ETA Stability & Storm Robustness Report
This report documents the performance of the Self-Supervised Gated ETA Smoother under both normal weather and severe storm surges (where zone velocities drop globally).
1. Backtest Results
A. Normal Conditions (100 routes)
- Raw MIMO Inaccurate Bumps: 21
- Learned Smoother Inaccurate Bumps: 9 (57.1% reduction)
- Raw Prediction MAE: 10.47 mins
- Learned Display MAE: 10.67 mins
B. Storm Surge / Monsoon Conditions (100 routes - Out-Of-Distribution)
- Raw MIMO Inaccurate Bumps: 15
- Learned Smoother Inaccurate Bumps: 2 (86.7% reduction)
- Raw Prediction MAE: 19.05 mins
- Learned Display MAE: 19.23 mins
2. Key Senior Design Takeaways
- Self-Supervised Labeling: By utilizing Residual Convergence (error between actual delivery and raw ETA less than 2 mins) post-hoc in our database logs, we eliminated clean simulated label dependencies. The model trains purely on observable, historic order arrival events.
- Storm-Surge Robustness (Velocity Normalization):
- Standard classifiers misclassify slow rider speeds during storms as individual rider-stopped noise, filtering out legitimate delay warnings.
- By dividing velocity by the local zone average velocity, the smoother identifies that all riders are slow, and passes the storm-induced delays to the consumer immediately without smoothing lag.
Interview Talking Point: "To ensure the ETA smoother is resilient to out-of-distribution shifts (like monsoon storms), I normalized rider velocity against the running zone average. This prevents the classifier from misclassifying global weather slow-downs as individual rider noise. In our storm simulations, this normalization maintained low ETA display errors while reducing display jitter by over 60%."