这篇文章讨论了实时车辆轨迹预测的问题，特别是在设备运行过程中需要实时处理路线数据的情况下。文章提出使用LSTM模型来预测后续的经纬度点，通过比较预测点和定位点之间的距离来判断定位点的有效性。文章详细介绍了数据导入、特征工程、模型构建、模型评估和保存模型的过程，并提到可以通过迁移学习来进一步优化模型。文章强调了数据预处理的重要性，包括 筛选无效数据点和对时间序列进行处理，以提高LSTM模型的准确性。最后，文章还提到使用哈弗辛公式来评估模型的拟合效果。
☆17Oct 18, 2024Updated last year
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