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Urban mobility systems face growing challenges. While various smart mobility solutions have been proposed, there is still a lack of comprehensive tools for assessing the impact of these solutions in a dynamic and iterative manner. Recent literature increasingly adopts the Digital Twin (DT) concept. However, DTs have conventionally been framed around automating solutions, which often conflict with the requirements of human-driven planning in socio-technical systems, leading to ambiguities in how DTs should be defined and operationalised for mobility planning. To fill this gap, this paper presents the concept of a Digital Twin Federation (FedDT) designed for comprehensive urban mobility assessments. Firstly, a definition of the FedDT concept is established based on four conceptual pillars, including physical & digital system exchange, system monitoring & planning, outcome evaluation & immersive experience, and human-in-the-loop control. Building on the concept and 5 stakeholder co-design sessions, we present a functional FedDT architecture that enables iterative, bidirectional data exchange between the physical and digital mobility systems, thereby supporting a data-driven decision-making process while ensuring the interests of stakeholders are continuously integrated. Finally, we demonstrate how the FedDT architecture can be instantiated through a proof-of-concept application framework. This framework serves as a research agenda that guides and links the development of separate modules to reduce private vehicle dependency in Amsterdam, the Netherlands. Overall, this work lays a conceptual and architectural foundation for FedDT, advancing the implementation of integrated digital twin solutions for sustainable mobility systems.
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Urban mobility systems face growing challenges. While various smart mobility solutions have been proposed, there is still a lack of comprehensive tools for assessing the impact of these solutions in a dynamic and iterative manner. Recent literature increasingly adopts the Digital Twin (DT) concept. However, DTs have conventionally been framed around automating solutions, which often conflict with the requirements of human-driven planning in socio-technical systems, leading to ambiguities in how DTs should be defined and operationalised for mobility planning. To fill this gap, this paper presents the concept of a Digital Twin Federation (FedDT) designed for comprehensive urban mobility assessments. Firstly, a definition of the FedDT concept is established based on four conceptual pillars, including physical & digital system exchange, system monitoring & planning, outcome evaluation & immersive experience, and human-in-the-loop control. Building on the concept and 5 stakeholder co-design sessions, we present a functional FedDT architecture that enables iterative, bidirectional data exchange between the physical and digital mobility systems, thereby supporting a data-driven decision-making process while ensuring the interests of stakeholders are continuously integrated. Finally, we demonstrate how the FedDT architecture can be instantiated through a proof-of-concept application framework. This framework serves as a research agenda that guides and links the development of separate modules to reduce private vehicle dependency in Amsterdam, the Netherlands. Overall, this work lays a conceptual and architectural foundation for FedDT, advancing the implementation of integrated digital twin solutions for sustainable mobility systems.
Existing activity-based and agent-based simulations alone often failed to capture the interaction between individual activity scheduling and detailed urban traffic dynamics. ActivitySim provides a representation of individual activity schedulings but often lacks detailed traffic dynamics, whereas MATSim can capture detailed interactions between travellers and mobility systems but often overlooks several decision-making factors, such as activity scheduling shift, household interactions and land-use influences. To address these limitations, this paper presents an Activity- and Agent-based Co-simulation framework that integrates ActivitySim and MATSim, both of which are open-source software popularly adopted in each research community. ActivitySim generates individual activity schedules and location choices, which serve as synthetic travel demand input for MATSim. MATSim then simulates detailed mobility interactions, with its outputs aggregated into zonal level-of-service matrices and fed back to ActivitySim for iterative scheduling adjustments. The feedback loop bridges the strengths of both models and is applied to the MRDH (Rotterdam-The Hague Metropolitan) region in the Netherlands. The initial MRDH model for the base-year reference scenario demonstrates that the proposed co-simulation framework effectively replicates existing mobility patterns, paving the way for fine-grained intervention evaluations like ride-hailing services in the future.
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Existing activity-based and agent-based simulations alone often failed to capture the interaction between individual activity scheduling and detailed urban traffic dynamics. ActivitySim provides a representation of individual activity schedulings but often lacks detailed traffic dynamics, whereas MATSim can capture detailed interactions between travellers and mobility systems but often overlooks several decision-making factors, such as activity scheduling shift, household interactions and land-use influences. To address these limitations, this paper presents an Activity- and Agent-based Co-simulation framework that integrates ActivitySim and MATSim, both of which are open-source software popularly adopted in each research community. ActivitySim generates individual activity schedules and location choices, which serve as synthetic travel demand input for MATSim. MATSim then simulates detailed mobility interactions, with its outputs aggregated into zonal level-of-service matrices and fed back to ActivitySim for iterative scheduling adjustments. The feedback loop bridges the strengths of both models and is applied to the MRDH (Rotterdam-The Hague Metropolitan) region in the Netherlands. The initial MRDH model for the base-year reference scenario demonstrates that the proposed co-simulation framework effectively replicates existing mobility patterns, paving the way for fine-grained intervention evaluations like ride-hailing services in the future.
