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Traffic jam that is resulted from the buildup of vehicles on the road has become an important problem, which leads to an interference with drivers. The impacts it has on cost and time effectiveness may take the form of increased fuel consumption, traffic emissions, and noise. This paper offers a solution by creating a smart traffic light using a fu...
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... In the 20th century, many technologies have been developed that lead to automatic control systems. Vehicle automation systems have attracted the attention of many parties both in research and commercial terms [1], [2], [3], [4], [5] . There are a lot of technologies nowadays, one of the most common to increase driving comfort is cruise control system technology or automatic speed control [6]. ...
An adaptive control system is an advanced method for controlling the speed of a moving motorized vehicle. Using this intelligent control system, the driver can easily control the speed of the car according to his wishes or the prevailing situation. The adaptive control system consists of a sensor attached to a moving vehicle which then registers the speed of the car and provides input to the processing unit. The controller is designed according to the force exerted by the car to drive a certain distance in a certain time. This time, the control uses a PID controller. This method is followed for various tunings of Kp, Ki, and Kd values for the P, PI, PID, and IPD structures for a cruise control system using MATLAB. The PID used in this experiment is intended to control the speed to make it more stable and optimal.
... Sabri and El Kamoun [13] stated that conventional traffic light systems offer a distributed solution for managing congestion but usually fail to regulate traffic flow in reality. Desmira et al. [14] created an intelligent traffic light using fuzzy logic inference for an adaptive traffic light. It is to manage the dynamics of the vehicles at an intersection. ...
Vehicles wishing to pass on the toll road must diverge from the traffic flow on public roads. The toll road movement consists of low vehicles (LV) and heavy vehicles (HV). The public road movement is a mixed traffic flow consisting of LV, HV, motorcycles, and unmotorized vehicles. Traffic lights are used at the T-junction of the toll road gate for travel safety management. The traffic lights that implement a fixed-time strategy should be optimized for efficiency. This study aims to review the safety of travel management at T-junctions for the toll road gate when adaptive traffic lights are used. The structural complexity of mathematical modeling with Petri net is used to analyze and measure the feasibility study. Results illustrate that the structural complexity of the traffic lights that implement a fixed time strategy equals 0.387. It is equal to 0.489 for the adaptive traffic lights. The structural complexity of adaptive traffic lights is 25% higher than conventional systems that implement a fixed-time strategy. The adaptive traffic lights time strategy is feasible for travel safety for road users. The travel time is efficient and comfortable because the delay is low. Furthermore, traffic lights can adjust to the demand of vehicles queuing.
... Also, the stochastic behavior of the traffic affects the travel time of buses. Delays at intersections [6] due to queuing, non-optimized signals [7], and mixed traffic lanes lead to an excess travel time for buses. Additionally, the bus driver's behavior [8] in scenarios such as early start, on-time start, and delayed start of the trips will make the prediction process more complicated. ...
p>A reliable transit service can motivate commuters to switch their traveling
mode from private to public. Providing necessary information to passengers
will reduce the uncertainties encountered during their travel and improve
service reliability. This article addresses the challenge of predicting dynamic
travel times in urban areas where real-time traffic flow information is
unavailable. In this perspective, a hybrid travel time estimation model
(HTTEM) is proposed to predict the dynamic travel time using the predicted
travel times of the machine learning model and the preceding trip details. The
proposed model is validated using the location data of public transit buses of,
Tumakuru, India. From the numerical results through error metrics, it is found
that HTTEM improves the prediction accuracy, finally, it is concluded that the
proposed model is suitable for estimating travel time in urban areas with
heterogeneous traffic and limited traffic infrastructure.</p
... Information searching such as the specifications, failures and functions off all components have been carried out. Moreover, programming using Arduino microcontroller, circuit construction using Proteus software and each sensor circuit to the microcontroller and output [23]- [25]. ...
The number of missing children and kidnapping is on the rise in recent years. Every parent wills definitely going through an agonizing experience to have their children missing. Therefore, there are many safety measurements to prevent this incident from happening. The help of modern technologies is one of the ways to reduce children missing and kidnapping. A child can be tracked by using the global positioning system (GPS) and global system for mobile communication (GSM) technology. Advanced child monitoring systems are expensive. Not all families have the same living standards. For this purpose, a low-cost child tracking system is proposed in this study. The implementation of the proposed approach is reported in real-time.
... These variations serve a variety of purposes, including vehicular signals, pedestrian signals, audio signals for the visually impaired, and flashing or flashing indicators. However, the appearance of smart traffic lights has revolutionized urban traffic management, [18]. ...
Currently, population growth in cities results in an increase in urban vehicle traffic. That is why it is necessary to improve the quality of life of citizens based on the improvement of transport control services. To solve this problem, there are solutions, related to the improvement of the road infrastructure by increasing the roads or paths. One of the solutions is using traffic lights that allow traffic regulation automatically with machine learning techniques. That is why the implementation of an intelligent traffic light system with automatic learning by reinforcement is proposed to reduce vehicular and pedestrian traffic. As a result, the use of the YOLOv4 tool allowed us to adequately count cars and people, differentiating them based on size and other characteristics. On the other hand, the position of the camera and its resolution is a key point for counting vehicles by detecting their contour. An improvement in time has been obtained using reinforcement learning, which depends on the number of episodes analyzed and affects the length of training time, where the analysis of 100 episodes takes around 12 hours on a Ryzen 7 computer with a graphics card built-in 2 GB.
... In contrast, adaptive MTSC algorithms have the capability to dynamically adjust signal timings and phase differences based on real-time traffic conditions. When considering the application of the adaptive multi-agent reinforcement learning (MARL) paradigm, it is important to note that non-MARL-based MTSC algorithms [12]- [15] often exhibit myopic planning and are incapable of addressing largescale and complex MTSC problems. In contrast, MARL-based algorithms [16]- [25] utilize MARL technology [26]- [29] to address the challenge of sequential decision-making in MTSC. ...
p>Effective management of multi-intersection traffic signal control (MTSC) is vital for intelligent transportation systems. Multi-agent reinforcement learning (MARL) has shown promise in achieving MTSC. However, existing MARL-based MTSC algorithms have primarily focused on capturing the spatial relationship between multi-intersection traffic signals but have overlooking the importance of the temporally stable traffic pattern. This pattern refers to the fixed positions and relatively stable traffic flow between intersections over short periods in real-world MTSC scenarios, which indicates that the learned spatial relationships between traffic signals should co- evolve over time. To this end, we propose a novel algorithm called Coevolutionary Multi-Agent Reinforcement Learning (Co- evoMARL). CoevoMARL employs a graph neural network to capture the complex spatial interaction network among traffic signals. Furthermore, we propose a relationship-driven progres- sive LSTM (RDP-LSTM) that dynamically evolves the learned spatial interaction network over time by leveraging insights from the temporally stable traffic pattern. To accelerate convergence, we also propose the mutual information reward optimization (MIRO) technique, which strengthens the correlation between policy learning and high-performance samples by using a mutual information-based intrinsic reward. Experimental results on both synthetic and real-world datasets demonstrate the superiority of CoevoMARL over existing MTSC algorithms, providing valuable insights into incorporating the temporally stable traffic pattern. </p
... In contrast, adaptive MTSC algorithms have the capability to dynamically adjust signal timings and phase differences based on real-time traffic conditions. When considering the application of the adaptive multi-agent reinforcement learning (MARL) paradigm, it is important to note that non-MARL-based MTSC algorithms [12]- [15] often exhibit myopic planning and are incapable of addressing largescale and complex MTSC problems. In contrast, MARL-based algorithms [16]- [25] utilize MARL technology [26]- [29] to address the challenge of sequential decision-making in MTSC. ...
p>Effective management of multi-intersection traffic signal control (MTSC) is vital for intelligent transportation systems. Multi-agent reinforcement learning (MARL) has shown promise in achieving MTSC. However, existing MARL-based MTSC algorithms have primarily focused on capturing the spatial relationship between multi-intersection traffic signals but have overlooking the importance of the temporally stable traffic pattern. This pattern refers to the fixed positions and relatively stable traffic flow between intersections over short periods in real-world MTSC scenarios, which indicates that the learned spatial relationships between traffic signals should co- evolve over time. To this end, we propose a novel algorithm called Coevolutionary Multi-Agent Reinforcement Learning (Co- evoMARL). CoevoMARL employs a graph neural network to capture the complex spatial interaction network among traffic signals. Furthermore, we propose a relationship-driven progres- sive LSTM (RDP-LSTM) that dynamically evolves the learned spatial interaction network over time by leveraging insights from the temporally stable traffic pattern. To accelerate convergence, we also propose the mutual information reward optimization (MIRO) technique, which strengthens the correlation between policy learning and high-performance samples by using a mutual information-based intrinsic reward. Experimental results on both synthetic and real-world datasets demonstrate the superiority of CoevoMARL over existing MTSC algorithms, providing valuable insights into incorporating the temporally stable traffic pattern. </p
When the operating system environment temperature rises above safety, the CPU may become unresponsive or even malfunction. To address this problem, to achieve this goal a two-part system was designed. The first, consists of a controlled sensor that constantly monitors the room environment temperature and alerts the user if it rises above acceptable levels for computer use. The second part adopts a Python that uses the OS module, which provides a portable interface for OS-dependent tasks and shuts down the device to prevent it from behaving unexpectedly. A series of experiments at different temperatures demonstrated the ability of the device to alert the user. Keywords: CPU Microcontroller Operating system OS module Python Sensor Temperature sensor This is an open access article under the CC BY-SA license.
The energy use that is in excess of practicum students' needs and the disturbed comfort that the practicum students experience when conducting practicums in the Electrical engineering vocational education (EEVE) laboratory. The main objective of this study was to figure out how to predict and streamline the use of electrical energy in the EEVE laboratory. The model used to achieve this research's goal was called the adaptive neuro-fuzzy inference system (ANFIS) model, which was coupled with principal component analysis (PCA) feature selection. The use of PCA in data grouping performance aims to improve the performance of the ANFIS model when predicting energy needs in accordance with the standards set by the campus while still taking students' confidence in conducting practicum activities during campus operating hours into consideration. After some experiments and tests, very good results were obtained in the training: R=1 in training; minimum RMSE=0.011900; the epoch of 100 per iteration; and R=0.37522. In conclusion, the ANFIS model coupled with PCA feature selection was excellent at predicting energy needs in the laboratory while the comfort of the students during practicums in the room remained within consideration.
