FairRelay — Driver Effort Prediction Model
Part of the FairRelay AI logistics platform.
Model Description
Driver Effort Prediction Model
Type: XGBRegressor Pipeline
Framework: scikit-learn Pipeline + XGBoost
Task: Regression
Performance
- R²: 0.9702
- MAE: 3.6631
- RMSE: 6.9566
- CV R² (5-fold): 0.9732 ± 0.0016
Input Features
| Feature | Importance |
|---|---|
num_packages |
0.2922 |
total_weight_kg |
0.0802 |
num_stops |
0.0793 |
route_difficulty_score |
0.0059 |
estimated_time_minutes |
0.4706 |
experience_days |
0.0601 |
recent_avg_workload |
0.0029 |
recent_hard_days |
0.0087 |
Usage
from skops import io as sio
from huggingface_hub import hf_hub_download
# Download and load
model_path = hf_hub_download(repo_id="muthuk1/fairrelay-driver-effort", filename="model.skops")
untrusted = sio.get_untrusted_types(file=model_path)
model = sio.load(model_path, trusted=untrusted)
# Predict
import numpy as np
features = np.array([[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]])
prediction = model.predict(features)
Training Data
- Cainiao-AI/LaDe-D: Real last-mile delivery data from Shanghai (KDD 2023)
- Africa Synth Last-Mile: Synthetic Nigerian delivery data
- FairRelay Synthetic: Physics-informed synthetic data calibrated to FairRelay's deterministic formulas
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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