摘要翻译：
SPaT（信号相位和定时）消息为每条车道描述信号交叉口处的当前相位以及该相位的剩余时间的估计。准确的SPaT信息可用于为车辆构造速度剖面，从而在接近或离开十字路口时减少其燃料消耗。本文利用实时信号相位测量，提出了半驱动信号交叉口SPaT估计算法。使用来自马里兰州蒙哥马利县两个交叉口的高分辨率数据对算法进行了评估。该算法可以很容易地在信号控制器上实现。这项研究支持三个发现。首先，与单纯基于历史数据的预测相比，实时信息极大地提高了剩余时间预测的准确性。第二，随着时间的增加，对剩余时间的预测可能会增加或减少。第三，由于司机在预测“绿色结束”和“红色结束”时对误差的权重不同，采用两种不同方法的司机可能倾向于对同一阶段的剩余时间进行不同的估计。
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英文标题：
《Estimating Phase Duration for SPaT Messages》
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作者：
Shahana Ibrahim, Dileep Kalathil, Rene O. Sanchez and Pravin Varaiya
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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：
  A SPaT (Signal Phase and Timing) message describes for each lane the current phase at a signalized intersection together with an estimate of the residual time of that phase. Accurate SPaT messages can be used to construct a speed profile for a vehicle that reduces its fuel consumption as it approaches or leaves an intersection. This paper presents SPaT estimation algorithms at an intersection with a semi-actuated signal, using real-time signal phase measurements. The algorithms are evaluated using high-resolution data from two intersections in Montgomery County, MD. The algorithms can be readily implemented at signal controllers. The study supports three findings. First, real-time information dramatically improves the accuracy of the prediction of the residual time compared with prediction based on historical data alone. Second, as time increases the prediction of the residual time may increase or decrease. Third, as drivers differently weight errors in predicting `end of green' and `end of red', drivers on two different approaches may prefer different estimates of the residual time of the same phase.
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PDF链接：
https://arxiv.org/pdf/1710.05394