Currently, many studies on the severity of traffic conflicts only considered the possibility of potential collisions but ignored the consequences severity of potential collisions. Aiming toward this defect, this study establishes a potential collision (serious conflict) consequences severity model on the basis of vehicle collision theory. Regional vehicles trajectory data and historical traffic accident data were obtained. The field data were brought into the conflict consequences severity model to calculate the conflict severity rate of each section under different TTC thresholds. For comparison, the traditional conflict rate of each section under different TTC thresholds that considered only the number of conflicts was also calculated. Results showed that the relationship between conflict severity rate and influencing factors was somehow different. The conflict severity rate seemed to have a higher correlation with accident rate and accident severity rate than conflict rate did. The TTC threshold value also affected the correlation between conflicts and accidents, with high and low TTC threshold indicating a lower correlation. The results showed that conflict severity rate that considered each single conflict consequence severity was a little better than the traditional conflict rate that considered only the numbers of conflicts in reflecting real risks as a new conflict evaluation indicator. The severity of traffic conflicts should consider two dimensions: the possibility and consequence of potential collisions. Based on this, we propose a new traffic safety evaluation method that takes into account the severity of the consequences of the conflict. More data and prediction models are needed to conduct more realistic and complex research in the future to ensure reliability of this new method.

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In order to overcome the inaccuracy of current research results of traffic flow prediction, this paper proposes a prediction method for traffic flow with small time granularity at intersection based on probability network. This method takes one minute as time granularity, collects traffic data such as cross-section flow, section traffic flow velocity data, traffic density, road occupancy, section delay and steering ratio by using RFID technology, and analyzes and processes the data. By introducing Bayesian network in probabilistic network and combining K-nearest neighbor method, historical data and predicted traffic flow state are classified to realize the prediction of traffic flow with small time granularity at intersections. The experimental results show that this method has high prediction accuracy and reliability, and is a feasible traffic flow prediction method.

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