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Adaptive-AR Model with Drivers’ Prediction for Traffic Simulation

机译:具有驾驶员预测的交通模拟自适应AR模型

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We present a novel model called A2R—“Adaptive-AR”—based on a well-known continuum-based model called AR Aw and Rascle (2000) for the simulation of vehicle traffic flows. However, in the standard continuum-based model, vehicles usually follow the flows passively, without taking into account drivers' behavior and effectiveness. In order to simulate real-life traffic flows, we extend the model with a few factors, which include the effectiveness of drivers' prediction, drivers' reaction time, and drivers' types. We demonstrate that our A2R model is effective and the results of the experiments agree well with experience in real world. It has been shown that such a model makes vehicle flows perform more realistically and is closer to the real-life traffic thanAR (short for Aw and Rascle and introduced in Aw and Rascle (2000)) model while having a similar performance.
机译:我们基于一种称为AR Aw和Rascle(2000)的基于连续体的模型来提出一种称为A2R(“自适应AR”)的新颖模型,用于模拟车辆交通流量。但是,在基于连续体的标准模型中,车辆通常被动地遵循流量,而不考虑驾驶员的行为和有效性。为了模拟现实生活中的交通流,我们用几个因素扩展了模型,这些因素包括驾驶员预测的有效性,驾驶员反应时间和驾驶员类型。我们证明了我们的A2R模型是有效的,并且实验结果与现实世界中的经验非常吻合。已经表明,与AR(Aw和Rascle的缩写,并在Aw和Rascle(2000)模型中引入)相比,这种模型使车辆流量表现得更逼真并且更接近现实交通。

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