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|Title:||Mining traffic accident data for hazard causality analysis||Authors:||Tasios, Dimitrios
|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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