基于多元因素的Bi-LSTM高速公路交通流预测

针对影响高速公路交通流量因素多样而复杂的问题, 提出了一种基于多元因素的Bi-LSTM (双向长短期记忆网络)高速公路交通流预测模型. 首先对原始数据进行清理和相关性分析, 提高研究准确性, 降低数据维度; 其次,基于时间滑动窗口, 构建多元因素交通流时序矩阵, 并以MAE与RMSE为评估指标, 训练优化Bi-LSTM...

Bi-LSTM Expressway Traffic Flow Prediction Based on Multiple Factor Data

Aiming at the diverse and complex factors affecting expressway traffic flow, this study proposes a Bi-LSTM prediction model based on multiple factors. Firstly, the original data are cleaned up and analyzed with respect to their correlation to improve the research accuracy and reduce the data dimension. Secondly, a multi-factor time series matrix for traffic flow is constructed based on the time sliding window and the proposed model is trained and optimized with MAE and RMSE as the evaluation indicators. This model considers high-correlation influencing factors such as weather conditions, holidays, and toll, as well as changes in the preorder and postorder of traffic flow. With the expressway toll data in Shaanxi Province as the object, the results show that the proposed model is more applicable and accurate than GRU and LSTM in the short-term prediction of expressway traffic flow.