Case Study: Transport for London cuts congestion by 10% with Neo4j

Transport For London Aims to Cut Congestion by 10% and $750 Million a Year With a Digital Twin Powered by Neo4j

Transport for London (TfL) manages one of the world’s most complex transport networks, but it was struggling with disparate, low-quality, and non-real-time data that made it difficult to detect road incidents quickly and respond to congestion. Working with Neo4j and its graph database, TfL set out to build a digital twin of London’s transport network to move from a reactive to a real-time operating model.

Neo4j helped TfL connect multiple data sets into a graph-powered transport twin, enabling near real-time incident detection and better traffic decision-making. In testing, the system identified five incidents the control room missed, and TfL estimates the solution could cut congestion by 10%, saving about $750 million annually and giving drivers more than $1,500 in time value each year.

Transport for London

Andy Emmonds

Chief Transport Analyst