Abstract
The short-time crash risk measurement on the freeway has been catching much attention of government and management authorities. Due to recent advanced development in information systems and traffic sensor technologies, the real-time crash prediction models are getting more practicality. Critically, crash frequency analysis is the most important step for traffic safety studies. The paper makes two major contributions. Firstly, a multi-level Bayesian framework has been researched to identify risk factors towards the urban expressway by modeling unprocessed traffic data and roadway geometric topology data. Secondly, the paper utilized Bayesian belief net to build the real-time crash prediction model for the basic freeway segments. The objective is to predict the formation probability of a hazardous traffic condition in 4-9 minutes in a 250-meter-long freeway road section. Results obtained can be used for the urban freeway management departments to understand the risk factors and take immediate actions in advance to avoid traffic accidents on the freeway.
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