使用卷积神经网络进行多类实时碰撞风险预测：伊斯坦布尔案例研究
Multi-class real-time crash risk forecasting using convolutional neural network: Istanbul case study
AI总结：
本文提出一种基于卷积神经网络（CNN）的多类实时碰撞风险预测模型，利用交通和气象数据，在事件前三个四分钟间隔内进行预测，其性能优于三种典型机器学习模型。
摘要：
本文展示了人工神经网络（ANN）在预测碰撞风险方面的性能。首先，获取一些交通和天气数据作为原始数据。然后对这些数据进行分析，并基于额外树和皮尔逊相关性选择相关特征作为输入数据。此外，将碰撞和非碰撞时间数据分开；然后，使用该时间段所有可用值的平均值，将碰撞和非碰撞事件的特征值写入事件发生前的三个四分钟间隔中。在基于事故标签计算每个时期的碰撞可能性后，非碰撞样本的数量被减少。所提出的CNN模型能够从记录、处理和分类的输入特征（如交通特征和气象条件）中学习。这项工作的目标是基于事件发生前的三个时期来预测实时碰撞的几率。展示了接收器操作特性曲线（ROC曲线）的曲线下面积（AUC），以及作为真阳性率的敏感性和作为假阳性率的特异性，并与三种典型的机器学习和神经网络模型进行比较。最后，在误差值、AUC、敏感性和特异性参数作为性能变量方面，执行的模型优于其他模型。这项研究的结果建议将CNN模型作为实时碰撞风险预测的多类预测模型。我们的重点在于多类预测，而先前的研究将其用于二元（两类）分类，如碰撞和非碰撞。
英文摘要：
The performance of an artificial neural network (ANN) in forecasting crash risk is shown in this paper. To begin, some traffic and weather data are acquired as raw data. This data is then analyzed, and relevant characteristics are chosen to utilize as input data based on additional tree and Pearson correlation. Furthermore, crash and non-crash time data are separated; then, feature values for crash and non-crash events are written in three four-minute intervals prior to the crash and non-crash events using the average of all available values for that period. The number of non-crash samples was lowered after calculating crash likelihood for each period based on accident labeling. The proposed CNN model is capable of learning from recorded, processed, and categorized input characteristics such as traffic characteristics and meteorological conditions. The goal of this work is to forecast the chance of a real-time crash based on three periods before events. The area under the curve (AUC) for the receiver operating characteristic curve (ROC curve), as well as sensitivity as the true positive rate and specificity as the false positive rate, are shown and compared with three typical machine learning and neural network models. Finally, when it comes to the error value, AUC, sensitivity, and specificity parameters as performance variables, the executed model outperforms other models. The findings of this research suggest applying the CNN model as a multi-class prediction model for real-time crash risk prediction. Our emphasis is on multi-class prediction, while prior research used this for binary (two-class) categorization like crash and non-crash.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
