Implementation of Fuzzy Mamdani Method for Traffic Lights Smart City in Rangkasbitung, Lebak Regency, Banten Province (Case Study of the Traffic Light T-junction, Cibadak, By Pas Sukarno Hatta Street)

Traffic jams are a common sight that can be seen in almost all major cities in Indonesia. One of them is in Rangkasbitung City, Lebak Regency. This happens because the number of vehicles continues to increase. The traffic light control system implemented in Indonesia is a static preset time because the time of each phase is predetermined. This type of control system is still not effective in overcoming traffic congestion, especially at certain peak traffic jams. By using the Mamdani fuzzy logic system, it is possible to implement the human mindset into a system. Some rules can be set out in the fuzzy logic controller. The purpose of this study is to design a traffic light control system using fuzzy inference that regulates traffic based on its density. The data used are observations made at the research site. The conclusion of this study is to explain that the fuzzy mamdani method can solve existing problems in traffic congestion in Rangkasbitung City, Lebak Regency, Banten Province. Keywords: Implementation, Fuzzy Mamdani, traffic, Lebak Regency, Rangkasbitung City

Traffic light control systems are commonly utilized to monitor and manage the flow of autos across multiple road intersections. Since traffic jams are ubiquitous in daily life, A crucial aim is to optimize the functioning of traffic signals for optimal traffic flow. Traffic light control systems aim to make sure automobiles flow smoothly along transit routes. However, considering the numerous parameters involved, synchronizing several traffic signal systems at neighboring junctions is a difficult challenge. Conventional systems are incapable of dealing with varied flows nearing intersections. Furthermore, the present traffic system does not account for reciprocal interference between nearby traffic signal systems, the asymmetry of automobile flow with time, accidents, the passage of emergency vehicles, and pedestrian crossings. This causes traffic congestion and jams. The goal is to provide an artificial intelligence-based method that enables adaptive management of traffic lights at an intersection to reduce traffic congestion in transportation. A simulation is also created to help visualize the problem and its solution. In this venture, an algorithm that enables adaptive control of congested crossings is presented to reduce traffic congestion in transportation networks. The algorithm modifies the optimal light timings to employ in the following cycle based on the number of cars in the junction and the rise in this number. To improve reliability, employing a doppler radar sensor to collect data from traffic is suggested. The transmitter pulses waves with the assistance of a doppler radar sensor, and the receiver listens for waves flowing back to the antenna as they strike objects in the atmosphere between each pulse. The distance between the antenna and the item (in this application, vehicles) may be measured simply by calculating the time it took for the wave to return to the antenna. The Doppler shift is used to illustrate the speed differential in traffic. The change in frequency or wavelength of a wave perceived by an observer moving relative to the wave's source is known as the Doppler shift. This is a common occurrence with sound and light waves. When the source of a wave approaches an observer, the frequency of the wave increases, resulting in a "blue shift." The frequency of the wave drops as the source moves away from the observer, resulting in a "redshift." As a result, the flexibility of each junction to select its signal lengths saves waiting times and guarantees that the intersection functions at peak performance. Keywords: Traffic control systems, intelligent traffic lights, modeling of traffic lights, traffic jam problems, reinforcement learning, doppler sensors.

Traffic congestion especially at road intersections is becoming an issue for which road traffic users contend with daily. The conventional traffic light applies a fixed logic of allocating equal “GO” time to lanes of traffic at road intersections irrespective of the density of traffic on each lane. Using the PIC18F4550 microcontroller interfaced with infrared sensors, a new traffic light control system was developed to ease the flow of traffic at a particular troubled spot in Uyo Metropolis (Abak Road, Udo Eduok, Udo Obio streets intersection) in Akwa Ibom State, Nigeria. Simulation of the proposed design was performed using the Proteus Software while implementation was carried out on a prototype. Performance evaluation of the prototype implemented showed that the 4 arrays (IR Transmitter and Receiver) of infrared sensors interfaced with the microcontroller using AND gates and strategically positioned to read the density of traffic on each lane at the intersection triggered when a vehicle comes between the transmitter and the receiver. Obtaining inputs from these sensors together with the logic from the microcontroller, a new traffic light control system was developed capable of controlling traffic based on the density of each lane of traffic. Results obtained from simulation and implementation of the design indicates that the traffic control system with the PIC18F4550 microcontroller and the infrared sensors gives a better performance compared to the conventional traffic light control system.Keywords: Road traffic density, Traffic light, Uyo metropolis, ‘+’ road intersection, Proteus

The traffic lights control system is broadly implemented to track and control the flow of vehicles through the intersection of multiple roads. Nevertheless, the synchronization of traffic light system at adjacent junctions is an intricate issue given the different parameters involved. Existing traffic light control systems do not control many flows approaching the same junctions. This results in traffic jams and congestion at urban areas or major cities with high volume traffic consisting of various types of vehicles. This includes emergency ambulances travelling on the same traffic junction during peak hour traffic. Thus, an enhanced traffic light control system is imperative to provide a smooth and free flow for an ambulance on the way to its destination. The Smart Ambulance Traffic Control System proposed in this paper is an integrated system of traffic light control for emergency ambulance service. The traffic lights can be controlled in a timely and efficient manner every time an emergency ambulance is approaching. The Radio-Frequency Identification (RFID) is used as an instrument to communicate with traffic lights during traffic congestion. The emergency ambulance driver needs to activate the RFID tag to allow the detection of RFID readers to control the traffic light operation at the upcoming traffic light junctions. The traffic lights in the path of the ambulance are forced to be green to allow the emergency ambulance to pass through the junction with top priority. Immediately after the ambulance has passed the junction, the control system will reset and return to normal operations.

Traffic signals are vital for traffic management. It is essential to increase the efficiency of the traffic controller to alleviate traffic congestion. In this paper, the traffic light control system using fuzzy logic is designed to minimize queue count (QC) and waiting time (WT) for vehicles at the intersection. Real-time traffic is generated and a fuzzy logic controller is implemented to control the traffic light system. In the proposed fuzzy logic controller, trapezoidal membership functions are combined with the rules to generate green light time for input QC and WT. The improvement in QC, WT, and tailback is witnessed by using Static Phase Scheduling Traffic Light System (SPSTLS) as a benchmark to measure the performance of the proposed controller. The performance comparison records a significant improvement of 81.68% in QC, 87.04% in average WT and 18.05% in the tailback. The results show that fuzzy logic controlled Dynamic Phase Scheduling Traffic Light System (DPSTLS) has the potential to resolve the problem of QC, WT, travel cost, accident, and traffic congestion.

Intelligent traffic light control system belongs to the field of transportation public safety control. So far, the traffic light control system on the market cannot change the working time of the traffic lights according to the actual traffic flow, which is not conducive to the easing of the traffic flow and easy to cause traffic jams. Therefore, it is necessary to solve the problem of the existing traffic light control system which cannot adjust the working hours of the traffic light according to the actual road conditions, so as to alleviate the traffic jam at the intersection to a certain extent. In this paper, the design of intelligent traffic light control system uses AT89C51 MCU as the core device. The system controls the traffic of different times and different conditions, and it can alleviate traffic jam to a certain extent. This paper analyzes the hardware and software designes of intelligent traffic light control system, and simulates traffic light control by simulation software. The system of this paper is simple in structure, economical and practical, reliable in operation and it can effectively dredge the traffic.

Intelligent transportation is a typical case of cyber-physical system (CPS). Due to the rapid increasing of the number of vehicles in city, problems caused by vehicles, like congestion and environment pollution, are becoming more and more serious. Traffic light control system is often used to control the vehicles passing for a solution of the congestion in the city. Present control systems used are normally assigned as to be static, i.e., traffic light signal changes in a static way. The aim of this paper is to propose a dynamical traffic light control system, i.e., change the traffic light signals in real time following the speed of vehicles. This system is an instance of V2I(Vehicle to Infrastructure) communication model, realizing data transmission between vehicles and traffic lights. Vehicles send speed messages to the traffic light when passing an intersection, then the traffic light analyzes the information and adjusts the signal time in real time. Each traffic light in each direction has a control strategy of itself without the orthogonal requirement. Therefore, the traffic light is a kind of cyber-physical system. This traffic light control system can maximize the number of vehicles passing intersection, and as a result, minimize the congestion and pollution. A traffic light control algorithm based on speed of vehicles and its simulation are presented. The safety and liveness of this control system are discussed too.

Conventional traffic light control systems are based on fixed time intervals of the traffic lights. These conventional fixed traffic light controllers have limitations and are less efficient because they use a hardware, which functions according to the program that lacks the flexibility of modification and adaptation on a real time basis. Thus due to the fixed time intervals of green and red signals there is excess and unnecessary waiting time on roads and vehicles consume more fuel. This eventually adds up to the environmental pollution and creates several health issues among the people on road and residing nearby. Also these conventional traffic light control systems do not have any provisions to provide any information on traffic densities on various roads, which leads to traffic congestions. Thus, to make traffic light controlling and traffic regulation more efficient, we exploit the emergence of new technique called as Adaptive Traffic Light Control System (ATLCS). The proposed system makes the use of network of array of sensors for sensing the traffic. On categorising this sensed traffic the timing intervals of red and green lights at each crossing of roads are intelligently decided and varied so as to keep the waiting time minimum. Thus, optimization of the traffic light switching increases the road capacity, saves time for travelling and prevents traffic congestions. The system also aims at incorporating special provisions for making immediate way for the emergency vehicles. GSM cell phone interface provides traffic information to drivers on demand and also helps in efficiently regulating the traffic and alternate route taking decisions. Efficiently regulated traffic also reduces pollution. The performance of this proposed system is compared with the conventional fixed time traffic light control system. The various performance evaluation parameters are efficient operation of sensor assembly, time saved per cycle, signal switching frequency, efficient emergency mode operation and the satisfactory operation of SMS using GSM mobile. The effectiveness of this system is shown and discussed in this paper by means of simulation results and on-board display.

In recent years, several studies have been conducted on the dynamic control of traffic signal durations using deep reinforcement learning with the aim of reducing traffic congestion. The unique advantages of independent control of traffic signals include reduction in the cost of information transmission and stable control without being affected by the failure of other traffic signals. However, conventional deep reinforcement learning methods such as Deep Q-Network may degrade the learning performance in a multi-agent environment where there are multiple traffic signals in the environment. Therefore, we propose a traffic light control system based on the dual targeting algorithm, which incorporates reinforcement of successful experiences in multi-agent environments, with the aim of realizing a better traffic light control system. The experimental results in a multi-agent environment using a traffic flow simulator based on simulation of urban mobility (SUMO) show that the proposed traffic light control system reduces the waiting time at traffic lights by 33% compared to a conventional traffic light control system using deep reinforcement learning. In the future works, we aim to apply this research to traffic light control systems in real environments.

Traffic congestion has become a serious issue due to the growing number of vehicles in Malaysia. Traffic light control system is widely used to control the flow of road junction. Currently, most of the traffic light system used pre-time and count down timers to control traffic flow. Due to the fixed-time setting, often the system unable to handle unexpected heavy traffic flows and cause traffic jam. Thus, there is a need of adaptive traffic signals which are able to do real time monitoring to control traffic light signal based on traffic density. This study proposed an adaptive traffic light control system that uses image processing and image matching technique in controlling the traffic in an effective manner by taking images of each lane at a junction. The density of traffic in the images at each junction are compared. Results show that more time are allocated for the vehicles on the densest road to pass compared to other less dense road. Edge operation detector is used to detect the density of traffic at each lane. In this study, a comparison study was carried out by applying five different edge detectors namely Roberts, Sobel, Log, Canny and active contour. Among these detectors, Canny edge operation detector has found to be the best as it could extract actual edges with average time of 0.453 second.

Traffic congestion is one of the main problems in transportation sector and it causes a lot of drawbacks to public. The traffic light system is used to reduce the level of occurring traffic congestion. Generally, the available traffic light systems use a fixed time setting. This old traffic control system is no longer able to manage the ever-changing traffic conditions effectively and efficiently, causing a long queue of vehicles. To overcome this problem, a traffic light control system that can adapt to actual conditions of road density and can run automatically is offered. This system utilizes Google Map API as a road density data source. The result of this study is a traffic control system that can adjust the green light time duration based on the obtained density values and density trends, simulation of this adaptive system as well as simulation results analysis. A prototype of this adaptive control system was also produced in this study.

Traffic directing and monitoring is becoming a difficult task now a days. Present day traffic light control system is based on a fixed time breaks of green, orange and red signals and thus the waiting time of vehicle is more and uses more fuel. Hence, ejection of CO from the vehicles at that region is more. Another major problem is the possibility of emergency vehicles to get stuck in traffic jam. This paper’s aim is to design a safe and efficient movement of traffic by reducing the effect of CO at that area and allot the right way, which minimizes the delay. Also, it allows the facility for clearance of the emergency vehicle, if any, by providing early warning along with direction from which vehicle is approaching. In this system a CO sensor is used which is used to sense the CO emissions from vehicles. Based on CO levels, the traffic light indication will be given in the appropriate directions. The status will be shown in LCD. Pedestrian crossing system is also included in this system for safe and efficient flow of traffic. Keywords: Traffic control system, CO, emergency vehicle clearance Cite this Article Keerthana MS, Minnu Manjooran, Hima IS et al . Smart Traffic Control System with Overriding Technology . Current Trends in Signal Processing . 2016; 6(2): 24–27p.

Intelligent Transportation System (ITS) has been emerged an important component and widely adopted for the smart city as it overcomes the limitations of the traditional transportation system. Existing fixed traffic light control systems split the traffic light signal into fixed duration and run in an inefficient way, therefore, it suffers from many weaknesses such as long waiting time, waste of fuel and increase in carbon emission. To tackle these issues and increase efficiency of the traffic light control system, in this work, a Dynamic and Intelligent Traffic Light Control System (DITLCS) is proposed which takes real-time traffic information as the input and dynamically adjusts the traffic light duration. Further, the proposed DITLCS runs in three modes namely Fair Mode (FM), Priority Mode (PM) and Emergency Mode (EM) where all the vehicles are considered with equal priority, vehicles of different categories are given different level of priority and emergency vehicles are given at most priority respectively. Furthermore, a deep reinforcement learning model is also proposed to switch the traffic lights in different phases (Red, Green and Yellow), and fuzzy inference system selects one mode among three modes i.e., FM, PM and EM according to the traffic information. We have evaluated DITLCS via realistic simulation on Gwalior city map of India using an open-source simulator i.e., Simulation of Urban MObility (SUMO). The simulation results prove the efficiency of DITLCS in comparison to other state of the art algorithms on various performance parameters.

Intersection which is one of the most important parts of the urban road network, is the main place where traffic accidents and traffic jams always happen. At the same time, it is the longest delay time place in special vehicle accidents. Therefore, modeling the highly efficient special vehicle priority traffic signal light control system to ensure smooth and safe traffic, is a very important thing needed to do. This paper proposes a new modeling method for the traffic signal light control system based on TPZN which integrates time Petri net and Z. Firstly, the system model is established according to the flowchart of priority access policy. Secondly, the behavior of the system is modeled by considering the dependence between the states with external rules. Finally, a case is given to illustrate and analyze the method. The results show that this formal verification method can accurately simulate and predict the behavior of the system.

Vehicular traveling is increasing throughout the world, particularly in large urban areas. By the increasing use of automobiles in cities, traffic congestion occurs. Thus, there is a requirement for optimizing traffic control methods for better accommodating the increasing demand. Therefore, the transportation system will continue to grow, and intelligent traffic controls have to be employed to face the road traffic congestion’s problems. Fuzzy controllers have been widely used in many consumer products and industrial applications successfully over the past two decades. For traffic control, however, fuzzy controllers have not been widely applied. This research presents an application of fuzzy logic for multi-agent based autonomous traffic lights control system using wireless sensors to overcome problems like congestion, accidents, speed, and traffic irregularity. The real time parameters such as traffic density and queue length are obtained by using image-processing techniques. Thus, On and Off timings for the green, red and or amber lights are adjusted to the actual road conditions. Fuzzy logic has been widely used to develop a traffic signal controller because it allows qualitative modeling of complex systems. This paper describes a fuzzy logic signal controller for a four–way intersection suitable for mixed traffic, including a high proportion of motorcycles. The proposed agent-based approach can provide a preferred solution by minimizing the vehicles’ waiting time especially the emergency vehicles using fuzzy logic control under the situations that normally occur during emergencies. The effectiveness of this approach is tested by taking two traffic junctions. Keyword: Traffic lights control system, application of fuzzy logic, autonomous systems, congestion control