Traffic Modeling and Prediction Using Sensor Networks: Who Will Go Where and When?
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.
traffic modeling and prediction algorithms design experimentation performance camera sensor network pedestrian detection image matching smart cameras