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GridLock — Traffic Demand Prediction
Built a traffic-demand prediction solution using gradient-boosting models, with experiments focused on validation design, distribution shift, and road-type-aware prediction.
PythonXGBoostCatBoostLightGBMFeature Engineering
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SYSTEM ARCHITECTURE
Model selection and blending are evaluated against held-out data; no leaderboard score is assumed by this diagram.
Problem
Traffic demand varies by time and road type, and validation data may not fully reflect the distribution encountered during evaluation.
Architecture
Feature preparation → gradient-boosting models → error analysis → road-type-aware model blending.
Challenges
- Analyzed prediction errors across time and road types
- Investigated differences between local validation and target evaluation
- Compared XGBoost, CatBoost, and LightGBM approaches
- Tested model blending and road-type-aware routing strategies
Benchmarks
Use leaderboard scores or post-mortem metrics only when they can be verified in the project repository and clearly labeled by evaluation split.
Lessons Learned
- Validation design can be as important as model complexity
- Different boosting models may perform differently across data regimes
- Error analysis helps identify distribution shift and model weaknesses