Dynamic Rerouting Strategies for Pluvial Flash Flood Disruptions
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Who
Brian Kang
Background
As climate change intensifies, cities face a growing threat from pluvial flash floods — sudden, high-intensity rainfall events that overwhelm local drainage. These floods paralyze urban road networks and impede emergency response.
What We Built
This project proposes a real-time traffic rerouting model designed to minimize disruption during flash floods. The system evaluates road conditions based on elevation, water accumulation, and real-time traffic flow to determine the optimal path for vehicles.
Modeling & Research
Using GIS data, rainfall simulation, and multi-agent traffic routing frameworks, we classified flood zones by severity and tested rerouting performance under dynamic conditions. The algorithm adjusts for congestion penalties, route feasibility, and accessibility metrics.
Impact
The model demonstrated measurable reductions in traffic delay and improved routing efficiency in flood-vulnerable areas. It offers potential applications for integration into smart city infrastructure and disaster response systems.
Outcome
Awarded the Gold Medal at the International Greenwich Olympiad (IGO) for innovation in climate-adaptive infrastructure. The project has been recognized for advancing resilience modeling in urban environments.
Who
Brian Kang
Background
As climate change intensifies, cities face a growing threat from pluvial flash floods — sudden, high-intensity rainfall events that overwhelm local drainage. These floods paralyze urban road networks and impede emergency response.
What We Built
This project proposes a real-time traffic rerouting model designed to minimize disruption during flash floods. The system evaluates road conditions based on elevation, water accumulation, and real-time traffic flow to determine the optimal path for vehicles.
Modeling & Research
Using GIS data, rainfall simulation, and multi-agent traffic routing frameworks, we classified flood zones by severity and tested rerouting performance under dynamic conditions. The algorithm adjusts for congestion penalties, route feasibility, and accessibility metrics.
Impact
The model demonstrated measurable reductions in traffic delay and improved routing efficiency in flood-vulnerable areas. It offers potential applications for integration into smart city infrastructure and disaster response systems.
Outcome
Awarded the Gold Medal at the International Greenwich Olympiad (IGO) for innovation in climate-adaptive infrastructure. The project has been recognized for advancing resilience modeling in urban environments.
Who
Brian Kang
Background
As climate change intensifies, cities face a growing threat from pluvial flash floods — sudden, high-intensity rainfall events that overwhelm local drainage. These floods paralyze urban road networks and impede emergency response.
What We Built
This project proposes a real-time traffic rerouting model designed to minimize disruption during flash floods. The system evaluates road conditions based on elevation, water accumulation, and real-time traffic flow to determine the optimal path for vehicles.
Modeling & Research
Using GIS data, rainfall simulation, and multi-agent traffic routing frameworks, we classified flood zones by severity and tested rerouting performance under dynamic conditions. The algorithm adjusts for congestion penalties, route feasibility, and accessibility metrics.
Impact
The model demonstrated measurable reductions in traffic delay and improved routing efficiency in flood-vulnerable areas. It offers potential applications for integration into smart city infrastructure and disaster response systems.
Outcome
Awarded the Gold Medal at the International Greenwich Olympiad (IGO) for innovation in climate-adaptive infrastructure. The project has been recognized for advancing resilience modeling in urban environments.
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