AI-Driven Incident Prediction for Road Freight Logistics

Use Case:

Development of a self-learning AI service for early detection and prevention of transport-related incidents in logistics operations. The system enriches route planning data with real-time external inputs (weather traffic construction data) and leverages decision tree learning to predict risk factors improving decision-making for freight tours and route adjustments.

Outcome:

• Successful integration of external weather (DWD), traffic (MDM), and construction data into operational logistics flows;
• Comparative evaluation of multiple ML models (GLM, DL, Random Forest, SVM, Gradient Boosted Trees) with Decision Trees yielding the most effective and interpretable performance;
• Deployment of a causal-model-based forecasting tool that highlights factors influencing incident risk (e.g., road type, sender/receiver quality, traffic alerts);
• Decision-support simulator enables real-time evaluation of planned or active tours with adjustable parameters; Scalable system designed for both integrated and standalone commercial offerings.

Ecosystem Support:

StairwAI support enabled access to AI expertise for model design, evaluation and integration. The collaboration involved iterative co-design cycles with AI experts to tailor learning approaches and incorporate simulation interfaces into the existing LOGIFY platform.

AI Relevance:

This success story showcases the value of AI democratization in logistics through:

1. Use of supervised learning methods accessible to non-experts (e.g., decision trees);
2. Integration with existing systems (via APIs);
3. Minimal data labeling via use of enriched external datasets;
4. Increased efficiency, safety, and predictability in freight operations through AI augmentation.

Summary:

Digital System Integration GmbH developed an AI-powered incident prediction and prevention service for the logistics sector as part of the StairwAI project. The solution enhances the company’s existing LOGIFY system by integrating external weather, traffic, and construction data to identify potential risks in freight transport operations. After evaluating multiple machine learning models, decision trees were chosen for their effectiveness and interpretability. A forecasting tool was developed to apply causal models to real or planned tours, enabling risk evaluation based on key factors such as road type, distance, quality of sender/receiver, and active incident alerts. The service can operate both as an add-on to the LOGIFY platform or as a standalone product, offering real-time decision support to freight operators. It represents a step forward in making AI accessible and valuable for SMEs in logistics through low-barrier integration, interpretable models, and operational impact.