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Title: An intelligent transportation system for accident risk index quantification
Authors: Michail, Harris 
Mouskos, Kyriacos C. 
Gregoriades, Andreas 
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
Bayesian networks
Information management
Information systems
Intelligent systems
Motor transportation
Traffic congestion
Street traffic control
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.
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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