我们可以使用机器学习从遥测数据中估计卡车事故风险吗？
Can we Estimate Truck Accident Risk from Telemetric Data using Machine Learning?
摘要：
道路事故的社会成本很高，可以通过使用机器学习改进风险预测来降低。本研究调查了长途卡车上收集的遥测数据是否可用于预测与驾驶员相关的事故风险。我们使用了一家卡车运输公司提供的数据集，其中包含1，141名司机18个月的驾驶数据。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FRESH算法从时间序列数据中提取特征，然后使用随机森林来估计风险。在第二种方法中，我们使用一个卷积神经网络直接估计的风险从时间序列数据。我们发现，这两种方法都不能成功地估计这个数据集上的事故风险，尽管有许多方法上的尝试。我们讨论的困难，使用遥测数据的事故，可以解释这一负面结果的风险估计。
英文摘要：
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.
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。
