An intelligent system to enhance traffic safety analysis
Traffic phenomena are characterized by complexity and uncertainty, hence require sophisticated information management to identify patterns relevant to safety. Traffic information systems have emerged with the aim to ease traffic congestion and improve road safety. However, assessment of traffic safe...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Conference Paper |
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IARIA
2011
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/49220 |
| _version_ | 1848758190819246080 |
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| author | Gregoriades, A. Mouskos, K. Ruiz-Juri, N. Parker, N. Hadjilambrou, I. Krishna, Aneesh |
| author2 | Eugen Borcoci |
| author_facet | Eugen Borcoci Gregoriades, A. Mouskos, K. Ruiz-Juri, N. Parker, N. Hadjilambrou, I. Krishna, Aneesh |
| author_sort | Gregoriades, A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Traffic phenomena are characterized by complexity and uncertainty, hence require sophisticated information management to identify patterns relevant to safety. 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 basedDynamic Traffic Assignment (DTA) and Bayesian Belief Networks (BBN). The former generates traffic information that is utilised by a Bayesian engine to quantify accident risk. Dynamic compilation of accident risks is used to gives rise to overall traffic safety. Preliminary results from this research have been validated. |
| first_indexed | 2025-11-14T09:40:03Z |
| format | Conference Paper |
| id | curtin-20.500.11937-49220 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:40:03Z |
| publishDate | 2011 |
| publisher | IARIA |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-492202023-02-02T07:57:35Z An intelligent system to enhance traffic safety analysis Gregoriades, A. Mouskos, K. Ruiz-Juri, N. Parker, N. Hadjilambrou, I. Krishna, Aneesh Eugen Borcoci Petre Dini Traffic Safety Bayesian Belief Networks Dynamic Traffic Assignment Traffic phenomena are characterized by complexity and uncertainty, hence require sophisticated information management to identify patterns relevant to safety. 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 basedDynamic Traffic Assignment (DTA) and Bayesian Belief Networks (BBN). The former generates traffic information that is utilised by a Bayesian engine to quantify accident risk. Dynamic compilation of accident risks is used to gives rise to overall traffic safety. Preliminary results from this research have been validated. 2011 Conference Paper http://hdl.handle.net/20.500.11937/49220 IARIA restricted |
| spellingShingle | Traffic Safety Bayesian Belief Networks Dynamic Traffic Assignment Gregoriades, A. Mouskos, K. Ruiz-Juri, N. Parker, N. Hadjilambrou, I. Krishna, Aneesh An intelligent system to enhance traffic safety analysis |
| title | An intelligent system to enhance traffic safety analysis |
| title_full | An intelligent system to enhance traffic safety analysis |
| title_fullStr | An intelligent system to enhance traffic safety analysis |
| title_full_unstemmed | An intelligent system to enhance traffic safety analysis |
| title_short | An intelligent system to enhance traffic safety analysis |
| title_sort | intelligent system to enhance traffic safety analysis |
| topic | Traffic Safety Bayesian Belief Networks Dynamic Traffic Assignment |
| url | http://hdl.handle.net/20.500.11937/49220 |