摘要翻译：
本研究提出一种深度生成对抗架构(GAA)用于全网时空流量状态估计。该算法能够将交通流理论与神经网络相结合，从而提高交通状态估计的精度。它由两个长短时记忆神经网络组成，捕捉交通流量和交通密度在时间和空间上的相关性。LSTM神经网络中的一个称为判别网络，其目的是最大限度地为真实交通状态矩阵（即给定时空区域内的交通流量和交通密度）和由另一神经网络生成的交通状态矩阵分配正确标签的概率。另一种LSTM神经网络称为生成网络，其目的是生成业务状态矩阵，以最大化识别网络为其分配真标记的概率。同时训练两个LSTM神经网络，使得训练后的生成网络能够产生与训练数据集中的流量矩阵相似的流量矩阵。给出一个含有丢失值的交通状态矩阵，利用三个定义的损失函数上的反向传播将损坏的矩阵映射到一个潜在空间。然后将映射向量通过预先训练的生成网络以估计损坏矩阵的丢失值。利用从华盛顿州西雅图和加利福尼亚州圣地亚哥收集的环路探测器数据，将所提出的GAA方法与现有的贝叶斯网络方法进行了比较。实验结果表明，GAA算法比贝叶斯网络算法在交通状态估计方面具有更高的精度。
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英文标题：
《A Deep Generative Adversarial Architecture for Network-Wide
  Spatial-Temporal Traffic State Estimation》
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作者：
Yunyi Liang, Zhiyong Cui, Yu Tian, Huimiao Chen, Yinhai Wang
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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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一级分类：Mathematics        数学
二级分类：Optimization and Control        优化与控制
分类描述：Operations research, linear programming, control theory, systems theory, optimal control, game theory
运筹学，线性规划，控制论，系统论，最优控制，博弈论
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英文摘要：
  This study proposes a deep generative adversarial architecture (GAA) for network-wide spatial-temporal traffic state estimation. The GAA is able to combine traffic flow theory with neural networks and thus improve the accuracy of traffic state estimation. It consists of two Long Short-Term Memory Neural Networks (LSTM NNs) which capture correlation in time and space among traffic flow and traffic density. One of the LSTM NNs, called a discriminative network, aims to maximize the probability of assigning correct labels to both true traffic state matrices (i.e., traffic flow and traffic density within a given spatial-temporal area) and the traffic state matrices generated from the other neural network. The other LSTM NN, called a generative network, aims to generate traffic state matrices which maximize the probability that the discriminative network assigns true labels to them. The two LSTM NNs are trained simultaneously such that the trained generative network can generate traffic matrices similar to those in the training data set. Given a traffic state matrix with missing values, we use back-propagation on three defined loss functions to map the corrupted matrix to a latent space. The mapping vector is then passed through the pre-trained generative network to estimate the missing values of the corrupted matrix. The proposed GAA is compared with the existing Bayesian network approach on loop detector data collected from Seattle, Washington and that collected from San Diego, California. Experimental results indicate that the GAA can achieve higher accuracy in traffic state estimation than the Bayesian network approach.
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PDF链接：
https://arxiv.org/pdf/1801.03818