论文标题
我们可以使用机器学习估算远程记录数据的卡车事故风险吗？
Can we Estimate Truck Accident Risk from Telemetric Data using Machine Learning?
论文作者
论文摘要
道路事故的社会成本很高，可以通过使用机器学习改善风险预测来降低。这项研究调查了在长距离卡车上收集的遥测数据是否可以用于预测与驾驶员相关的事故的风险。我们使用卡车运输公司提供的数据集，该数据集包含18个月的1,141个驾驶员的驾驶数据。 我们评估了两种不同的机器学习方法来执行此任务。在第一种方法中，使用新鲜算法从时间序列数据中提取功能，然后使用随机森林来估计风险。在第二种方法中，我们使用卷积神经网络直接估计时间序列数据的风险。我们发现，尽管有许多方法论尝试，但两种方法都无法成功估计该数据集发生事故的风险。我们讨论了使用遥测数据以估计可能解释这一负面结果的事故风险的困难。
Road accidents have a high societal cost that could be reduced through improved risk predictions using machine learning. This study investigates whether telemetric data collected on long-distance trucks can be used to predict the risk of accidents associated with a driver. We use a dataset provided by a truck transportation company containing the driving data of 1,141 drivers for 18 months. We evaluate two different machine learning approaches to perform this task. In the first approach, features are extracted from the time series data using the FRESH algorithm and then used to estimate the risk using Random Forests. In the second approach, we use a convolutional neural network to directly estimate the risk from the time-series data. We find that neither approach is able to successfully estimate the risk of accidents on this dataset, in spite of many methodological attempts. We discuss the difficulties of using telemetric data for the estimation of the risk of accidents that could explain this negative result.
