Simulation environment creates traffic states and vehicle flow.
Case Study2025
Adaptive Traffic AI System
A systems-design case study for adaptive traffic-light coordination using simulation and AI-assisted reasoning.
A systems-design case study exploring how synthetic traffic simulation, congestion signals, and AI-assisted reasoning could support adaptive traffic-light coordination.
Problem
What this project solves
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.
Solution
How I approached it
The project starts with a digital city simulation that can generate synthetic traffic data, test signal strategies, and evaluate adaptive behavior safely.
Architecture
System structure
Data layer captures intersections, wait times, density, and congestion signals.
AI layer proposes signal adjustments based on network state.
Dashboard layer visualizes intersections, interventions, and outcomes.
Decisions
Tradeoffs and outcomes
Simulation before real-world claims
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.
Synthetic data before city integration
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.
City-wide coordination over isolated optimization
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.
Proof
Evidence and impact
This case study shows systems thinking, AI planning, simulation-first development, and responsible boundaries around infrastructure automation.
Mental model for data collection and preparation
Simulation-first development strategy
AI reasoning system concept
Evaluation and dashboard direction
Roadmap
Next iteration
Build the first minimal grid simulation.
Define baseline algorithms for comparison.
Create an evaluation dashboard.