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Builds transit's first shared view across trains, bus routes, and stops.
Community safety operations were hindered by fragmented data. Agencies functioned in silos with sporadic data sharing, resulting in manual, time-consuming triage processes that could not keep pace with rising incident rates.
To resolve these inefficiencies, the project developed an integrated dashboard that established the City's first unified common operating picture across all transit modes (LRT, bus routes and stops). By breaking down silos and consolidating disparate data streams, the solution replaced slow, manual prioritizing with automated big data analytics. Created using open-source tools within existing resources.
The solution ensures efficient safety deployment and prioritizes response to incidents, improving the overall safety of transit riders across the entire system.
A major technical takeaway was that traditional predictive modeling relies too heavily on long-term historical data. The disruptions of the COVID-19 pandemic proved that legacy datasets can instantly become obsolete. The project team learned that a "just-in-time" data approach created a much more accurate, agile and realistic forecasting model for modern crisis management.





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