GRU-LSTM Model Based on the SSA for Short-Term Traffic Flow Prediction

Changxi Ma;Xiaoyu Huang;Yongpeng Zhao;Tao Wang;Bo Du
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Abstract

The transportation department relies on accurate traffic forecasting for effective decision-making. However, determining relevant parameters for existing traffic flow prediction models poses challenges. To address this issue, this study proposes a hybrid model, sparrow search algorithm-gated recurrent unit-long short-term memory (SSA-GRU-LSTM), which leverages the SSA to optimize the GRUs and LSTM networks. The SSA is employed to identify the optimal parameters for the GRU-LSTM model, mitigating their impact on prediction accuracy. This model integrates the predictive efficiency of the GRU, LSTM's capability in temporal data analysis, and the fast convergence and global search attributes of the SSA. Comprehensive experiments are conducted to validate the proposed method via traffic flow datasets, and the results are compared with those of baseline models. The numerical results demonstrate the superior performance of the SSA-GRU-LSTM model. Compared with the baselines, the proposed model results in reductions in the root mean square error (RMSE) of 4.632%–45.206%, the mean absolute error (MAE) of 2.608%–53.327%, the mean absolute percentage error (MAPE) of 1.324%–13.723%, and an increase in $R^{2}$ of 0.5%–17.5%. Consequently, the SSA-GRU-LSTM model has high prediction accuracy and measurement stability.
基于SSA的GRU-LSTM模型短期交通流预测
交通部门依靠准确的交通预测来进行有效的决策。然而,对于现有的交通流预测模型,如何确定相关参数是一个挑战。为了解决这一问题,本研究提出了一种混合模型,即麻雀搜索算法-门控循环单元-长短期记忆(SSA- gru -LSTM),该模型利用SSA来优化gru和LSTM网络。SSA用于识别GRU-LSTM模型的最优参数,减轻其对预测精度的影响。该模型综合了GRU的预测效率、LSTM的时间数据分析能力和SSA的快速收敛和全局搜索特性。在交通流数据集上进行了综合实验,验证了该方法的有效性,并与基线模型的结果进行了比较。数值结果证明了SSA-GRU-LSTM模型的优越性能。与基线相比,该模型的均方根误差(RMSE)降低了4.632% ~ 45.206%,平均绝对误差(MAE)降低了2.608% ~ 53.327%,平均绝对百分比误差(MAPE)降低了1.324% ~ 13.723%,$R^{2}$增加了0.5% ~ 17.5%。因此,SSA-GRU-LSTM模型具有较高的预测精度和测量稳定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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