基于梯度关联规则的老年行人交通事故风险识别

In order to provide a recognition system for the safety features associated with older pedestrian traffic crashes severity levels. This paper applies Extreme Gradient Boost-Apriori (XGB-Apriori) to recognize the features of older pedestrian crashes in the road network. First, the methodology optimizes the weight of those crashes associated features by implementing the Select From Models(SFM) function from the XGBoost algorithm in scikit-learn. This process trains an XGBoost model to select important features by a pre-set threshold, then calculates a relative feature score (F-Score) for those selected features and set up a directional constrain to achieve an applicable data mining program for the causality analysis of traffic crashes which proposes a multi-dimensional interaction Apriori algorithm. This algorithm in this study recognizes the associated features, selects the highly frequent features, outputs association roles with relatively high confidence and lift. Moreover, this study evaluates the proposed SFM function and XGB-Apriori algorithm, the accuracy of the SFM function is 78.31% and the XGB-Apriori can increase 91% accuracy of the traditional algorithm, which indicates that the proposed algorithm and system can accurately predict the correlations among the causes and features leading to the severity of traffic accidents of older pedestrians. This study provides insights on the influence of the demographic features of driver and pedestrian, the features of vehicle and road structure on the severity of older pedestrian crashes; among them (1) more fatal crashes happen with male drivers compared with female drivers; (2) SUV, pickup truck and utility vehicles involved in more fatal pedestrian crashes than passenger cars; (3) and when the older pedestrian crash happens on the grade curve road, it is more likely to be a fatal crash. This paper proposes an accurate prognostic method for the comprehensive identification of the coupling factors of older pedestrian crashes and the implementation of targeted risk prevention and control, providing the necessary theoretical support for the effective protection of vulnerable groups on the road.