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Statistical Origin-destination generation with multiple sources

机译:具有多个来源的统计来源地的生成

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Any trajectory is always generated with its origin and destination. Origin-destination (OD) generation for trips plays an important role in many applications such as trajectory mining, traffic simulation, or marketing. In previous work on traffic pattern recognition, microscopic ODs for limited areas are estimated with probe-car data, while macroscopic ODs for broad areas are usually generated by using road-traffic-census data. In this paper, we propose a microscopic OD determination method for broad areas with the same data and landmark information, which is based on an L1-regularized Poisson regression. We demonstrate performance improvements over baseline methods in numerical experiments with a massive data set from Tokyo.
机译:任何轨迹都会始终带有其起点和终点。旅行的起点(OD)生成在许多应用中(例如轨迹挖掘,交通模拟或市场营销)起着重要作用。在先前的交通模式识别工作中,有限区域的微观OD是用探测车数据估算的,而广域的宏观OD通常是通过道路交通普查数据生成的。在本文中,我们提出了一种基于L1正则化Poisson回归的,具有相同数据和地标信息的大面积显微OD测定方法。我们使用东京的大量数据证明了在数值实验中性能优于基线方法。

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