FairRelay — Delivery Time Estimation Model (v2)
Part of the FairRelay AI logistics platform.
Model Description
Predicts total delivery time (in minutes) for a courier's daily route based on package count, stops, distance, and temporal features. Trained on real last-mile delivery data from Cainiao-AI/LaDe-D (KDD 2023, Shanghai + Hangzhou, ~70K courier-days) combined with physics-informed synthetic data calibrated to FairRelay's estimate_route_time() formula.
Type: XGBRegressor Pipeline (StandardScaler + XGBoost) Task: Regression (delivery time in minutes)
Performance
- R²: 0.6206
- MAE: 73.01 minutes
- RMSE: 105.68 minutes
- Median AE: 46.03 minutes
- MAPE: 33.4%
- CV R² (5-fold): 0.5580 ± 0.2773
Input Features
| Feature | Importance |
|---|---|
num_packages |
0.1579 |
num_stops |
0.2307 |
total_distance_km |
0.1216 |
avg_distance_km |
0.0709 |
spatial_spread_km |
0.1569 |
start_hour |
0.0713 |
active_hours |
0.0852 |
packages_per_stop |
0.1055 |
Usage
from skops import io as sio
from huggingface_hub import hf_hub_download
import numpy as np
# Download and load
model_path = hf_hub_download(repo_id="muthuk1/fairrelay-delivery-time", filename="model.skops")
untrusted = sio.get_untrusted_types(file=model_path)
model = sio.load(model_path, trusted=untrusted)
# Predict: [num_packages, num_stops, total_distance_km, avg_distance_km,
# spatial_spread_km, start_hour, active_hours, packages_per_stop]
features = np.array([[25, 15, 12.5, 0.83, 5.2, 9, 6, 1.67]])
predicted_minutes = model.predict(features)
print(f"Estimated delivery time: {predicted_minutes[0]:.0f} minutes")
Training Data
- Cainiao-AI/LaDe-D: Real last-mile delivery data (Shanghai + Hangzhou splits, ~70K courier-days from 1.5M+ individual deliveries)
- FairRelay Synthetic: 20K samples with physics-informed noise, calibrated to
estimate_route_time()formula
Part of FairRelay
FairRelay is an AI-powered logistics platform for fair load consolidation and dispatch:
- 🚚 5-agent load consolidation pipeline (KMeans + OR-Tools CP-SAT)
- ⚖️ 8-agent fair dispatch pipeline (Gini optimization)
- 📊 XGBoost effort prediction + Thompson Sampling bandit
- 🌱 EV-aware routing with battery constraints
Built for LogisticsNow Hackathon 2026 — Challenge #5: AI Load Consolidation
License
MIT
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