A Bayesian Network Approach to Causation Analysis of Road Accidents Using Netica
Based on an overall consideration of factors affecting road safety evaluations, the Bayesian network theory based on probability risk analysis was applied to the causation analysis of road accidents. By taking Adelaide Central Business District (CBD) in South Australia as a case, the Bayesian networ...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2017/2525481 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832561022802591744 |
|---|---|
| author | Xin Zou Wen Long Yue |
| author_facet | Xin Zou Wen Long Yue |
| author_sort | Xin Zou |
| collection | DOAJ |
| description | Based on an overall consideration of factors affecting road safety evaluations, the Bayesian network theory based on probability risk analysis was applied to the causation analysis of road accidents. By taking Adelaide Central Business District (CBD) in South Australia as a case, the Bayesian network structure was established by integrating K2 algorithm with experts’ knowledge, and Expectation-Maximization algorithm that could process missing data was adopted to conduct the parameter learning in Netica, thereby establishing the Bayesian network model for the causation analysis of road accidents. Then Netica was used to carry out posterior probability reasoning, the most probable explanation, and inferential analysis. The results showed that the Bayesian network model could effectively explore the complex logical relation in road accidents and express the uncertain relation among related variables. The model not only can quantitatively predict the probability of an accident in certain road traffic condition but also can find the key reasons and the most unfavorable state combination which leads to the occurrence of an accident. The results of the study can provide theoretical support for urban road management authorities to thoroughly analyse the induction factors of road accidents and then establish basis in improving the safety performance of the urban road traffic system. |
| format | Article |
| id | doaj-art-7b44a7f5af174b45bc1230849561c7b8 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-7b44a7f5af174b45bc1230849561c7b82025-02-03T01:26:12ZengWileyJournal of Advanced Transportation0197-67292042-31952017-01-01201710.1155/2017/25254812525481A Bayesian Network Approach to Causation Analysis of Road Accidents Using NeticaXin Zou0Wen Long Yue1School of Natural and Built Environments, University of South Australia, Adelaide, SA 5095, AustraliaSchool of Natural and Built Environments, University of South Australia, Adelaide, SA 5095, AustraliaBased on an overall consideration of factors affecting road safety evaluations, the Bayesian network theory based on probability risk analysis was applied to the causation analysis of road accidents. By taking Adelaide Central Business District (CBD) in South Australia as a case, the Bayesian network structure was established by integrating K2 algorithm with experts’ knowledge, and Expectation-Maximization algorithm that could process missing data was adopted to conduct the parameter learning in Netica, thereby establishing the Bayesian network model for the causation analysis of road accidents. Then Netica was used to carry out posterior probability reasoning, the most probable explanation, and inferential analysis. The results showed that the Bayesian network model could effectively explore the complex logical relation in road accidents and express the uncertain relation among related variables. The model not only can quantitatively predict the probability of an accident in certain road traffic condition but also can find the key reasons and the most unfavorable state combination which leads to the occurrence of an accident. The results of the study can provide theoretical support for urban road management authorities to thoroughly analyse the induction factors of road accidents and then establish basis in improving the safety performance of the urban road traffic system.http://dx.doi.org/10.1155/2017/2525481 |
| spellingShingle | Xin Zou Wen Long Yue A Bayesian Network Approach to Causation Analysis of Road Accidents Using Netica Journal of Advanced Transportation |
| title | A Bayesian Network Approach to Causation Analysis of Road Accidents Using Netica |
| title_full | A Bayesian Network Approach to Causation Analysis of Road Accidents Using Netica |
| title_fullStr | A Bayesian Network Approach to Causation Analysis of Road Accidents Using Netica |
| title_full_unstemmed | A Bayesian Network Approach to Causation Analysis of Road Accidents Using Netica |
| title_short | A Bayesian Network Approach to Causation Analysis of Road Accidents Using Netica |
| title_sort | bayesian network approach to causation analysis of road accidents using netica |
| url | http://dx.doi.org/10.1155/2017/2525481 |
| work_keys_str_mv | AT xinzou abayesiannetworkapproachtocausationanalysisofroadaccidentsusingnetica AT wenlongyue abayesiannetworkapproachtocausationanalysisofroadaccidentsusingnetica AT xinzou bayesiannetworkapproachtocausationanalysisofroadaccidentsusingnetica AT wenlongyue bayesiannetworkapproachtocausationanalysisofroadaccidentsusingnetica |