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Large-Scale Estimation in Cyberphysical Systems Using Streaming Data: A Case Study With Arterial Traffic Estimation

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5 Author(s)
Hunter, T. ; Dept. of Electr. & Comput. Sci., Univ. of California at Berkeley, Berkeley, CA, USA ; Das, T. ; Zaharia, M. ; Abbeel, P.
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Controlling and analyzing cyberphysical and robotics systems is increasingly becoming a Big Data challenge. We study the case of predicting drivers' travel times in a large urban area from sparse GPS traces. We present a framework that can accommodate a wide variety of traffic distributions and spread all the computations on a cluster to achieve small latencies. Our framework is built on Discretized Streams, a recently proposed approach to stream processing at scale. We demonstrate the usefulness of Discretized Streams with a novel algorithm to estimate vehicular traffic in urban networks. Our online EM algorithm can estimate traffic on a very large city network (the San Francisco Bay Area) by processing tens of thousands of observations per second, with a latency of a few seconds.

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Automation Science and Engineering, IEEE Transactions on  (Volume:10 ,  Issue: 4 )