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|Title:||Traffic accidents analysis using self-organizing maps and association rules for improved tourist safety||Authors:||Gregoriades, Andreas
|Keywords:||Association rules;Self-organizing maps;Tourists safety||Category:||Computer and Information Sciences||Field:||Natural Sciences||Issue Date:||Apr-2017||Publisher:||SciTePress||Source:||19th International Conference on Enterprise Information Systems, 2017, 26-29 April||Abstract:||Traffic accidents is the most common cause of injury among tourists. This paper presents a method and a tool for analysing historical traffic accident records using data mining techniques for the development of an application that warns tourist drivers of possible accident risks. The knowledge necessary for the specification of the application is based on patterns distilled from spatiotemporal analysis of historical accidents records. Raw accident obtained from Police records, underwent pre-processing and subsequently was integrated with secondary traffic-flow data from a mesoscopic simulation. Two data mining techniques were applied on the resulting dataset, namely, clustering with self-organizing maps (SOM) and association rules. The former was used to identify accident black spots, while the latter was applied in the clusters that emerged from SOM to identify causes of accidents in each black spot. Identified patterns were utilized to develop a software application to alert travellers of imminent accident risks, using characteristics of drivers along with real-time feeds of drivers' geolocation and environmental conditions.||URI:||http://ktisis.cut.ac.cy/handle/10488/12660||Rights:||© 2017 SCITEPRESS||Type:||Conference Papers|
|Appears in Collections:||Δημοσιεύσεις σε συνέδρια /Conference papers - poster -presentation|
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