Vinicius Pozzobon Borin

Vinicius Pozzobon Borin, Jean Michel de Souza Sant'Ana, Usama Raheel et al.

Train delays result from complex interactions between operational, technical, and environmental factors. While weather impacts railway reliability, particularly in Nordic regions, existing datasets rarely integrate meteorological information with operational train data. This study presents the first publicly available dataset combining Finnish railway operations with synchronized meteorological observations from 2018-2024. The dataset integrates operational metrics from Finland Digitraffic Railway Traffic Service with weather measurements from 209 environmental monitoring stations, using spatial-temporal alignment via Haversine distance. It encompasses 28 engineered features across operational variables and meteorological measurements, covering approximately 38.5 million observations from Finland's 5,915-kilometer rail network. Preprocessing includes strategic missing data handling through spatial fallback algorithms, cyclical encoding of temporal features, and robust scaling of weather data to address sensor outliers. Analysis reveals distinct seasonal patterns, with winter months exhibiting delay rates exceeding 25\% and geographic clustering of high-delay corridors in central and northern Finland. Furthermore, the work demonstrates applications of the data set in analysing the reliability of railway traffic in Finland. A baseline experiment using XGBoost regression achieved a Mean Absolute Error of 2.73 minutes for predicting station-specific delays, demonstrating the dataset's utility for machine learning applications. The dataset enables diverse applications, including train delay prediction, weather impact assessment, and infrastructure vulnerability mapping, providing researchers with a flexible resource for machine learning applications in railway operations research.

Victor Maus, Vinicius Pozzobon Borin

Exact hierarchical agglomerative clustering (HAC) of large spatial datasets is limited in practice by the $\mathcal{O}(n^2)$ time and memory required for the full pairwise distance matrix. We present GSHAC (Geographically Sparse Hierarchical Agglomerative Clustering), a system that makes exact HAC feasible at scales of millions of geographic features on a commodity workstation. GSHAC replaces the distance matrix with a sparse geographic distance graph containing only pairs within a user-specified geodesic bound~$h_{\max}$, constructed in $\mathcal{O}(n \cdot k)$ time via spatial indexing, where~$k$ is the mean number of neighbors within~$h_{\max}$. Connected components of this graph define independent subproblems, and we prove that the resulting assignments are exact for all standard linkage methods at any cut height $h \le h_{\max}$. For single linkage, an MST-based path keeps memory at $\mathcal{O}(n_k + m_k)$ per component. Applied to a global mining inventory ($n = 261{,}073$), the system completes in 12\,s (109\,MiB peak HAC memory) versus $\approx 545$\,GiB for the dense baseline. On a 2-million-point GeoNames sample, all tested thresholds completed in under 3\,minutes with peak memory under 3\,GiB. We provide a scikit-learn-compatible implementation for direct integration into GIS workflows.