Shiju George

Intelligent transportation system (ITS) is an advance leading edge technology that aims to deliver innovative services to different modes of transport and traffic management. Traffic flow prediction (TFP) is one of the key macroscopic parameters of traffic that supports traffic management in ITS. Growth of the real-time data in transportation from various modern equipments, technology, and other resources has led to generate big data, posing a huge concern to deal with. Recently, deep learning (DL) techniques have demonstrated the capability to extract comprehensive features efficiently, using multiple hidden layers, from such huge raw, unstructured, and nonlinear data. Nonlinearity in traffic data is the major cause of inaccuracy in TFP. In this article, we propose a flow strength indicator-based Chronological Dolphin Echolocation-Fuzzy, a bioinspired optimization method with fuzzy logic for incremental learning of deep belief network. Technical indicators provide flow strength features as an input to the model. Hidden layers of DL architecture consequently learn more features and propagate it as an input to next layer for supervised learning. The degree of membership to the features is identified by the membership functions, followed by weight optimization using Dolphin Echolocation algorithm to fit the model for the nonlinear data. Experiments performed on two different data sets, namely Traffic-major roads and performance measurement system-San Francisco (PEMS-SF), show good results for the proposed deep architecture. The analysis of the proposed method using log mean square error and log root mean square deviation acquires a minimum value of 2.4141 and 0.61 for the Traffic-major roads database taken for the time step duration of 1 year and a minimum value of 1.6691 and 0.5208 for PEMS-SF data set for the time step interval of 5 minutes, respectively. These positive results demonstrate key importance of our traffic flow model for the transportation system.

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).