This work aims to explore the use of machine learning techniques, particularly clustering and cluster evolution tracking, to analyze travel patterns in public transportation in a city and provide valuable insights for urban transit planning and optimization. Clustering involves identifying and grouping similar objects, such as passengers with different ticket types, and distinguishing them from dissimilar objects in other groups. Over time, groups can change, so tracking this change can provide more detailed and valuable insights than analyzing data in aggregates. Clustering and cluster evolution tracking can reveal groups of passengers that are more or less affected by changes such as seasonality or fare increases. We propose a framework called DUGET (Dynamic User Grouping and Evolution Tracking), which clusters anonymized users based on their ticket choices and temporal travel patterns using a multi-step approach. The clusters are then tracked over time using Jaccard similarity based on memberships, allowing for the analysis and visualization of changes. Our experiments using a real-world public transportation dataset collected in Stockholm, Sweden, show the feasibility of tracking change over time in public transportation by examining passenger behavior as a temporal aggregate. The framework we propose is generalizable and can be used for future projects to understand trends in groups of objects.

In wood science, accurate segmentation of non-cellular elements in microscopy images is critical for assessing wood quality and understanding growth patterns. Yet, it is challenging due to the complex morphology of wood components. This work explores the development of a semi-supervised deep learning model for segmenting non-cellular elements in wood microscopy images of Norway spruce, an essential source for construction materials in Europe, addressing the challenge of manual annotation’s labor intensity and expertise requirement. The segmentation model employs advanced deep learning architectures, including Convolutional Neural Networks and a Vision Transformer, to capture the intrinsic patterns embedded in wood structures. We proposed a Pixel-level Guided Mean-Teacher (PG-MT) framework as an improvement to the Mean-Teacher semi-supervised learning technique. Our framework enables pixel-level guided correction to enhance segmentation accuracy and model robustness with limited labeled datasets. Our experimental evaluations show that the proposed PG-MT framework improved the Dice score for medullary ray segmentation by 0.95% and the IoU score by 1.14% over the Uncertainty-Aware Mean-Teacher (UA-MT) framework. Additionally, the integration with laboratory instruments emphasizes the model’s effectiveness in accurately estimating cross-sectional cell wall thickness, demonstrating a strong correlation with X-ray measurements. This result validates the model’s practical applicability in laboratory settings, enhancing the analysis of wood properties. This work provides a robust semi-supervised DL framework for segmenting non-cellular elements in wood microscopy images, significantly reducing the annotation burden and paving the way for more automated and precise wood property analysis.

Neural networks are used for different machine learning tasks, such as spatial time-series forecasting. Accurate modelling of a large and complex system requires large datasets to train a deep neural network that causes a challenge of scale as training the network and serving the model are computationally and memory intensive. One example of a complex system that produces a large number of spatial time-series is a large road sensor infrastructure deployed for traffic monitoring. The goal of this work is twofold: 1) To model large amount of spatial time-series from road sensors; 2) To address the scalability problem in a real-life task of large-scale road traffic prediction which is an important part of an Intelligent Transportation System.We propose a partitioning technique to tackle the scalability problem that enables parallelism in both training and prediction: 1) We represent the sensor system as a directed weighted graph based on the road structure, which reflects dependencies between sensor readings, and weighted by sensor readings and inter-sensor distances; 2) We propose an algorithm to automatically partition the graph taking into account dependencies between spatial time-series from sensors; 3) We use the generated sensor graph partitions to train a prediction model per partition. Our experimental results on traffic density prediction using Long Short-Term Memory (LSTM) Neural Networks show that the partitioning-based models take 2x, if run sequentially, and 12x, if run in parallel, less training time, and 20x less prediction time compared to the unpartitioned model of the entire road infrastructure. The partitioning-based models take 100x less total sequential training time compared to single sensor models, i.e., one model per sensor. Furthermore, the partitioning-based models have 2x less prediction error (RMSE) compared to both the single sensor models and the entire road model.