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Τίτλος: Mining traffic accident data for hazard causality analysis
Συγγραφείς: Tasios, Dimitrios 
Tjortjis, Christos 
Gregoriades, Andreas 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Λέξεις-κλειδιά: Classification;Artificial Intelligence and Applications;Data mining;Traffic accidents
Ημερομηνία Έκδοσης: 1-Σεπ-2019
Πηγή: 4th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference, SEEDA-CECNSM 2019
Conference: South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM) 
Περίληψη: Over 1.25 million people are killed, and 20-50 million people are seriously injured by traffic accidents every year globally, according to the World Bank. This paper aims to identify patterns in traffic accident data, collected by Cyprus Police between 2007 and 2014. The dataset that was used includes information regarding 3 groups of accident properties: human, vehicle and general environmental or infrastructural information. Data mining techniques were used, and several patterns were identified. Five classifiers were evaluated using a preprocessed dataset, to extract accident patterns. Preliminary results indicate some of the main issues with regards to accident causalities in Cyprus that could be used for real time accident warnings.
URI: https://hdl.handle.net/20.500.14279/18392
ISBN: 9781728147574
DOI: 10.1109/SEEDA-CECNSM.2019.8908346
Rights: © IEEE
Type: Conference Papers
Affiliation: Cyprus University of Technology 
International Hellenic University 
Publication Type: Peer Reviewed
Εμφανίζεται στις συλλογές:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation

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