Derivative data sets can be produced today using statistical models and Machine Learning to produce more accurate predictions of bus arrival times, along with improved capacity planning and deeper visualization. The edge use cases of citizen evacuation, for example may be due to natural and man-made causes. Today drive a need to review historical data.
The DSC (NIST GCTC subgroup) recommends communities establish long-term storage procedures for mass transit data. Typical real-time data from mass transit systems such as Clever Devices can be collected via JSON requests and credentials. Public Safety and critical infrastructure maintenance and plans are enabled by deep data dives and new machine learning algorithms.
Boston’s Massachusetts Bay Transit Authority (MBTA) operates the 4th busiest subway system in the U.S. after New York, Washington, and Chicago. If you live in or around the city you have probably ridden on it. The MBTA recently began publishing substantial amount of subway data through its public APIs.