为提升寒区沿边公路的安全性和可靠性，提前规避部分事故风险，提出一种基于SSA-SVM的寒区沿边公路潜在事故黑点识别方法。首先，针对寒区沿边公路的特征设计了32种驾驶模拟试验对比场景，利用驾驶模拟器和眼动仪采集车辆运行指标数据和驾驶人驾驶行为指标数据，并进行了指标差异性分析，选取加速踏板开合度、制动信号、方向盘转角、横向加速度、驾驶人瞳孔直径5项指标综合反映寒区沿边公路的潜在事故风险；然后，构建基于SSA-SVM算法的寒区沿边公路潜在事故黑点识别模型，通过SSA算法高效的搜索能力和寻优时较高的准确性来优化SVM模型的参数；最后，利用驾驶模拟试验数据验证所提SSA-SVM模型的有效性，并与CPO-SVM、GWO-SVM模型进行对比分析。结果表明：在3种模型中，基于SSA-SVM的寒区沿边公路潜在事故黑点识别模型的识别准确率最高，其预测集准确率为93.12%，最优适应度值为0.001 41；该模型能有效识别出不同季节条件下寒区沿边公路潜在事故黑点，可为制定科学的寒区沿边公路事故预防措施提供理论依据。

A SSA-SVM based method for identifying potential accident black spots on cold-region border highways was proposed in order to enhance the safety and reliability of these highways and mitigate some accident risks in advance. Firstly, 32 driving simulation test comparison scenarios were designed according to the characteristics of cold-region border highways. Vehicle operation indicator data and drivers' driving behaviour indicator data were gathered using driving simulator and eye tracker, and the differences in indicators were analyzed. Five indicators, including accelerator pedal position, brake pedal, steering wheel angle, lateral acceleration, and driver's pupil diameter, were selected to comprehensively represent the potential accident risk of cold-region border highways. Then, a potential accident black spot identification model for cold-region border highways based on SSA-SVM algorithm was proposed, which utilized the efficient search ability and high accuracy of SSA algorithm to optimize the parameters of SVM model. Finally, the effectiveness of the proposed SSA-SVM model was verified using driving simulation test data, and it was compared with CPO-SVM and GWO-SVM models. The results showed that among these three models, the SSA-SVM based model for identifying potential accident black spots on cold-region border highways had the highest recognition accuracy, with a prediction set accuracy of 93.12% and an optimal fitness value of 0.001 41. This method is able to effectively identify potential accident black spots on highways in cold regions under different seasons, which can provide theoretical basis for formulating scientific accident prevention measures for cold-region border highways.