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Origin-destination trip table estimation based on subarea network OD flow and vehicle trajectory data

机译:基于分区OD流和车辆轨迹数据的原点-目的地行程表估计

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Identifying accurate origin-destination (O-D) travel demand is one of the most important and challenging tasks in the transportation planning field. Recently, a wide range of traffic data has been made available. This paper proposes an O-D estimation model using multiple field data. This study takes advantage of emerging technologies- car navigation systems, highway toll collecting systems and link traffic counts- to determine O-D demand. The proposed method is unique since these multiple data are combined to improve the accuracy of O-D estimation for an entire network. We tested our model on a sample network and found great potential for using multiple data as a means of O-D estimation. The errors of a single input data source do not critically affect the model's overall accuracy, meaning that combining multiple data provides resilience to these errors. It is suggested that the model is a feasible means for more reliable O-D estimation.
机译:在运输计划领域,确定准确的出发地(O-D)旅行需求是最重要和最具挑战性的任务之一。近来,已经提供了广泛的交通数据。本文提出了一种使用多个现场数据的O-D估计模型。这项研究利用新兴技术(汽车导航系统,高速公路收费系统和链接交通计数)来确定O-D需求。所提出的方法是独特的,因为这些多个数据被组合以提高整个网络的O-D估计精度。我们在示例网络上测试了我们的模型,发现使用多个数据作为O-D估计方法的巨大潜力。单个输入数据源的错误不会严重影响模型的整体准确性,这意味着组合多个数据可为这些错误提供弹性。建议该模型是进行更可靠的O-D估计的可行方法。

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