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）结合探测车数据和探测器数据开发交通状态反馈估计将探测车数据集成到传统探测器数据中可以更准确地实时估计交通密度、空间平均速度和行驶时间。估计模型，卡尔曼滤波器，采用高阶宏观交通流模型作为状态方程，探测器数据和探针数据作为观测方程，作为反馈估计技术，该模型对于自由流状态下的广泛交通情况是有效的。到拥堵交通状态，包括由于交通流量突然变化而导致的事件状态，通过数值实验发现，至少需要 4% 至 - 5% 的市场渗透率和 10 至 30 秒的采样间隔才能准确估计交通状态。2 ）根据交通计数数据估计 O-D 流量和转弯运动为了估计交叉口的转弯运动，将基于 logit 的随机用户均衡 (SUE) 模型集成到遗传算法 (GA) 中，其中包括三个导数模型。开发：1）双重约束估计器，2）单约束估计器，3）基于 OD 的估计器在第一个和第二个估计器中，SUE 模型被制定为联合行程分配和交通分配（TD / TA）。程序，而在第三个估计器中，SUE 模型基于标准起点-目的地 (OD) 分配程序。首先，通过将这些转弯运动估计器 (TME) 模型应用于各种情况来进行检查。道路网络，将具有模拟数据的虚拟道路网络覆盖到现实中的大型城市规模网络，得出的结论是，除了小型道路网络外，双约束模型在路段流量和转弯的估计方面表现出最佳性能。 3）城市道路应用的不间断和中断速度之间的转换与高速公路不同，城市交通流通常会被信号中断，因此观测到的交通数据可能与真实（不间断）流量特征不同。在使用探测器数据之前，需要在有信号交叉口的城市道路上实现不间断的速度，并且针对传统冲击波的不切实际的特性，开发了两种修正版本的冲击波边界。还讨论了速度变换，通过改变接近需求，在虚拟隔离信号交叉口进行了测试。数值结果表明，与实际速度相比，所有模型都可以显着减小实际速度和估计的不间断（或中断）速度之间的差异。没有转换的情况下。