In the intricate tapestry of urban development, the efficiency and resilience of public transport systems emerge as linchpins in fostering sustainable, livable cities. Addressing this need, a pioneering approach combining Multi-Objective Optimisation (MOO) and Digital Twin modeling presents a paradigm shift in route planning for smart city public transport networks.
This innovative scheme, showcased in a study focused on optimizing route planning in Fuzhou City, China, pioneers a dual-objective optimisation strategy. By concurrently minimising operational costs and reducing emissions, the framework cultivates a transportation network that balances economic efficiency with environmental consciousness.
At the heart of this approach lies a metaheuristic approach; a general-purpose problem solving method, known as the Modified Bat Algorithm, suited to MOO complexities. Its ability to efficiently explore solution spaces and converge to optimal or near-optimal solutions positions it as a robust optimiser for smart city public transport route planning.
Integral to the proposed scheme is the integration of Digital Twin technology, which provides a dynamic simulation environment mirroring the physical transportation infrastructure. This virtual representation enables comprehensive evaluations, ensuring the adaptability and robustness of the route planning scheme in response to dynamic urban dynamics and unforeseen events.
Through a comparative analysis, the study underscores the superiority of the MOO framework enhanced by the Modified Bat Algorithm over conventional optimisation algorithms. This not only highlights the efficacy of the approach in addressing the complexities of smart city public transport systems but also emphasises its contribution to both environmental sustainability and economic efficiency objectives.
In essence, the integration of MOO and Digital Twin modelling represents a transformative leap towards sustainable and resilient route planning in smart city public transport networks. By synergistically optimising operational costs and reducing emissions, this approach paves the way for more inclusive, accessible, and environmentally conscious urban mobility solutions, ultimately enhancing the quality of life for residents and visitors alike.
Source
Sustainable and robust route planning scheme for smart city public transport based on multi-objective optimization: Digital twin model, Sustainable Energy Technologies and Assessments, 2024-05
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