Abstract
•Fuzzy logic-based energy management based new traffic flow model was applied in the cyber physical system.•This research focuses on the construction of a fuzzy logic-based energy management and TFP model (FLEM-TFP) for CPS in ITS with this rationale.•An adaptive neuro fuzzy inference system (ANFIS) model is used to calculate the engine torque necessary depending on various measurements.•The FLEM-TFP technique combines two distinct processes: optimal ANFIS-based energy management and a TFP model based on SFO.•The SFO model is created by training fuzzy wavelet neural network (FWNN) with the SFO algorithm to predict traffic flow in an ITS environment.
Cyber-Physical Systems (CPS) is a multidisciplinary effort that links cyber and physical vectors. Cyber systems may be incorporated into any physical system, such as intelligent transportation systems (ITS), industries, and cities, to improve its intelligence, energy efficiency, and comfort. The design of self-driving cars has a large influence on vehicle travel demand and energy consumption. ITS also heavily relies on the Traffic Flow Prediction (TFP) system. With this rationale, this work focuses on the development of a Fuzzy Logic-based Energy Management and TFP model (FLEM-TFP) for CPS in ITS. The FLEM-TFP approach proposed here involves two primary processes: energy management and TFP. An adaptive neuro-fuzzy inference system (ANFIS) model is also utilized to compute the engine torque required based on various readings. In addition, a sailfish optimizer (SFO)-based fuzzy wavelet neural network (FWNN) approach is used in the ITS to estimate traffic flow. According to the findings of the experiments, the FLEM-TFP approach outperformed previous state-of-the-art procedures.
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