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MTRL-enhanced simulation-based optimization for improving traffic resilience to urban flooding
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AI
DOI：10.1080/21680566.2026.2686383.png)
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Abstract
En 中文
With respect to urban flooding frequency, insufficient mitigation resources cannot cover all inundation sites, making efficient drainage strategy development crucial for improving traffic performance. Evaluating candidate strategies requires traffic simulations to capture the nonlinear impacts of flooding and drainage strategies on urban mobility, but these simulations are computationally expensive and non-differentiable. Conventional methods typically treat interrelated scenarios induced by drainage strategies independently, limiting the potential for knowledge transfer. To overcome these challenges, this study proposes a Simulation-Based Optimization framework based on Multi-Task Representation Learning (MTRL-SBO). A shared encoder is employed to extract task-invariant features, and Conditional Batch Normalization (CBN) is introduced to model task-specific variations. In addition, task-specific surrogate models are trained to accelerate strategy evaluation and guide the optimization search. A case study in central Guangzhou shows that the optimal drainage strategy reduces the average traffic delay by 10.3%, demonstrating superior optimization capability and practical value for urban flood mitigation planning.
Keywords:
Simulation-based optimization
multi-task representation learning
traffic performance
urban flood mitigation
mesoscopic traffic simulation
Journal
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Papers:
42
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