In this paper, a smart and centralized traffic light control and monitoring system is proposed to control the modern transportation systems and make the city safer, using programmable logic controllers (PLCs) and programmable electronic microcontrollers. A camera is used to monitor the mishaps during the traffic flow of vehicles. The system has four modes, i.e., auto-control mode (ACM), manual control mode (MCM), central control mode (CCM), and remote control mode (RCM). In the auto-control mode (ACM), the traffic light signals are controlled automatically through programmable electronic microcontrollers at specific times, while the manual control mode (MCM) controls the traffic light signals manually (on–off switches) according to the traffic congestion. The central control mode (CCM) is considered to be a centralized mode, where the programmable logic controller (PLC) is used by a computer workstation. In this mode, the traffic light signals are controlled by a ladder logic program of the PLC. The third mode, RCM, is linked with the second mode, CCM; in this mode, the traffic light signals are remotely controlled through the software by transferring programmable logic controller (PLC) functions to the software interface. As a result, this transportation system can also be controlled remotely. The designed system delivers suitable, flexible, and reliable control for traffic signaling and transportation.

A major problem in the photovoltaic (PV) system is to determine the maximum power point (MPP) and to overcome the limitations of environmental change. To resolve the limitation of different techniques with high convergence rate and less fluctuations, a hybrid model of fractional open circuit voltage is proposed. For partial shading, incremental conductance is used. The proposed technique is extremely useful, provides high efficiency, and takes less time to achieve the MPP. The tenacity of the proposed method has been checked using MATLAB/Simulink, which clearly shows that the proposed technique has high efficiency compared to other MPP tracking methods.