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A case for real-time calibration of data-driven microscopic traffic simulation tools

机译:数据驱动的微观交通模拟工具实时校准的案例

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Despite recent technological advancements in alleviating roadway congestion, there is still a considerable amount of time and fuel wasted by travelers. In searching for solutions to mitigate congestion, a number of research efforts have been geared toward developing simulation tools to provide real-time performance measures. One of the challenges of such tools is that the underlying simulation model does not always adequately reflect field conditions outside of the time period for which it was calibrated. In this paper, this is highlighted when calibrating a model for two different periods. During this exercise, 1000 model replicates were generated to explore the sensitivity of potential calibration parameter values. From this analysis only one replicate was found to be adequately calibrated for both periods. This paper suggests that a real-time calibration algorithm should be included in online, data-driven microscopic traffic simulation tools.
机译:尽管最近在缓解道路拥堵方面有了技术进步,但仍然有大量时间和燃料被旅行者浪费。在寻找缓解拥塞的解决方案时,许多研究工作都致力于开发仿真工具以提供实时性能指标。这种工具的挑战之一是,基础的仿真模型无法始终充分反映其校准时间段以外的现场条件。在本文中,在两个不同时期校准模型时会突出显示该内容。在此练习中,生成了1000个模型副本以探索潜在校准参数值的敏感性。从该分析中,发现在两个时期内仅对一个重复进行了充分校准。本文建议将实时校准算法包含在在线的,数据驱动的微观交通仿真工具中。

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