Case: Trip Success Prediction for Waste Removal

Case: ML trip-success prediction for a waste-removal operator — up to 95% accuracy, +5% successful trips. Predictive analytics for logistics — AI Solutions Hub.

Case: trip success prediction — waste-removal logistics

LogisticsML / Forecasting

Task. A regional waste-removal operator was losing trips: a truck arrived to an empty container — or an overflowing one waited for days. A forecast was needed: which points to serve first and with which vehicles.

Solution

We built an ML model predicting trip success from collection history, seasonality, point type and weather. The dispatcher gets a prioritized daily list, and the system builds routes automatically. Deployed on the client’s servers; training took 4 weeks.

Results

+5%more successful trips

up to 95%trip-success forecast accuracy

x2faster dispatcher decisions

Technologies: Python, CatBoost, PostgreSQL, FastAPI, Docker.

Project stages

Data audit (1–2 weeks) — flight history, logistics and waste-collection telemetry review.
Model development (3–6 weeks) — ML models predicting trip success and route efficiency.
Integration (2–4 weeks) — embedding predictions into dispatching workflows and dashboards.
Rollout — pilot on one fleet, then scaling across all operations.

Where this applies

The solution fits logistics operators in Almaty and Tashkent, municipal services in Bishkek and Dushanbe, and transport companies across Central Asia and Turkey. See also our data management & analytics service.

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Tell us about your task — we will propose an optimal AI solution for your business in Kyrgyzstan, Central Asia or Türkiye. We reply within one business day.