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Publicação
Identifying No-Pickup Patterns in Transport Services through Geospatial Models: Optimizing Transport Logistics: Predicting and Preventing Non-Pickups
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Autores
Orientador(es)
Resumo(s)
Non‑pickups (nPU) in business‑to‑business vehicle‑transfer services remain a critical source of operational cost and reputational risk. This thesis augments earlier spatial analyses with advanced feature engineering and ensemble learning to improve the early detection of nPUs. After auditing column types and harmonising five years of booking records (2019–2024), we engineered lead‑time windows and multi‑scale seasonality indices. The enriched dataset fed a hybrid modelling pipeline that combines Multi‑scale Geographically Weighted Regression (MGWR) for interpretability with XGBoost for predictive accuracy. Preliminary analyses indicate an uplift in predictive performance, while MGWR maps still expose supplier‑capacity hotspots in Southern Europe. The study demonstrates that spatial awareness and tailored temporal features jointly advance proactive mitigation strategies for logistics providers.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
Palavras-chave
Non-Pickups (nPU) MGWR B2B Vehicle Transfers Predictive Modelling Spatial Analysis Supplier Performance Optimization Operational Risk Mitigation