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Macro modelling of traffic flow using continuous timed Petri nets

机译:连续定时培养网的交通流量宏观建模

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In this paper, continuous timed Petri nets (CTPN) are used to develop a hybrid traffic model, where the network is modelled as a macroscopic model and calibrated by microscopic models. The concept of CTPN is used to build a modular model, where first the highway traffic system is decomposed into several systems, based on structural entities (highway segment, on- and off-ramp links), which are coalesced into a complete model. The result is a light, versatile and easily scalable stochastic model for traffic flow. The calibration and validation of the traffic model is performed through the comparison of basic traffic parameters (flow rate, density, and mean speed) obtained through the traffic model implemented and the commercial micro-modelling software, Aimsun, for part of Portugal's highway network. The results show that the proposed methodology results in a good trade-off between accuracy, simplicity, and computational cost.
机译:在本文中,使用连续定时Petri网(CTPN)来开发混合交通模型,其中网络被建模为宏观模型并被微观模型校准。 CTPN的概念用于构建一个模块化模型,其中首先,首先,基于结构实体(公路段,开除斜坡链路),首先,首先高速公路交通系统被分解成几个系统,该结构实体(公路段,开坡道链路)被聚集成完整的模型。 结果是用于交通流量的光,多功能且易于可扩展的随机模型。 流量模型的校准和验证是通过基本的交通参数(流量,密度和平均速度)通过通过实现的业务模型和商业微型建模软件,AIMSUN,葡萄牙公路网络的一部分进行比较来执行。 结果表明,所提出的方法在准确性,简单性和计算成本之间导致良好的权衡。

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