In China, construction accidents have killed more people than any other industry since 2012. The factors which led to the accident have complex interaction. Real data about accidents is the key to reveal the mechanism among these factors. But the data from the questionnaire and interview has inherent defects. Many behaviors that impact safety are illegal. In China, most of the cases are from accident investigation reports. Finding out the cause of the accident and liability affirmation are the core of incident investigation reports. So the truth of some answers from the respondents is doubtful. With a series of NLP technologies, in this paper, causal factors of construction accidents are extracted and organized from Chinese incident case texts. Finally, three kinds of neglected causal factors are discovered after data analysis.

Accident detection is a vital part of traffic safety . Many road users suffer from traffic accidents, as well as their consequences such as delay, congestion, air pollu tion, and so on . In this study, we utilize two advanced deep learning techniques, Long Short - Term Memory (LSTM) and Gated Recurrent Units (GRU s), to detect traffic accidents in Chicago . These two techniques are selected because they are known to perform we ll with sequential data (i.e., time series). The full dataset consists of 241 accident and 6, 038 non - accident cases selected from Chicago expressway, and it includes traffic spatiotemporal data, weather condition data, and congestion status data . Moreover, b ecause the dataset is imbala nced (i.e., the dataset contains many more non - accident cases t han accident cases), Synthetic Minority Over - sampling Technique (SMOTE) is employed . Overall, the two models perform significantly well, both with an Area Under Curve (AUC) of 0.85. Nonetheless, the GRU model is observed to perform slightly better than LSTM model with respect to detection rate . The performance of both models is similar in terms of false alarm rate.