基于ADASYN-XGBoost的交通事故自动检测方法

基于数据驱动的交通事故自动检测对道路事故的及时救援与降低事故影响具有重要作用。为解决道路交通事故自动检测中的样本不均衡问题，研究了混合自适应过采样技术与极限梯度提升树算法的交通事故自动检测方法（ADASYN-XGBoost）。其中，为从不均衡的交通事故样本中有效挖掘数据的时空特征与事故发生之间的内在关联规律，构建了初始特征变量组合，引入自适应合成过采样方法（adaptive synthetic oversampling method，ADASYN）来平衡事故类与非事故类的样本数量，以增强训练数据的质量；其次，为提高检测效果，构建了基于XGBoost的交通事故检测模型，利用该模型对增强后的数据样本进行特征筛选；最后，为获取最佳参数组合，采用了贝叶斯优化算法对XGBoost进行参数的快速标定。本文使用波特兰高速公路数据集对ADASYN-XGBoost方法进行模型验证与实证研究。结果表明：与先进的基准模型相比，ADASYN-XGBoost的各项检测指标均最优，其F1分数达到94.47%且误检率低至8.95%。在模型训练样本数为2800，500（18%的初始样本量），150（5%的初始样本量）时，ADASYN-XGBoost的F1分数分别为94.47%，88.89%，81.93%。在进一步的消融实验中，均衡正负样本后各基准模型的性能指标提高了2.68%~44.85%。本文提出的方法能够有效解决道路交通事故检测中的样本不均衡问题，同时也为道路交通安全预防与事故处理等提供了技术保障。

An Automatic Detection Method for Traffic Accidents Based on ADASYN-XGBoost

A data-driven approach for automatic detection of road traffic accidents plays an important role in timely rescue and reducing the impact of road accidents. In order to solve the sample imbalance problem in automatic detection of traffic accidents a hybrid adaptive oversampling technique and extreme gradient boosting tree algorithm (ADASYN-XGBoost) is studied. In particular, to effectively mine the intrinsic correlation law between spatio-temporal feature of the data and accident occurrence form the unbalanced traffic accident samples. The initial combinations of feature variable are set. And to improve the quality of the training data, the adaptive synthetic oversampling method (ADASYN) is introduced to balance the number of samples between the accident class and the non-accident class. To improving the detection effect, a traffic accident detection model based on extreme gradient boosting (XGBoost) is developed, which is utilized to filter the features of the enhanced data samples. Finally, to obtain the best combination of parameters, a Bayesian optimization algorithm is used to quickly calibrate the parameters of XGBoost. In this paper, the ADASYN-XGBoost method is validated and investigated using the Portland Freeway dataset. The results show that ADASYN-XGBoost optimizes all detection metrics compared to the state-of-the-art benchmark model. The F1 score reaches 94.47% and the false detection rate is as low as 8.95%. The F1 scores of ADASYN-XGBoost are 94.47%, 88.89%, and 81.93% when the number of model training samples are 2800, 500 (18% of the initial sample size), and 150 (5% of the initial sample size). In further ablation experiments, the performance indexes of each benchmark model after equalizing positive and negative samples are improved by 2.68% to 44.85%. The method proposed in this paper can effectively solve the sample imbalance problem in detection of road traffic accidents, which also provides technical support for road traffic safety prevention and accident management.