为实现急弯路段的追尾碰撞风险主动防控，提出了一种基于多源数据融合的追尾冲突动态预测方法。首先，基于无人机、毫米波雷达等采集的车辆运行数据，提出了适用于急弯路段交通流特征的追尾冲突判别模型及冲突等级阈值划分标准，分析了急弯路段的追尾冲突空间分布特征。然后，筛选车型、大车比率、断面速度差等13个交通流特征指标作为输入变量，以粒子群算法为基础，分别构建了其与BP神经网络、随机森林、支持向量机算法的追尾冲突动态组合预测模型，并根据混淆矩阵和曲线下面积评估各模型的预测性能，利用黑箱解释方法分析冲突发生概率的显著性影响因素及影响程度。结果表明：相较于平直或一般弯道路段，急弯路段的追尾冲突TTC （Time to Collision）值更小，出弯缓和曲线段冲突更为严重，且弯道内侧碰撞风险最高；粒子群-随机森林模型的追尾冲突预测性能最佳，灵敏度达90.70%；急弯路段追尾冲突受车辆平均车头间距的影响程度最大，当平均车头间距为25 m左右时，冲突发生概率最小，向心加速度均值、速度均值等因素亦对其有显著影响。

In order to realize the active prevention and control of rear-end collision on sharp curves, a dynamic prediction method of rear-end conflicts was proposed based on converged multi-source data. In this study, firstly, a model for distinguishing rear-end conflicts on sharp curves and a classification criteria of conflict level threshold were proposed by using the vehicle traveling data collected by drones and millimeter wave radar. The spatial distribution characteristics of such conflicts on sharp curves was subsequently analyzed. Then, 13 variables related to traffic flow characteristic such as vehicle type, ratio of large vehicle, and speed difference between sections were selected as input variables, and dynamic combined prediction models of rear-end conflicts on sharp curves with BP neural network, random forest and support vector machine algorithms were constructed based on particle swarm algorithm, respectively. The prediction performance of each prediction model was evaluated based on confusion matrix and area under curve, and the black box interpretation method was used to analyze the significant influence variables and the probabilities of rear-end conflicts occurrence. The results show that the TTC values of rear-end conflicts on sharp curves are smaller than those on flat or normal curved sections, and such conflicts are more serious on the gentle curve exit sections, and the conflicts risk on the inner side of the curves is the highest; the particle swarm algorithm- random forest model has the best performance in predicting the rear-end conflicts, with a sensitivity of 90.70%; the impact of average vehicle headway on rear-end conflict in sharp curve sections is the most significant. When the average vehicle headway is around 25 meters, the probability of conflict is the lowest. Factors such as mean centripetal acceleration and mean velocity also have a significant influence on it.