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

Transport for London cuts congestion by 10% with Neo4j

Transport for London (TfL) faced the challenge of managing one of the world's most complex transport networks, which was costing the city billions annually in congestion. With disparate, low-quality data and a reactive approach, TfL struggled to detect and address the 20,000 yearly transport incidents quickly. They partnered with Neo4j to build a solution powered by a graph database to create a digital twin of the network.

Using Neo4j's graph solution, TfL implemented a digital twin that connected disparate data sets to model the transport network in near real-time. This allowed them to detect incidents faster and test scenarios virtually. The solution helped TfL cut its incident detection time dramatically, contributing to an estimated 10% reduction in congestion. This equates to $750 million in annual economic savings and saves each driver an estimated $1,500 worth of time per year.

Transport for London