Page 67 - Citiverse Use Case Taxonomy Overview - Use Case Identification Track

Transport and Mobility
Horizon

Predictive Transit Scheduling 1

Description Example Impacts SDG Alignment
Digital twins can be used to support AI- Madrid has embraced the power of AI
powered predictive transit scheduling by and digital twins to modernize its Reduced
simulating and visualizing real-time passenger public transport system, focusing on Congestion Ridership
Analysis
demand, traffic conditions, and operational bus scheduling. The Empresa
scenarios across city-wide transport networks. Municipal de Transportes optimizes
City stakeholders such as fleet operators and bus deployment based on passenger Increased Target 9.4
Reduced
transport planners can use these platforms to demand, traffic conditions, and Emissions Transit
dynamically adjust transport schedules, environmental data. This approach Adoption
optimize vehicle dispatching, and reduce minimizes delays, improves fleet
service gaps based on live and forecasted efficiency, and enhances the rider
data. IoT sensors across vehicles, stations, experience, especially during peak Operational Improved
and road networks can feed real-time hours or disruptions. AI helps to efficiency Accessibility
information into digital replicas of the transit predict ridership surges and adjust Target 11.2, 11.3, 11.6
system to enable continuous performance frequencies, contributing to
monitoring. AI could be leveraged to operational cost savings and Risk Level
anticipate surges in demand, recommend fleet sustainability goals.
adjustments, and optimize resource Public safety Low Medium High
allocation.
Stakeholder acceptance Low Medium High
Key Technologies
Data privacy and security Low Medium High
Digital
Twin Metaverse AR VR MR GAI AI IoT
Financial/operational Low Medium High

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