Traffic modeling and prediction using sensor networks: Who will go where and when?

Zaihong Shuai, Sangseok Yoon, Songhwai Oh, Ming Hsuan Yang

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

We propose a probabilistic framework for modeling and predicting traffic patterns using information obtained from wireless sensor networks. For concreteness, we apply the proposed framework to a smart building application in which traffic patterns of humans are modeled and predicted through human detection and matching of their images taken from cameras at different locations. Experiments with more than 100,000 images of over 40 subjects demonstrate promising results in traffic pattern prediction using the proposed algorithm. The algorithm can also be applied to other applications, including surveillance, traffic monitoring, abnormality detection, and location-based services. In addition, the long-term deployment of the network can be used for security, energy conservation, and utilization improvement of smart buildings.

Original languageEnglish
Article number6
JournalACM Transactions on Sensor Networks
Volume9
Issue number1
DOIs
Publication statusPublished - 2012 Nov 1

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Intelligent buildings
Sensor networks
Location based services
Wireless sensor networks
Energy conservation
Energy utilization
Cameras
Monitoring
Experiments

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Cite this

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Traffic modeling and prediction using sensor networks : Who will go where and when? / Shuai, Zaihong; Yoon, Sangseok; Oh, Songhwai; Yang, Ming Hsuan.

In: ACM Transactions on Sensor Networks, Vol. 9, No. 1, 6, 01.11.2012.

Research output: Contribution to journalArticle

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