Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/14783
Title: Black spots identification through a Bayesian Networks quantification of accident risk index
Authors: Gregoriades, Andreas 
Mouskos, Kyriacos C. 
Keywords: Accident analysis;Bayesian Networks;Crash analysis;Dynamic Traffic Assignment;Road safety
Category: Electrical Engineering - Electronic Engineering - Information Engineering
Field: Engineering and Technology
Issue Date: 2013
Source: Transportation Research Part C: Emerging Technologies Volume 28, March 2013, Pages 28-43
Journal: Transportation Research Part C: Emerging Technologies 
Abstract: Traffic accidents constitute a major problem worldwide. One of the principal causes of traffic accidents is adverse driving behavior that is inherently influenced by traffic conditions and infrastructure among other parameters. Probabilistic models for the assessment of road accidents risk usually employs machine learning using historical data of accident records. The main drawback of these approaches is limited coverage of traffic data. This study illustrates a prototype approach that escapes from this problem, and highlights the need to enhance historical accident records with traffic information for improved road safety analysis. Traffic conditions estimation is achieved through Dynamic Traffic Assignment (DTA) simulation that utilizes temporal aspects of a transportation system. Accident risk quantification is achieved through a Bayesian Networks (BNs) model learned from the method's enriched accidents dataset. The study illustrates the integration of BN with the DTA-based simulator, Visual Interactive Systems for Transport Algorithms (VISTAs), for the assessment of accident risk index (ARI), used to identify accident black spots on road networks. .
URI: https://ktisis.cut.ac.cy/handle/10488/14783
ISSN: 2-s2.0-84873054062
https://api.elsevier.com/content/abstract/scopus_id/84873054062
2-s2.0-84873054062
0968090X
https://api.elsevier.com/content/abstract/scopus_id/84873054062
DOI: 10.1016/j.trc.2012.12.008
Rights: © 2012 Elsevier Ltd
Type: Article
Appears in Collections:Άρθρα/Articles

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