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雷达卡
摘要翻译：
无处不在的出租车轨迹数据使得将其应用于不同类型的出行分析成为可能。令人感兴趣的是，需要允许有人实时监控空间中任何位置的旅行势头和相关拥堵。然而，尽管在出租车数据可视化及其在出行分析中的适用性方面有大量的文献，但还没有简单的方法。为了测量出租车在某一地点的行程动量，现有的方法需要过滤在某一特定时间范围内停在某一地点的出租车轨迹，这在计算上是昂贵的。我们提出了一种替代的，计算更便宜的方法，基于对轨迹的向量场进行预处理。给出了生成向量核密度的算法，以估计城市空间中无出行模型的基于向量场的出行动量表示。这些算法作为一个名为VectorKD的开源GIS三维扩展在网上共享。我们利用北京每天1700万个出租车GPS点，在四天的时间里，演示如何从不断更新的出租车出行动量向量场中实时生成一系列预测，以查询城市中任何地方的兴趣点，如中央商务区或机场。该方法允许政策制定者自动识别到某个地点的旅行需求的时间净流入。所提出的方法比传统的轨迹选择查询快20倍以上。我们还利用进入北京首都国际机场和中央商务区的出租车数据，演示了如何几乎实时地量化由于出租车巡游或等待乘客而导致的进出站排队和拥挤时段的发生和大小，所有这些都不需要对数据拟合任何数学排队模型。
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英文标题：
《Online monitoring of local taxi travel momentum and congestion effects
using projections of taxi GPS-based vector fields》
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作者：
Xintao Liu, Joseph Y. J. Chow, Songnian Li
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最新提交年份：
2018
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分类信息：
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：
Ubiquitous taxi trajectory data has made it possible to apply it to different types of travel analysis. Of interest is the need to allow someone to monitor travel momentum and associated congestion in any location in space in real time. However, despite an abundant literature in taxi data visualization and its applicability to travel analysis, no easy method exists. To measure taxi travel momentum at a location, current methods require filtering taxi trajectories that stop at a location at a particular time range, which is computationally expensive. We propose an alternative, computationally cheaper way based on pre-processing vector fields from the trajectories. Algorithms are formalized for generating vector kernel density to estimate a travel-model-free vector field-based representation of travel momentum in an urban space. The algorithms are shared online as an open source GIS 3D extension called VectorKD. Using 17 million daily taxi GPS points within Beijing over a four-day period, we demonstrate how to generate in real time a series of projections from a continuously updated vector field of taxi travel momentum to query a point of interest anywhere in a city, such as the CBD or the airport. This method allows a policy-maker to automatically identify temporal net influxes of travel demand to a location. The proposed methodology is shown to be over twenty times faster than a conventional selection query of trajectories. We also demonstrate, using taxi data entering the Beijing Capital International Airport and the CBD, how we can quantify in nearly real time the occurrence and magnitude of inbound or outbound queueing and congestion periods due to taxis cruising or waiting for passengers, all without having to fit any mathematical queueing model to the data.
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PDF链接：
https://arxiv.org/pdf/1803.10686
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