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|Title:||An intelligent transportation system for accident risk index quantification||Authors:||Michail, Harris
Mouskos, Kyriacos C.
|Keywords:||Accident frequency;Accident risks;Domain knowledge;Dynamic traffic assignments;Intelligent transportation systems;Road safety;Traffic flow;Road section;Traffic information systems;Traffic safety;Accident prevention;Accidents;Bayesian networks;Information management;Information systems;Intelligent systems;Motor transportation;Traffic congestion;Street traffic control||Category:||Electrical Engineering, Electronic Engineering, Information Engineering||Field:||Engineering and Technology||Issue Date:||2012||Source:||14th International Conference on Enterprise Information Systems, Wroclaw, Poland, 28 June-1 July, 2012||Abstract:||Traffic phenomena are characterized by complexity and uncertainty, hence require sophisticated information management to identify patterns relevant to safety and reliability. Traffic information systems have emerged with the aim to ease traffic congestion and improve road safety. However, assessment of traffic safety and congestion requires significant amount of data which in most cases is not available. This work illustrates an approach that aims to alleviate this problem through the integration of two mature technologies namely, simulation-based Dynamic Traffic Assignment (DTA) and Bayesian Networks (BN). The former generates traffic flow data, utilised by a BN model that quantifies accident risk. Traffic flow data is used to assess the accident risk index per road section and hence, escape from the limitation of traditional approaches that use only accident frequencies to quantify accident risk. The development of the BN model combines historical accident records obtained from the Cyprus police and domain knowledge from road safety.||URI:||http://ktisis.cut.ac.cy/handle/10488/3750||Type:||Conference Papers|
|Appears in Collections:||Δημοσιεύσεις σε συνέδρια/Conference papers|
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