St. Lawrence Seaway operates the bridges and locks along the St. Lawrence Seaway system.  Our original ETAs where based on historic standard transit time as a way to have an idea (rough estimate) of when vessels would reach bridges and locks.  These times where not very accurate due to a number of reasons, however, they were meant only for internal use.  As cities have grown around the system the volume of traffic crossing the movable bridges has significantly increased and there became a need to provide this information to the public.
In the next generation, the bridge status estimated times were being calculated by a few different systems using different calculation methods depending on the bridge location and surrounding environment. Some bridges are located along a stretch of the Seaway unencumbered by locks or other physical impediments, while others are situated right next to locks. Depending on a vessel’s approach to a bridge, the estimated times need to be calculated differently.
It become clear that these methods were not good enough  There were many elements in which the St. Lawrence Seaway did not have control over and they realized more advance analysis and AI was needed to help get better ETA.
As a Premier Partner of AVEVA, Maya HTT has over 12 years of experience implementing and integrating the PI System at customers across various industries from Transportation and Marine to Datacenters and Manufacturing and Oil&Gas; St-Lawrence Seaway selected Maya HTT to accelerate their journey into advanced analytics leveraging the PI System data and external data sources.  
This topic details our journey evolving from the early days of using historic standards to today using AIS, SCADA, and historical data to build models in PI Asset Framework using PI Asset Analyitcs and machine learning to come up with better predictions.