The invention discloses an expressway in-transit vehicle position prediction method. The method comprises the following steps: obtaining original trajectory data of a target vehicle from ETC (Electronic Toll Collection) data; performing time dimension stabilization processing on the original trajectory data of the target vehicle to obtain vehicle trajectory point data homogenized in the time dimension; performing spatial dimension stabilization processing on the homogenized vehicle track point data, so that the distance between the vehicle and the portal frame is mapped as the vehicle position variable quantity at adjacent moments, and obtaining the data after spatial-temporal dimension stabilization processing after ADF inspection; the method comprises the following steps: constructing a DLCNN-LSTM-Attention vehicle position prediction model based on L1 regularization; using a mean square error as a loss function, and using an Adam optimizer to train the vehicle position prediction model to obtain an optimal model; and inputting to-be-predicted data subjected to time-space dimension stabilization processing into the optimal model to obtain a predicted vehicle position. According to the invention, the non-uniformity and non-stability of the vehicle position data are effectively handled, and the accuracy is improved.
本发明公开一种高速公路在途车辆位置预测方法,从ETC数据中得到目标车辆的原始轨迹数据;目标车辆的原始轨迹数据进行时间维度的平稳化处理,得到在时间维度上均匀化的车辆轨迹点数据;对均匀化的车辆轨迹点数据进行空间维度的平稳化处理,使得车辆与门架的距离映射为相邻时刻车辆位置变化量,并通过ADF检验后得到时空维度平稳化处理后的数据;构建基于L1正则化的DLCNN‑LSTM‑Attention的车辆位置预测模型;使用均方误差作为损失函数,使用Adam优化器训练车辆位置预测模型得到最优模型;将待预测的时空维度平稳化处理后的数据输入最优模型得到预测的车辆位置。本发明有效应对了车辆位置数据的不均匀性和非平稳性,提高了准确性。
Expressway in-transit vehicle position prediction method
一种高速公路在途车辆位置预测方法
2024-03-08
Patent
Electronic Resource
Chinese
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