SEER: Metropolitan-Scale Traffic Perception Based on Lossy Sensory Data

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Executive Summary

Intelligent transportation systems have become increasingly important for the public transportation in Shanghai. In response, ShanghaiGrid (SG) aims to provide abundant intelligent transportation services to improve the traffic condition. A challenging service in SG is to estimate the real-time traffic condition on surface streets. In this paper, the authors present an innovative approach SEER to tackle this problem. In SEER, they deploy a cost-effective system of taxi traffic sensors. These taxi sensory data are found to be noisy and very lossy in both time and space.

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