The proposal aims to build a multimodal digital twin platform, namely GreenRipple, that helps Transport for Greater Manchester (TfGM) to monitor their transport networks in real-time. The platform will ingest data from multiple sources for buses, trams, trains, bike hires and private vehicles to detect unplanned major disruptions in real-time. How disruptions are propagated across the entire network will be visualised. A carbon emission shifting model will be developed to calculate how citizens have made their decisions during disruptions.
With a time-machine-like feature, users can look back at a certain time in history to reflect on their decisions and make better ones in the future. With the dates that certain policies were introduced in mind, TfGM will also be able to analyse the impact of policies over time.
The proposed solution is designed to be ready for the UK’s coming railway nationalisation and nationwide bus franchising, with the flexibility to adapt quickly to other European cities that have similar setups for centralised public transport data. The project reduces service replanning costs for mobility operators and delivers enhanced passenger experience for citizens. This is the first time in history that transport bodies and local authorities can produce policy-driven metrics, to prove they are spending taxpayers’ money efficiently and sustainably.
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TfGM experiences 30% of buses being delayed but no approach is currently used to determine the causes. A digital twin is sought to address this challenge.
GreenRipple provides an AI-powered digital twin platform that allows TfGM to trace root causes of disruptions (car accidents, roadworks, local events) and their ripple effects (impacted bus services).
A web-based digital twin trialled at TfGM’s control centre and application programming interfaces (APIs) ready to be integrated with 3rd party journey mapping applications.
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