Tian Xiujuan, Ding Jinyong, Liu Huanying, Xing Xue, Liu Jiaojiao
School of Transportation Science and Engineering, Jilin Jianzhu University, Changchun, 130118, Jilin, China.
FAW Logistics Co., Ltd, Changchun, 130011, Jilin, China.
Sci Rep. 2025 Jul 19;15(1):26296. doi: 10.1038/s41598-025-11919-6.
Based on historical data, a new short-term traffic flow prediction model (ICEEMDAN-MPE-PSO-DELM-ARIMA) for intersections is proposed. In order to improve prediction accuracy, ICEEMDAN decomposition algorithm is applied on traffic flow time series to obtain multiple Intrinsic Mode Function (IMF) components. Then PSO-MPE algorithm is introduced to obtain the multi-scale permutation entropy values of each IMF component, to judge the randomness. According to the randomness, different prediction models are established. Prediction models based on PSO-DELM algorithm are established for IMF components with big randomness. ARIMA prediction models are established for IMF components with small randomness. In order to obtain the final predicted traffic flow values, multiple prediction results are added together. Finally, two actual intersections are selected to verify the proposed model. Results show that compared with other models, the proposed model has the smallest prediction errors and the best fitting effect with the real values, which can effectively improve prediction accuracy.
基于历史数据，提出了一种新的用于交叉路口的短期交通流预测模型（ICEEMDAN-MPE-PSO-DELM-ARIMA）。为了提高预测精度，将ICEEMDAN分解算法应用于交通流时间序列以获得多个本征模态函数（IMF）分量。然后引入PSO-MPE算法来获取每个IMF分量的多尺度排列熵值，以判断随机性。根据随机性，建立不同的预测模型。对具有较大随机性的IMF分量建立基于PSO-DELM算法的预测模型。对具有较小随机性的IMF分量建立ARIMA预测模型。为了获得最终的预测交通流值，将多个预测结果相加。最后，选择两个实际交叉路口来验证所提出的模型。结果表明，与其他模型相比，所提出的模型具有最小的预测误差和与实际值最佳的拟合效果，能够有效提高预测精度。
