simultaneous dynamic estimators and on-line recursive Kalman filter-based estimators. Experiments are carried out on the real test site of A4–A23 motorways in North-Eastern Italy. Results confirm the acceptability of the assumption of quasi-dynamic o–d flows, even under the hypothesis of constant distribution shares for the whole day and show that the quasi-dynamic estimator outperforms significantly the simultaneous estimator. Data also suggest that using the quasi-dynamic estimates instead of the simultaneous estimates as historical o–d flows improves significantly the performances of the Kalman filter, which strongly depends of the quality of the seed o–d flows. In addition, it is shown that the aggregation of quasi-dynamic o–d estimates across subsequent time slices represents also the most effective way to obtain o–d estimates for larger time horizons (e.g. hourly estimates). Finally, a validation based on an hold-out sample of link flows (i.e. counts not used as inputs in the o–d estimation/updating process) revealed the quasi-dynamic estimator to be overall more robust and effective with respect to the other tested estimators.     

Path flow estimator for planning applications in small communities

This paper presents an alternative planning framework to model and forecast network traffic for planning applications in small communities, where limited resources debilitate the development and applications of the conventional four-step travel demand forecasting model. The core idea is to use the Path Flow Estimator (PFE) to estimate current and forecast future traffic demand while taking into account of various field and planning data as modeling constraints. Specifically, two versions of PFE are developed: a base year PFE for estimating the current network traffic conditions using field data and planning data, if available, and a future year PFE for predicting future network traffic conditions using forecast planning data and the estimated base year origin–destination trip table as constraints. In the absence of travel survey data, the proposed method uses similar data (traffic counts and land use data) as a four-step model for model development and calibration. Since the Institute of Transportation Engineers (ITE) trip generation rates and Highway Capacity Manual (HCM) are both utilized in the modeling process, the analysis scope and results are consistent with those of common traffic impact studies and other short-range, localized transportation improvement programs. Solution algorithms are also developed to solve the two PFE models and integrated into a GIS-based software called Visual PFE. For proof of concept, two case studies in northern California are performed to demonstrate how the tool can be used in practice. The first case study is a small community of St. Helena, where the city’s planning department has neither an existing travel demand model nor the budget for developing a full four-step model. The second case study is in the city of Eureka, where there is a four-step model developed for the Humboldt County that can be used for comparison. The results show that the proposed approach is applicable for small communities with limited resources.     

Performance estimation model of a torque converter part I: Correlation between the internal flow field and energy loss coefficient

The objective of this paper is to improve the performance estimation model of the internal flow field of a torque converter. Compared with performance experiment results, the converter based on the one-dimensional model does not satisfy the performance requirements demanded in practice. Therefore, we need to develop more predictable and reliable performance estimation models. In order to obtain shape information on three-dimensional blade geometry, a process of reverse engineering conducts a torque converter assembly, impeller, turbine and stator. In addition, a CFD simulation including mesh generation and post-processing was carried out to extract equivalent parameters from the internal flow field. The internal flow field can be explained by analyze the correlation between a performance estimation model and CFD analysis. The equivalent performance model adopts the variation of energy loss coefficients for a given operating condition according to the application of a changing energy loss coefficient by the least mean squares method. The estimated equivalent model improves the agreement in performance between experiments and the theoretical model. This model can reduce the error to within about 3 percent. Furthermore, this procedure for predicted performance achieves eminence in the estimation of the capacity factor.     

一种基于卡尔曼滤波器的单级倒立摆的LQR方法

倒立摆的控制因其系统动态模型是非最小相位系统而很难用经典控制算法得到较好的控制效果.提出利用最优控制LQR方法来完成控制.由于利用最优控制,系统状态必须全部已知.考虑到系统噪声和量测噪声,根据分离性原理,利用LQR方法设计控制律,利用卡尔曼状态估计来完成系统状态的重构.仿真结果显示该方法具有良好的控制效果.     

Effective assessment of tyre–road friction coefficient using a hybrid estimator

Vehicle stability and active safety control depend heavily on tyre forces available on each wheel of a vehicle. Since tyre forces are strongly affected by the tyre–road friction coefficient, it is crucial to optimise the use of the adhesion limits of the tyres. This study presents a hybrid method to identify the road friction limitation; it contributes significantly to active vehicle safety. A hybrid estimator is developed based on the three degrees-of-freedom vehicle model, which considers longitudinal, lateral and yaw motions. The proposed hybrid estimator includes two sub-estimators: one is the vehicle state information estimator using the unscented Kalman filter and another is the integrated road friction estimator. By connecting two sub-estimators simultaneously, the proposed algorithm can effectively estimate the road friction coefficient. The performance of the proposed estimation algorithm is validated in CarSim/Matlab co-simulation environment under three different road conditions (high-μ, low-μ and mixed-μ). Simulation results show that the proposed estimator can assess vehicle states and road friction coefficient with good accuracy.     

一种新型自适应误差估计方法

对当前两种主要的自适应误差估计方法－基于残值的误差估计方法和基于应力修匀的误差估计方法作了详细的分析比较。在此基础上，从工程角度提出了一种新型自适应误差估计方法，通过分析和数值算例证明了它的有效性和可靠性。     

直接磁场定向控制在主变流器中的应用

由于直接磁场定向控制是通过对电机的反馈测量而计算出用于控制算法的定子或转子磁链,具有对电机参数变化的自适应作用,因而更加适合于对控制性能要求较高的主变流器的应用.结合电机的电流模型对转子磁链进行辨识,在控制策略中设计了转速、电流、磁链等多个闭环,采用了简单实用的偏差电压解耦方式,构建了完整的控制方案.通过在150 kVA机组上的各种试验,结果表明该控制系统运行稳定、动态性能好,满足实际运用的要求.     

随机截断数据的非参数积累生存风险估计的强相合性

本文计论了对于随机截断数据情形,积累生存风险Λ(t)的Nelson估 计和peterson估计的强相合性,并得至,1其收敛速度为O(n-1/2ln1/2n)。      

左截断的Turnbull估计与相应右截断的Kaplan-Meier估计的关系

本文推导只有左截断的Turnbull估计与某种右截断机制下的KaplanMeier估计间的关系。     

A generalized sensor location model for the estimation of network origin–destination matrices

This study examined the network sensor location problem by using heterogeneous sensor information to estimate link-based network origin–destination (O–D) demands. The proposed generalized sensor location model enables different sensors’ traffic monitoring capabilities to be used efficiently and the optimal number and deployment locations of both passive- and active-type sensors to be determined simultaneously without path enumeration. The proposed sensor location model was applied to solve the network O–D demand estimation problem. One unique aspect of the proposed model and solution algorithms is that they provide satisfactory network O–D demand estimates without requiring unreasonable assumptions of known prior information on O–D demands, turning proportions, or route choice probabilities. Therefore, the proposed model and solution algorithms can be practically used in numerous offline transportation planning and online traffic operation applications.