A systems-design case study exploring how synthetic traffic simulation, congestion signals, and AI-assisted reasoning could support adaptive traffic-light coordination.
Adaptive Traffic AI System
ConceptConcept documentation: no implementation yet.
Urban traffic is often managed intersection by intersection, but congestion forms across networks. A smarter system needs to reason about the city as a connected environment.
The project starts with a digital city simulation that can generate synthetic traffic data, test signal strategies, and evaluate adaptive behavior safely.
Stack: Python · Simulation · AI Planning · Data Modeling · Systems Design
Tradeoff
Real traffic-light control is high-stakes and cannot be responsibly claimed without validated data, infrastructure access, and safety testing.
Outcome
Framed the project as a systems-design and simulation case study before any real-world deployment claim.
Tradeoff
Live city integration sounds impressive, but without access and validation it would be unrealistic and hard to test.
Outcome
Started from synthetic congestion patterns, simulation inputs, and evaluation metrics to test adaptive-control ideas safely.
Tradeoff
Optimizing one intersection can make a local signal look better while creating bottlenecks elsewhere.
Outcome
Treated coordination, constraints, congestion propagation, and network-level flow as core parts of the system design.
This project is a documented systems-design study. There is no built interface to show, so nothing here is presented as a screenshot.