Topic Modeling and Clustering for Analysis of Road Traffic Accidents

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/250497
Download file(s):
File Description SizeFormat 
250497.pdfFulltext1.77 MBAdobe PDFView/Open
Type: Examensarbete för masterexamen
Master Thesis
Title: Topic Modeling and Clustering for Analysis of Road Traffic Accidents
Authors: Mekonnen, Agazi
Abdullayev, Shamsi
Abstract: In this thesis, we examined different approaches on how to cluster, summarise and search accident descriptions in Swedish Traffic Accident Data Acquisition (STRADA) dataset. One of the central questions in this project was that how to retrieve similar documents if a query does not have any common words with relevant documents. Another question is how to increase similarity between documents which describe the same or similar scenarios in different words. We designed a new pre-processing technique using keyword extraction and word embeddings to address these issues. Theoretical and empirical results show the pre-processing technique employed improved the results of the examined topic modeling, clustering and document ranking methods.
Keywords: Transport;Datavetenskap (datalogi);Transport;Computer Science
Issue Date: 2017
Publisher: Chalmers tekniska högskola / Institutionen för tillämpad mekanik
Chalmers University of Technology / Department of Applied Mechanics
Series/Report no.: Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden : 2017:65
URI: https://hdl.handle.net/20.500.12380/250497
Collection:Examensarbeten för masterexamen // Master Theses



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.