You have 200 couriers — each making 15–25 deliveries per day. A third of clients call the call center: "Where is my order?" The standard ETA of "by 18:00" gives a spread of ±2–3 hours. When a driver is an hour late, the client gets nervous. Especially if the order is dinner groceries? The result — cancellations, lower NPS, losses. We build ML systems that predict arrival time with 15–30 minute accuracy, using LightGBM, LSTM, and real-time data. Our cumulative experience — 20+ projects for logistics operators. We guarantee stable model performance in production and post-deployment support.
We have already implemented such projects for 20+ logistics operators in Russia and the CIS, accumulating over 5 years of ML experience in logistics. According to Uber Movement, LightGBM outperforms linear regression by 2–3 times in accuracy on historical data. Reducing call center load by 30–40% saves up to 3 million rubles per year. Contact us — we will audit your data in 2 days.
Why traditional ETA methods don't work
Linear regression based on average speed and distance does not account for:
- Traffic jams: during peak hours, speed drops by 2–3 times.
- Weather: rain or snow adds 15–40% time.
- Operational delays: queue at loading, time at stops.
- Historical patterns: routes with regular delays on specific days.
Traditional methods yield MAPE of 25–40%. An ML model reduces MAPE to 10–15%, saving up to 20% in logistics costs. For an operator with a fleet of 200 vehicles, savings from reduced failed deliveries can reach 5–7 million rubles per year.
How AI improves ETA accuracy
Feature engineering is key. We collect features from several sources: Route data:
- Route distance (Google Maps / HERE / OpenStreetMap OSRM)
- Historical speeds on roads at different times of day
- Geofencing of pickup and delivery points
Operational data:
- Warehouse processing time (pick-pack-ship)
- Current queue at loading/unloading
- Number of stops en route to the target point
External factors:
- Weather: rain/snow/fog increase time by 15–40%
- Traffic events: accidents, roadworks, closures (TomTom TrafficStats, HERE Traffic API)
- Time patterns: morning peak 08–10, evening peak 17–19
Model architecture
Task: regression — predict time from dispatch to delivery in minutes. Feature matrix:
features = { # Route 'distance_km': route_distance, 'n_stops': stops_remaining, 'route_complexity': turns_per_km, # Time 'hour_of_day': departure_hour, 'day_of_week': departure_dow, 'is_holiday': holiday_flag, 'month': departure_month, # Traffic 'historical_avg_speed': avg_speed_for_route_time, 'current_traffic_index': live_traffic_score, # 1.0 = normal, 2.0 = jam 'weather_delay_factor': weather_impact_estimate, # Operational 'shipment_weight_kg': weight, 'vehicle_type': truck_van_bike, 'driver_experience_days': driver_tenure, # Historical for this route 'route_historical_eta': past_mean_eta_for_route, 'route_eta_std': past_std_eta_for_route } Models:
- LightGBM Regressor: primary model for tabular data.
- Quantile Regression (p10/p50/p90): for ETA with confidence intervals.
- LSTM: if a sequence of intermediate GPS points is available.
Model comparison for ETA
| Model | Accuracy (MAPE) | Training time | Real-time support | Data requirements |
|---|---|---|---|---|
| LightGBM | 10–15% | Fast (minutes) | Yes (inference <5ms) | Tabular features |
| LSTM | 8–12% (with sequences) | Slow (hours) | Yes (inference <10ms) | GPS tracks, sequential |
| Linear regression | 25–40% | Instant | Yes | Minimum |
Real-time ETA update
A static forecast at dispatch is not enough. The ETA must update dynamically: Update triggers:
- Courier GPS tracking every 30 seconds.
- Traffic jam detected on route (traffic API polling every 5 min).
- Delay at previous delivery point.
- Weather event.
Online learning vs. static model: In production: the static model is retrained daily on new data. Real-time corrections via a kinematic motion model (speed + distance → updated ETA) without restarting the ML model.
def update_eta_realtime(current_position, destination, remaining_stops, base_eta, traffic_api): remaining_distance = calculate_distance(current_position, destination, via=remaining_stops) current_speed = traffic_api.get_current_speed(current_position, destination) historical_speed = get_historical_speed(current_position, destination, datetime.now()) traffic_factor = historical_speed / current_speed remaining_time = (remaining_distance / historical_speed) * traffic_factor * 60 return remaining_time Comparison of approaches: Last Mile vs. Long Haul
| Parameter | Last Mile | Long Haul |
|---|---|---|
| Number of stops | 10–30+ | 1–3 |
| Uncertainty | Client not opening, parking | Weather, weight restrictions |
| Forecast horizon | 1–4 hours | 1–5 days |
| Update frequency | 15–30 min | 1 hour |
| Integration with TSP | Yes (route optimization) | No |
| Key metric | % on time within ±15 min | MAPE |
Customer notifications
ETA is useless without integration with a communication layer: Notification workflow:
- After dispatch: "Your order is on its way, expected time: 14:30–15:00".
- 60 minutes before: "Courier will arrive in ~55 minutes".
- 15 minutes before: "Courier is nearby, will arrive in ~12 minutes".
- If delay > 20% from ETA: automatic notification with new time and reason.
Channels: SMS (Twilio/SMS.ru), Push notifications, Email, WhatsApp Business API. System metrics:
- ETA Accuracy: % of deliveries within ±15 min of ETA.
- ETA MAPE: average forecast error in percent.
- Proactive notification rate: % of delays that the customer was informed about before occurrence.
- CSAT correlation: correlation of ETA accuracy with delivery rating.
What's included in ETA system development
- Data audit: assess quality and completeness of historical data, configure pipelines.
- Feature engineering: develop a feature set tailored to your specifics (cargo type, region, seasonality).
- Modeling: LightGBM / LSTM / Quantile Regression, validation via cross-validation.
- Real-time update: integration with GPS tracker and traffic API.
- Notifications: configure triggers and channels (SMS, Push, Email).
- Metrics dashboard: panel for monitoring accuracy and proactivity.
- Support: documentation, training your team, 3-month warranty.
Timelines: basic ETA model with static forecast — 3–4 weeks. Real-time update + customer notifications + metrics — 10–12 weeks.
Order ETA system development — we will assess your project in 2 days. Get a consultation from our AI engineer. Contact us to discuss details.