Case Study: Purdue University achieves faster, more cost-effective traffic data analysis with Looker

Purdue University traffic research program cuts data analysis and batching from hours to minutes with BigQuery

Purdue University’s Joint Transportation Research Program (JTRP) needed a better way to store and analyze massive connected and autonomous vehicle data sets. As the data grew, its on-premises servers became expensive, slow, and difficult to scale, making it hard to support transportation research and deliver timely insights.

Looker helped support a cloud-based analytics approach built on Google Cloud BigQuery, allowing JTRP to query and analyze data much faster and more cost-effectively. With the new solution, a 24-hour highway analysis dropped from 90 minutes to seven minutes, and batching 10 billion records was reduced from a month to just five minutes, cutting costs from about $20,000 in field labor to roughly $10 in queries.

Purdue University

Darcy Bullock

Director, Joint Transportation Research Program,