Road traffic accident databases provide the basis for road traffic accident analysis, the data inside which usually has a radial, multidimensional, and multilayered structure. Traditional data mining algorithms such as association rules, when applied alone, often yield uncertain and unreliable results. An improved association rule algorithm based on Particle Swarm Optimization (PSO) put forward by this paper can be used to analyze the correlation between accident attributes and causes. The new algorithm focuses on characteristics of the hyperstereo structure of road traffic accident data, and the association rules of accident causes can be calculated more accurately and in higher rates. A new concept of Association Entropy is also defined to help compare the importance between different accident attributes. T-test model and Delphi method were deployed to test and verify the accuracy of the improved algorithm, the result of which was a ten times faster speed for random traffic accident data sampling analyses on average. In the paper, the algorithms were tested on a sample database of more than twenty thousand items, each with 56 accident attributes. And the final result proves that the improved algorithm was accurate and stable.

Pedestrians are some of the worst victims, as one of the weaker groups in road traffic accidents, but, at the same time, their unsafe behaviors are also an important factor in traffic accidents. This paper builds a pedestrian crossing hazard automatic-balance model and waiting-time threshold model by analyzing the process by which pedestrians cross the street. Then, the reasons for pedestrians’ unsafe behavior when crossing the street are analyzed by using traffic psychology. Finally, this paper puts forward some measures, based on aspects of pedestrian psychology, to reduce or alleviate pedestrians’ unsafe behaviors.

With the study of traffic crashes on curved road segments as the focus of research, a logistic regression based curve road crash severity prediction model was established based on a sample crash database of 20000 entries collected from 4 regions of China and 15 evaluation indicators involving driver, driving environment, and traffic environment factors. Maximum Likelihood Estimation and step-back technique were deployed for data analysis, the conclusion of which is that the three main contributory factors on curve road crash severity are weather, roadside protection facility, and pavement structure. Hosmer and Lemeshow tests were used to verify the reliability of the model, and the model variables were discussed to a certain degree as well.

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Since the disaster point of road traffic emergency and the emergency demand were uncertain, the demand weighting model and the hierarchical location model are suitable for the characteristics of road traffic emergency. According to the requirements for coverage area of the macroscopic-location of the large area of disaster relief material repository, the demand weighting model and the hierarchical location model were established in this article. Among them, the demand weight model was solved by modeling, and the demand weight of each disaster point was obtained; the location model was combined with immune algorithm and ant colony algorithm to get the hierarchical location scheme. Finally, Jing-jin-ji that represented China's ''capital circle'' was taken as an example, the model was solved using MATLAB, the mathematical software, and provided scientific and reasonable decision-making support for location selection. Moreover, it also provided a basis for the classification of the road traffic disaster relief material repository.