An improved long short‐term memory networks with Takagi‐Sugeno fuzzy for traffic speed prediction considering abnormal traffic situation. (27th February 2020)

• Record Type: Journal Article
• Authors: George, Shiju; Santra, Ajit Kumar
• Is Part Of: Computational intelligence. Volume 36:Number 3(2020)
• Page Start: 964
• Page End: 993
• Publication Date: 2020-02-27
• Subjects: agglomerative hierarchical K‐means clustering -- fuzzy optimized long short‐term memory -- Gaussian bell‐shaped membership function -- traffic speed prediction -- whale optimization algorithm; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12291
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: 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).