Transport for London (TfL) moves people and things around London. As the city grows, it is increasingly important to get the most efficient use of its transportation networks, safely, while keeping them moving.
TfL uses a wide range of data for operational purposes, but the underlying data is typically held in multiple disconnected systems. Freedom of Information requests have helped prove the value of sharing this data. TfL is embarking on a journey to make more of this data open and available in real time.
Roland Major shares the evolving big data and IoT architectures and services TfL is building to pull together these diverse datasets to better support operational teams and accelerate the identification and classification of disruption to improve response times for incidents. The biggest challenge is temporal and spatial correlations of related data. Time and location are the keys to bringing information sets together coherently, identifying related facts, and providing greater insight—but must be done while working with both restricted and confidential-rated data with very different security needs. Roland explains how bringing all these together required some unusual approaches to segregation and integration while working with the existing governance processes.
Roland Major is an enterprise architect at Transport for London, where he works on the Surface Intelligent Transport System, which is looking to improve the operation of the roads network across London and provide greater insight from existing and new data sources using modern data analytic techniques. Previously, Roland worked on event-driven architectures and solutions in the nuclear, petrochemical, and transport industries.
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