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Analytical processes for evaluating public transportation usage, including trip volumes and travel hours.
Distinct from Communication Pattern Analysis: No candidates cover the domain of urban transit and commuting patterns.
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This project is a collection of big data frameworks and pipelines, including an Apache Hive analysis framework, a behavioral data analytics platform, a predictive analytics engine, and real-time data pipelines. It provides the infrastructure for building Extract, Transform, Load (ETL) workflows to process large datasets for distributed storage and SQL-based analysis. The system supports diverse analytical implementations, such as a predictive engine using linear regression for value forecasting and a real-time architecture that moves data through message brokers for immediate reporting. It in
Calculates total trips, expenditure, and peak travel hours to identify public transportation usage trends.