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首页> 外文期刊>Journal of Transportation Engineering >Establishing Multisource Data-Integration Framework for Transportation Data Analytics
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Establishing Multisource Data-Integration Framework for Transportation Data Analytics

机译：建立交通数据分析的多源数据集成框架

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摘要

In recent years, with the advancement in traffic sensing, data storage, and communication technologies, the availability and diversity of transportation data have increased substantially. When the volume and variety of traffic data increase dramatically, integrating multisource traffic data to conduct traffic analysis is becoming a challenging task. The heterogeneous spatiotemporal resolutions of traffic data and the lack of standard geospatial representations of multisource data are the main hurdles for solving the traffic data-integration problem. In this study, to overcome these challenges, a transportation data-integration framework based on a uniform geospatial roadway referencing layer is proposed. In the framework, on the basis of traffic sensors' locations and sensing areas, transportation-related data are classified into four categories, including on-road segment-based data, off-road segment-based data, on-road point-based data, and off-road point-based data. Four data-integration solutions are proposed accordingly. An iterative map conflation algorithm as a core component of the framework is proposed for integrating the on-road segment-based data. The overall integration performance of the four types of data and the efficiency of the iterative map conflation algorithm in terms of percentage of integrated roadway segments and computation time are analyzed. To produce efficient transportation analytics, the proposed framework is implemented on an interactive data-driven transportation analytics platform. Based on the implemented framework, several case studies of real-world transportation data analytics are presented and discussed.
机译：近年来，随着交通传感，数据存储和通信技术的进步，交通数据的可用性和多样性已经大大增加。当交通数据的数量和种类急剧增加时，集成多源交通数据以进行交通分析正成为一项具有挑战性的任务。交通数据的时空异构和缺乏多源数据的标准地理空间表示是解决交通数据集成问题的主要障碍。为了克服这些挑战，提出了一种基于统一地理空间道路参考层的交通数据集成框架。在该框架中，基于交通传感器的位置和感知区域，与交通相关的数据分为四类，包括基于路段的数据，基于路段的数据，基于路点的数据以及基于越野点的数据。相应地提出了四种数据集成解决方案。提出了一种迭代地图合并算法作为框架的核心组件，用于集成基于路段的数据。从综合路段的百分比和计算时间的角度，分析了四种数据的整体集成性能以及迭代地图合并算法的效率。为了产生有效的运输分析，建议的框架在交互式数据驱动的运输分析平台上实施。在已实现的框架的基础上，提出并讨论了一些实际运输数据分析的案例研究。

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