当前位置: X-MOL 学术Comput. Intell. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
An improved long short‐term memory networks with Takagi‐Sugeno fuzzy for traffic speed prediction considering abnormal traffic situation
Computational Intelligence ( IF 2.8 ) Pub Date : 2020-02-27 , DOI: 10.1111/coin.12291
Shiju George 1, 2 , Ajit Kumar Santra 1
Affiliation  

Traffic speed prediction is an emerging paradigm for achieving a better transportation system in smart cities and improving the heavy traffic management in the intelligent transportation system (ITS). The accurate traffic speed prediction is affected by many contextual factors such as abnormal traffic conditions, traffic incidents, lane closures due to construction or events, and traffic congestion. To overcome these problems, we propose a new method named fuzzy optimized long short‐term memory (FOLSTM) neural network for long‐term traffic speed prediction. FOLSTM technique is a hybrid method composed of computational intelligence (CI), machine learning (ML), and metaheuristic techniques, capable of predicting the speed for macroscopic traffic key parameters. First, the proposed hybrid unsupervised learning method, agglomerated hierarchical K‐means (AHK) clustering, divides the input samples into a group of clusters. Second, based on parameters the Gaussian bell‐shaped fuzzy membership function calculates the degree of membership (high, low, and medium) for each cluster using Takagi‐Sugeno fuzzy rules. Finally, the whale optimization algorithm (WOA) is used in LSTM to optimize the parameters obtained by fuzzy rules and calculate the optimal weight value. FOLSTM evaluates the accurate traffic speed from the abnormal traffic data to overcome the nonlinear characteristics. Experimental results demonstrated that our proposed method outperforms the state‐of‐the‐art approaches in terms of metrics such as mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE).

中文翻译:

考虑交通异常情况的改进的Takagi-Sugeno模糊长记忆网络用于交通速度预测

交通速度预测是在智能城市中实现更好的交通系统并改善智能交通系统(ITS)中繁忙交通管理的新兴范例。准确的交通速度预测会受到许多上下文因素的影响,例如异常交通状况,交通事件,由于施工或事件导致的车道封闭以及交通拥堵。为了克服这些问题,我们提出了一种用于长期交通速度预测的名为模糊优化长短期记忆(FOLSTM)神经网络的新方法。FOLSTM技术是一种由计算智能(CI),机器学习(ML)和元启发式技术组成的混合方法,能够预测宏观交通关键参数的速度。首先,提出了混合无监督的学习方法K均值(AHK)聚类将输入样本分为一组聚类。其次,基于参数,高斯钟形模糊隶属度函数使用Takagi-Sugeno模糊规则计算每个聚类的隶属度(高,低和中)。最后,在LSTM中使用鲸鱼优化算法(WOA)来优化通过模糊规则获得的参数并计算最佳权重值。FOLSTM从异常交通数据中评估准确的交通速度,以克服非线性特征。实验结果表明,我们提出的方法在诸如均方误差(MSE),均方根误差(RMSE),均值绝对误差(MAE)和均值绝对百分比误差等指标方面均优于最新方法(MAPE)。
更新日期:2020-02-27
down
wechat
bug