{'fraction_non_alphanumeric': 0.012290502793296089, 'fraction_numerical': 0.0011173184357541898, 'mean_word_length': 5.492753623188406, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Bayesian Network-Based Road Traffic Accident Causality Analysis

Traffic accident causality analysis is an important aspect in the traffic safety research field. Based on data survey and statistical analysis, a Bayesian network for traffic accident causality analysis was developed. The structure and parameter of the Bayesian network was learnt with K2 algorithm and Bayesian parameter estimation respectively. With the Junction Tree algorithm, the effect of road cross-section on the accident casualties was inferred. The results show that the Bayesian network can express the complicated relationship between the traffic accident and the causes, as well the correlations among the factors of causes. The results of analysis provide the valuable information on how to reveal the traffic accident causality mechanisms and how to take effective measures to improve the traffic safety situations.