Abstract
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.
Highlights
With the expansion of urban development and the surging of vehicle ownership, urban travel becomes vulnerable to three “chronic diseases,” which are congestion, accident, and pollution
According to Global Plan for the Decade of Action for Road Safety 2011–2020 developed by the UN Road Safety Collaboration in 2011, nearly 1.3 million people die as a result of road traffic collisions per annum, which means more than 3,000 fatalities per day
In Bayesian network, the directed acyclic graph is a visual expression form that is closer to the characteristics of thought and reasoning mode of human
Summary
With the expansion of urban development and the surging of vehicle ownership, urban travel becomes vulnerable to three “chronic diseases,” which are congestion, accident, and pollution. Bayesian network is one of the effective methods in the field of artificial intelligence to express uncertainty analysis and probability reasoning of a system It can exploit the dependence relationships based on local conditions in a model to conduct bidirectional uncertainty investigation for prediction, classification, and diagnostic analyses. There are some software platforms available for the construction of a Bayesian network, such as Bayes Net Toolbox (BNT), BayesBuilder, and JavaBayes, of which the MATLABbased BNT developed by Murphy [13] is extensively used.
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