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Dynamic OD estimation using three phase traffic flow theory

机译：基于三相交通流理论的动态OD估计

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Advanced Transportation Management and Information Systems (ATMIS) can use dynamic origin-destination (OD) demand models to make short-term predictions regarding developments in traffic states. However, existing dynamic OD prediction models do not achieve this reliably for two main reasons. First, this is a bi-level system that consists of a traffic flow process at the lower level and a dynamic OD process at the upper level. Due to the inherent non-convexity of bi-level systems, it is difficult to guarantee that any calculated solution is globally optimal. In this paper, we propose a new traffic flow model that uses real-time traffic data, such as traffic flows, speed and occupancy, collected from vehicle detectors, to address the difficulties that arise in existing bi-level programming formulations. Second, in order to estimate a dynamic OD demand between on and off-ramps on the freeways, a traffic flow model is needed to estimate the proportion of traffic moving between them. In this paper, we present a dynamic traffic estimation model based on Kerner's [1] three-phase traffic theory, which represents the complexity of traffic phenomena based on phase transitions' between free-flow, synchronized flow and moving jam phases, and on their complex nonlinear spatio-temporal features. The present model explains and estimates traffic congestion in terms of speed breakdown, phase transition and queue propagation. We show how a genetic algorithm can be used to solve this to estimate dynamic OD flows and the associated link, on and off-ramp flows during each time interval using traffic data collected from vehicle detection systems implemented on Korean freeways.
机译：高级运输管理和信息系统（ATMIS）可以使用动态始发地（OD）需求模型对交通状况的发展做出短期预测。但是，由于两个主要原因，现有的动态OD预测模型无法可靠地实现这一目标。首先，这是一个双层系统，由较低级别的业务流过程和较高级别的动态OD过程组成。由于双层系统固有的非凸性，很难保证任何计算出的解都是全局最优的。在本文中，我们提出了一种新的交通流模型，该模型使用从车辆检测器收集的实时交通数据，例如交通流，速度和占用率，来解决现有双层编程公式中出现的困难。其次，为了估算高速公路上/下坡道之间的动态OD需求，需要一种交通流模型来估算高速公路之间的交通流比例。在本文中，我们提出了一种基于Kerner [1]三相交通理论的动态交通量估算模型，该模型基于自由流，同步流和运动阻塞相之间的相变以及它们之间的相移来表示交通现象的复杂性复杂的非线性时空特征。本模型从速度分解，相变和队列传播方面解释和估计流量拥塞。我们展示了如何使用遗传算法来解决此问题，以使用从韩国高速公路上实施的车辆检测系统收集的交通数据，估算每个时间间隔内的动态OD流量以及相关的链接，匝道流量和匝道流量。

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