摘要翻译：
城市道路网络中时空交通现象的建模和预测问题对于交通热点的检测和预测等交通应用具有重要意义。本文提出了一种分散式数据融合与主动感知(D2FAS)算法，用于移动传感器主动探索道路网络，收集和同化信息最丰富的数据，以预测交通现象。我们分析了D2FAS的时间复杂度和通信复杂度，证明了它可以在大量观测和传感器的情况下很好地扩展。我们从理论上保证了它的预测性能与复杂的集中式稀疏近似高斯过程(GP)模型的预测性能相当：这样的稀疏近似GP模型的计算可以并行化并分布在移动传感器之间（以类似Google的MapReduce范式），从而实现高效和可扩展的预测。我们还从理论上保证了它的主动传感性能在各种实际环境条件下都有所提高。对实际城市道路网络数据的实证评估表明，我们提出的D2FAS算法比现有的集中式算法具有更高的时间效率和可扩展性，同时具有相当的预测性能。
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英文标题：
《Decentralized Data Fusion and Active Sensing with Mobile Sensors for
  Modeling and Predicting Spatiotemporal Traffic Phenomena》
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作者：
Jie Chen, Kian Hsiang Low, Colin Keng-Yan Tan, Ali Oran, Patrick
  Jaillet, John M. Dolan and Gaurav S. Sukhatme
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Distributed, Parallel, and Cluster Computing        分布式、并行和集群计算
分类描述：Covers fault-tolerance, distributed algorithms, stabilility, parallel computation, and cluster computing. Roughly includes material in ACM Subject Classes C.1.2, C.1.4, C.2.4, D.1.3, D.4.5, D.4.7, E.1.
包括容错、分布式算法、稳定性、并行计算和集群计算。大致包括ACM学科类C.1.2、C.1.4、C.2.4、D.1.3、D.4.5、D.4.7、E.1中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Multiagent Systems        多智能体系统
分类描述：Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
涵盖多Agent系统、分布式人工智能、智能Agent、协调交互。和实际应用。大致涵盖ACM科目I.2.11类。
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一级分类：Computer Science        计算机科学
二级分类：Robotics        机器人学
分类描述：Roughly includes material in ACM Subject Class I.2.9.
大致包括ACM科目I.2.9类的材料。
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英文摘要：
  The problem of modeling and predicting spatiotemporal traffic phenomena over an urban road network is important to many traffic applications such as detecting and forecasting congestion hotspots. This paper presents a decentralized data fusion and active sensing (D2FAS) algorithm for mobile sensors to actively explore the road network to gather and assimilate the most informative data for predicting the traffic phenomenon. We analyze the time and communication complexity of D2FAS and demonstrate that it can scale well with a large number of observations and sensors. We provide a theoretical guarantee on its predictive performance to be equivalent to that of a sophisticated centralized sparse approximation for the Gaussian process (GP) model: The computation of such a sparse approximate GP model can thus be parallelized and distributed among the mobile sensors (in a Google-like MapReduce paradigm), thereby achieving efficient and scalable prediction. We also theoretically guarantee its active sensing performance that improves under various practical environmental conditions. Empirical evaluation on real-world urban road network data shows that our D2FAS algorithm is significantly more time-efficient and scalable than state-of-the-art centralized algorithms while achieving comparable predictive performance.
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PDF链接：
https://arxiv.org/pdf/1206.6230