摘要翻译：
近年来，众包GPS探测数据作为实时交通信息的来源越来越受欢迎。已作出努力，从不同角度评价这类数据的质量。任何交通数据源的质量指标是描述数据准时性的延迟，它对实时操作、紧急响应和出行者信息系统至关重要。本文提供了一种测量探针数据延迟的方法，该方法相对于选定的参考源。虽然蓝牙重新识别数据被用作参考源，但该方法可以应用于任何其他选择的地面真相数据源（即自动车牌阅读器、电子收费标签）。该方法的核心是一个最大模式匹配算法，该算法适用于三个不同的适应度目标。为了测试该方法，使用便携式蓝牙传感器在多个高速公路路段收集了为期两周的样本现场参考数据。从一个私人供应商那里获得了等效的GPS探测数据，并对其延迟进行了评估。文中还讨论了不同时段的潜伏期、道路分割方案对潜伏期的影响、潜伏期对速度减慢的敏感性以及从减慢事件中恢复的情况。
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英文标题：
《A methodology for calculating the latency of GPS-probe data》
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作者：
Zhongxiang Wang, Masoud Hamedi, Stanley Young
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最新提交年份：
2018
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分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  Crowdsourced GPS probe data has been gaining popularity in recent years as a source for real-time traffic information. Efforts have been made to evaluate the quality of such data from different perspectives. A quality indicator of any traffic data source is latency that describes the punctuality of data, which is critical for real-time operations, emergency response, and traveler information systems. This paper offers a methodology for measuring the probe data latency, with respect to a selected reference source. Although Bluetooth re-identification data is used as the reference source, the methodology can be applied to any other ground-truth data source of choice (i.e. Automatic License Plate Readers, Electronic Toll Tag). The core of the methodology is a maximum pattern matching algorithm that works with three different fitness objectives. To test the methodology, sample field reference data were collected on multiple freeways segments for a two-week period using portable Bluetooth sensors as ground-truth. Equivalent GPS probe data was obtained from a private vendor, and its latency was evaluated. Latency at different times of the day, the impact of road segmentation scheme on latency, and sensitivity of the latency to both speed slowdown, and recovery from slowdown episodes are also discussed.
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PDF链接：
https://arxiv.org/pdf/1801.06128