Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/18392
Title: Mining traffic accident data for hazard causality analysis
Authors: Tasios, Dimitrios 
Tjortjis, Christos 
Gregoriades, Andreas 
Keywords: Classification;Artificial Intelligence and Applications;Data mining;Traffic accidents
Category: Electrical Engineering - Electronic Engineering - Information Engineering
Field: Engineering and Technology
Issue Date: 1-Sep-2019
Source: 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) 
Abstract: 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://ktisis.cut.ac.cy/handle/10488/18392
ISBN: 9781728147574
DOI: 10.1109/SEEDA-CECNSM.2019.8908346
Collaboration : Cyprus University of Technology
International Hellenic University
Rights: © IEEE
Type: Conference Papers
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers - poster -presentation

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