GPS TRAJECTORY CLUSTERING FOR SPATIO – TEMPORAL BEHAVIOR ANALYSIS: THE APPLICATION OF HEATMAP TECHNIQUES AND SPATIO- TEMPORAL DUAL GRAPH NEURAL NETWORK

The introduction of GPS technology has led to the creation of vast amounts of spatio-temporal data, which captures the movement patterns of different things. Efficient allocation of resources to ensure user satisfaction is a crucial factor in shaping the future of urban planning and development. It is required to comprehend the factors that can contribute to the creation of methods for studying user behaviours using a substantial number of persons within a brief timeframe. It is essential to employ appropriate clustering approaches to analyze this data in order to comprehend spatio-temporal behaviours.Heatmaps offer a graphical display of changes in density across both location and Time, making them a user-friendly tool for initial data analysis and identifying areas of high activity. The Spatio-Temporal Dynamic Graph Neural Network (ST-DGNN) utilizes graph neural networks to represent the intricate connections present in spatio-temporal data, encompassing both spatial interdependencies and temporal changes. Our methodology improves the accuracy and interpretability of trajectory clustering by integrating different methods. The suggested method has been shown to identify relevant clusters effectively and reveal noteworthy spatio-temporal characteristics through experimental analysis on real-world GPS datasets. The research utilizes a dataset comprising 182 users for analysis. Numerous measures are taken to boost the clustering accuracy of the applied techniques, including addressing missing values and outliers. Additionally, this thesis introduces a framework for time estimation based on graph-based deep learning, termed Spatio-Temporal Dual Graph Neural II Networks (STDGNN). The method entails constructing node-level and edge-level graphs that depict the adjacency connections between intersections and road segments. The results showed a number of cluster changes in each period of time dependent on move users and period; for example, the (2592) cluster of period one hour.