This master’s thesis investigates the application of anomaly detection techniques to analyze crowdmovements using cell location data, a topic of growing interest in public safety and policymaking. Thisresearch uses machine learning algorithms, specifically Isolation Forest and DBSCAN, to identify unusualmovement patterns within a large, unlabeled dataset. The study addresses the challenges inherent inprocessing and analyzing vast amounts of spatial and temporal data through a comprehensive method-ology that includes data preprocessing, feature engineering, and optimizing algorithm parameters. Thefindings highlight the feasibility of employing anomaly detection in real-world scenarios, demonstratingthe algorithms’ ability to detect anomalies and offering insights into crowd dynamics.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ltu-105231 |
| Date | January 2024 |
| Creators | Longberg, Victor |
| Publisher | Luleå tekniska universitet, Institutionen för system- och rymdteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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