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Real-Time Road Traffic Analysis and Prediction

Tags:Machine LearningBatch ProcessingWindowingEvent Time ProcessingAggregationFile SourceIMDG StoragePipeline API

This demo shows how to use Jet for real-time machine learning use-cases. It combines real-time model training and prediction into one Jet Pipeline.

Jet reads the traffic data from a CSV file. The data source can be easily replaced with a stream such as Kafka. Every input record contains timestamp, location and respective car count. After the ingestion, the records are routed to two separate computations.

The first computation uses the car count to train the model. The model contains the car count trends computed using a linear regression. The second computation combines current car count, with the trend from the previous week, to predict a car count in the next two hours. Prediction results are stored in a file in predictions/directory.

DAG Visualization

Output

Hazelcast Jet

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