Traffic Control Prediction Design Based on Fuzzy Logic and Lyapunov Approaches to Improve the Performance of Road Intersection

Abstract

Due to the increasing use of private cars for urbanization and urban transport, the travel time of urban transportation is increasing. People spend a lot of time in the streets, and the queue length of waiting increases accordingly; this has direct effects on fuel consumption too. Traffic flow forecasts and traffic light schedules were studied separately in the urban traffic system. This paper presents a new stable TS (Takagi–Sugeno) fuzzy controller for urban traffic. The state-space dynamics are utilized to formulate both the vehicle’s average waiting time at an isolated intersection and the length of queues. A fuzzy intelligent controller is designed for light control based upon the length of the queue, and eventually, the system’s stability is proved using the Lyapunov theorem. Moreover, the input variables are the length of queue and number of input or output vehicles from each lane. The simulation results describe the appearance of the proposed controller. An illustrative example is also given to show the proposed method’s effectiveness; the suggested method is more efficient than both the conventional fuzzy traffic controllers and the fixed time controller.

Smart fuzzy logic-based model of traffic light management

“…Based on the current traffic circumstances, this sort of traffic management system cannot prolong the current green light time length and cannot minimize the vehicle's waiting time at the red light. A fuzzy logic-based traffic light controller may be utilized for optimal regulation of traffic volumes, such as oversaturation or exceptional load circumstances [20,21,22,23,24,25,26,27,28]. The paper proposes the deployment of a developed fuzzy logic controller system for regulating traffic signals at numerous intersection models in the city.…”

“…The rules employ the input membership values as weighting factors to assess their impact on the final output conclusion's fuzzy output sets. After the functions have been inferred, scaled, and concatenated, they are defuzzified into a crisp out that powers the system [17,18,19,20,21]. The purpose of transportation research is to optimize the movement of people and products.…”

Lithium-Ion Battery Estimation in Online Framework Using Extreme Gradient Boosting Machine Learning Approach

“…Monitoring the battery's state online is crucial to avoid dangerous operating conditions and extend battery life. In this study, a method has been described to examine the aging of lithium polymer batteries using electrochemical impedance spectroscopy based on an analysis done of the impedance spectra [20][21][22]. It is crucial to predict degradation in lithium-ion batteries to ensure battery safety.…”

XGBoost-Based Remaining Useful Life Estimation Model with Extended Kalman Particle Filter for Lithium-Ion Batteries

“…On the other hand, because of their iterative structure along the time axis, RNNs require a long time for information to flow through the network for prediction. The attention method allows the network to focus on specific sections of sequences while utilizing the parallelization advantages of CNN on GPUs, and positional encoding adds location information [ 54 , 55 ]. Employing various quantifiable data from the BMS, such as charging profiles with varying voltage, current, and temperature as they age, one may estimate the RUL even when capacity regeneration is present.…”