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

python

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