Many people who live and work in Silicon Valley depend on Caltrain for transportation. And because the Silicon Valley Data Science headquarters are in Mountain View, not far from a station, Caltrain is literally in our own backyard. Our R&D program emphasizes investigating emerging technologies, including Apache Spark and Kafka, and giving back to the community to which we belong. So with Caltrain Rider (iOS, Android), we have been using data science techniques to understand and predict delays in the Caltrain system. It’s a complete data product, comprising a mobile app, predictive backend and deriving input from public sources and a a sensor platform.
We built a view of the Caltrain systems using information from some of our own sensors (cameras and microphones) combined with information from publicly available sources (Twitter, Caltrain API). You might think this would be easy, since Caltrain provides schedules and even a real-time API. But as with all trains, Caltrain sometimes gets behind schedule. Sometimes its API estimates are off, and at other times its API crashes and provides no information. So we’ve been attempting to get a larger view of the Caltrain system from a variety of sources.
We consume, integrate and analyze a variety of local, distributed, redundant, and possibly unreliable information sources in order to understand the state of the system. Data sources are never completely reliable or consistent across a large system, so this gives us experience producing predictions from messy and possibly erroneous data.
We’re excited to be part of Datapalooza, and talk about what’s involved in creating a complete data product: from R&D, to public launch. We’ll use a strong Q&A format to drive our session. On any one of the days, there’ll be a host leader from SVDS, plus some data scientists and engineers from our teams who worked on the project.