摘要
深度学习短时交通流预测中,存在数据处理实时性较弱,以及算法对交通流数据的复用和修正能力不足导致预测性能较差的问题。针对这一问题,提出一种融合滞后极限学习机的深度双向长短时记忆神经网络短时交通流预测方法。首先,引入权值共享机制对双向长短时记忆网络模型进行结构优化,在模型训练过程中不断进行权重更新和偏置更新,从而充分利用逆序逆转数据增强数据的复用和修正能力;其次,为了进一步提高算法实时性,引入极限学习机模型,并在其神经元激活函数中嵌入生物神经系统中的滞后参数进行优化,加速了运算效率,提升算法的整体实时性。实验结果表明:提出的方法预测精度和算法实时性均有提升,与经典方法CNN-BiLSTM和多元集合CNN-LSTM相比,平均绝对误差分别减少了6.82、6.47,计算速度分别提高了12、19 s,具备良好的短时交通流预测能力和实时性。
In deep learning short-term traffic flow prediction,there are some problems,such as the weak real-time performance in data processing and the poor prediction performance caused by the insufficient reuse and correction ability of the algorithm for traffic flow data.To address these problems,a kind of short term traffic flow prediction method using deep bidirectional long short-term memory neural network which incorporated a hysteretic extreme learning machine was proposed.Firstly,a weight-sharing mechanism was introduced to optimize the structure of the bidirectional long and short-term memory network model,and the weights and biases were continuously updated during the training process of the model,so as to make full use of the reverse-order data to enhance the reuse and correction ability of the data.Secondly,in order to further improve the real-time performance of the algorithm,the extreme learning machine model was introduced and the hysteretic parameters of the biological neural system were embedded in the activation function of its neurons,which optimized the computational efficiency and enhanced the overall real-time performance of the algorithm.The experimental results show that the proposed method has improved prediction accuracy and algorithm real-time performance,its average absolute error is respectively reduced by 6.82 and 6.47 and its computation speed is respectively improved by 12 and 19 seconds,compared with the classical method CNN-BiLSTM and multivariate ensemble CNN-LSTM,which has good short-term traffic flow prediction capability and real-time performance.
作者
张阳
王梓良
姚芳钰
许浩越
杨书敏
ZHANG Yang;WANG Ziliang;YAO Fangyu;XU Haoyue;YANG Shumin(School of Transportation,Fujian University of Technology,Fuzhou 350118,Fujian,China;School of Computer Science and Technology,Harbin University of Science and Technology,Harbin 150080,Heilongjiang,China;School of Transportation Engineering,Tongji University,Shanghai 201804,China)
出处
《重庆交通大学学报（自然科学版）》
CAS
CSCD
北大核心
2024年第6期39-46,共8页
Journal of Chongqing Jiaotong University(Natural Science)
基金
福建省自然科学基金高联合资助项目(2023J01946)。
关键词
交通工程
深度学习
双向长短时记忆神经网络
极限学习机
交通预测
traffic engineering
deep learning
bidirectional long-short term memory neural networks
extreme learning machines
traffic prediction
