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Traffic Control System Utilizing Prove Vehicle Position Data

利用证明车辆位置数据的交通控制系统

基本信息

批准号：
15560452
负责人：
NAKATSUJI Takashi
金额：
$ 1.79万
依托单位：
HOKKAIDO UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2003
资助国家：
日本
起止时间：
2003 至 2005
项目状态：
已结题

项目摘要

1)Development of a Feedback Estimate of Traffic States Combining Probe Vehicle Date with Detector DataIntegration of probe vehicle data into conventional detector data made it possible to estimate of traffic density, space mean speed, and travel time on real time basis more accurately. In the estimation model, A Kalman filter, adopting a high-order macroscopic traffic flow model as state equation and both detector data and probe data as observation equation, was used as a feedback estimate technique. The model was effective for extensive traffic situations from free flow states to congested flow states, including incident states due to sudden change in traffic flow. From numerical experiments, it was identified that at least 4 to - 5% of market penetration and 10 to 30 second of sampling interval are required to estimate traffic states accurately.2)Estimate of O-D Flow and Turning Movements from Traffic Count DataIn order to estimate turning movements at intersections, a logit based st … More ochastic user equilibrium (SUE) model was integrated into a genetic algorithm (GA). Three derivative models were developed : 1) a doubly constrained estimator, 2) a singly constrained estimator, and 3) an OD-based estimator. In the first and second estimators, the SUE model was formulated as a joint trip distribution and traffic assignment (TD/TA) program, whereas in the third estimator, the SUE model was based on a standard origin-destination (OD) distribution program. At first, these turning movement estimators (TME) models were examined by applying them to various road networks, covering a virtual road network with simulated data to a large city-size network in real field. It was concluded that except a small road network, the doubly constrained model showed the best performance in terms of the estimation of link volume and turning ratios.3)Transformation between Uninterrupted and Interrupted Speed for Urban Road ApplicationsUnlike freeway, urban traffic stream is generally interrupted by signal, and thus observed traffic data may differ from the true (uninterrupted) flow characteristic. The transformation between interrupted and uninterrupted speed on urban road with signalized intersection is necessary before using detector data. Conventional shock wave model and two modified versions of shock wave boundary that address for the unrealistic characteristics of the conventional shock wave are developed. A method of integrating probes and detector data for speed transformation is also discussed. The proposed methods are tested with a virtual isolated signalized intersection by varying the approach demand. The numerical results suggest that all models can significantly reduce the difference between the actual and the estimated uninterrupted (or interrupted) speed compared to the case of no conversion. Less

1)结合探测车数据和检测器数据的交通状态反馈估计的开发将探测车数据集成到传统的检测器数据中，使得能够更准确地实时估计交通密度、空间平均速度和行程时间。在估计模型中，以高阶宏观交通流模型为状态方程，以检测器数据和探头数据为观测方程，采用卡尔曼滤波作为反馈估计技术。该模型适用于从自由流状态到拥挤流状态的广泛交通情况，包括由于交通流突然变化而导致的事件状态。通过数值实验，我们发现，至少需要4%- 5%的市场渗透率和10 - 30秒的采样间隔才能准确地估计交通状态。2）从交通量数据估计O-D流量和转向运动为了估计交叉口的转向运动，本文提出了一种基于logit的交通量估计方法。 ...更多信息将随机用户均衡（SUE）模型引入遗传算法（GA）中。开发了三种衍生模型：1）双约束估计，2）单约束估计，3）基于OD的估计。在第一和第二估计，SUE模型制定为一个联合出行分布和交通分配（TD/TA）计划，而在第三估计，SUE模型是基于一个标准的起点-目的地（OD）分布程序。首先，这些转向运动估计（TME）模型进行了检查，通过将它们应用到各种道路网络，覆盖一个虚拟的道路网络与模拟数据的大型城市规模的网络在真实的领域。结果表明，除小规模路网外，双约束模型在路段流量和转弯率的估计方面表现最好。3）城市道路应用中的不间断和间断速度转换与高速公路不同，城市交通流通常会被信号中断，因此观察到的交通数据可能与真实（不间断）的流量特性不同。在使用检测器数据之前，需要对信号交叉口的中断速度和不中断速度进行转换。针对传统激波模型不切实际的特点，提出了两种改进的激波边界模型。本文还讨论了一种将探头和探测器数据结合起来进行速度变换的方法。通过一个虚拟的孤立信号交叉口，通过不同的方法的需求进行了测试。数值结果表明，所有的模型可以显着减少之间的差异，实际和估计的不间断（或中断）的速度相比，没有转换的情况下。少