Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/18397
Title: An Integrated Dynamic Traffic Assignment–Bayesian Belief Networks Methodology to Assess Traffic Safety
Authors: Gregoriades, Andreas 
Mouskos, Kyriacos C. 
Ruiz-Junco, Natalia 
Parker, Neville 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Keywords: Road Safety;Dynamic Traffic Assignment;Bayesian Networks
Issue Date: 2011
Abstract: Traffic conditions significantly affect drivers‟ behavior that constitutes one of the principal causes of road accidents. Being able to accurately predict traffic conditions significantly improves the assessment of accident risk. Traditional approaches to traffic flow analysis use static representations of the road system and as such have limited accuracy. Dynamic Traffic Assignment (DTA) approaches better utilize temporal aspects of such system (where and when to travel on the road network) to produce better predictions. The work presented herein integrates two mature methodologies namely simulation-based DTA and Bayesian Networks (BN) to address this problem. The former is widely used in transport modeling to predict driver‟s travel behavior while the later constitute a powerful artificial intelligence technique that predicts effects given prior knowledge and input evidence in uncertain settings. The integration of BN with the DTA-based simulator, Visual Interactive Systems for Transport Algorithms (VISTA), provides the framework for improved safety evaluation of road networks and future planning.
URI: https://ktisis.cut.ac.cy/handle/10488/18397
Type: Conference Papers
Affiliation : European University Cyprus 
University of Texas 
The City College of New York 
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation

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