scholarly journals Optimization of smart traffic lights to prevent traffic congestion using fuzzy logic

2019 ◽  
Vol 17 (1) ◽  
pp. 320 ◽  
Author(s):  
Dian Hartanti ◽  
Rosida Nur Aziza ◽  
Puji Catur Siswipraptini
Keyword(s):  
Fuzzy Logic ◽  
Traffic Congestion ◽  
Traffic Lights ◽  
Smart Traffic

scholarly journals TRAFFIC LIGHT CONTROL USING FUZZY LOGIC MAMDANI METHOD

2019 ◽  
Vol 3 (1) ◽  
pp. 1-10
Author(s):  
Paula Juniana ◽  
Lukman Hakim
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Traffic Congestion ◽  
Green Light ◽  
Light Control ◽  
Traffic Light ◽  
Additional Time ◽  
Traffic Lights ◽  
Traffic Jams ◽  
Traffic Light Control

Traffic congestion is a common occurrence in Indonesia. Traffic congestion is increasing from year to year, causing many things to happen, such as longer travel time, increased transportation costs, serious disruptions to transporting products, decreasing levels of work productivity, and wasteful use of labor energy. Congestion is also caused by a traffic light control system that is made with a fixed time so it can not detect the density of certain paths. Traffic lights in Indonesia, frequent damage that makes the density and the flow of his road vehicles can not be controlled. From these problems, conducted research to reduce the density of vehicles using infrared sensors and see the waiting time of the vehicle when the red light. The traffic light control system will use Fuzzy Logic Mamdani method. In Mamdani method by applying fuzzy into each variable and will be done matching between rule with condition which fulfilled to determine contents of output to be executed by prototype. This congestion detection will help the system in controlling the green light time by looking at stable, medium, and traffic jams. When the bottleneck starts to detect, the prototype will add a green light time according to the condition that is 0 seconds, 5 seconds, 10 seconds, and 15 seconds. However, when the streets are not detected by traffic jams, the green light will be back to normal at 15 seconds without additional time

scholarly journals An Intelligent Fuzzy Logic Based Traffic Controller

2020 ◽  
Keyword(s):  
Fuzzy Logic ◽  
Cycle Time ◽  
Traffic Congestion ◽  
Carbon Dioxide Emissions ◽  
Road Traffic ◽  
Green Light ◽  
Traffic Lights ◽  
Road Congestion ◽  
Light Duration ◽  
Matlab Programming

Increasing road congestion, travel time, number of accidents, carbon dioxide emissions, and fuel consumption are some of the consequences of growth in the vehicle population. Therefore, intelligent traffic controllers are required to solve road traffic congestion problems. The results of prevalent methods, including preset cycle time controller and vehicle-actuated controller, indicated that they do not effectively perform at traffic peak moments. Therefore, due to the deficiency of common methods, fuzzy logic based traffic signal controllers have attracted a lot of attention among researchers. In this article, a fuzzy logic based algorithm for 4-way intersections is proposed and it consists of two main stages for sorting the phase and determining the green light duration. The proposed system is simulated in the MATLAB programming environment and the performance of the designed controller and a conventional controller is compared for some of the presumed conditions. The results of applying the proposed system indicate that this algorithm has a better performance in different traffic conditions in contrast to a preset cycle time controller and it can reduce the number of vehicles behind traffic lights at intersections and the waiting time of passengers.

scholarly journals Designing a Smart Traffic Light Algorithm (HMS) Based on Modified Round Robin Algorithm

2017 ◽  
Vol 2 (1) ◽  
pp. 27-30
Author(s):  
Hozan Khalid Hamarashid ◽  
Miran Hama Rahim Saeed ◽  
Soran Saeed
Keyword(s):  
Traffic Congestion ◽  
Fixed Time ◽  
Traffic Load ◽  
Traffic Light ◽  
Traffic Jam ◽  
Traffic Lights ◽  
Traffic System ◽  
Light System ◽  
Smart Traffic ◽  
Fixed System

Nowadays, traffic light system is very important to avoid car crashes and arrange traffic load. In the Sulaimani City / Iraq, there are many traffic problems such as traffic congestion or traffic jam and the amount of time provided manually to the traffic light system. This is the main difficulty that we try to solve. The traffic lights exist but still do not manage traffic congestion due to the fixed time provided for each lane regardless of their different load. Therefore, we are proposing to change the traditional traffic system to smart traffic system (adaptive system). This paper Focuses on the existing system (fixed system), then propose the adaptive one. The main crucial side effects of the existing system are:   Emergency cases: congested traffics might block the way of emergencies for instance ambulance, which transports people to the hospital Wasting time of people generally and specially Delays, which lead people to not to be punctual, this means people arrive late to the work  Wasting more fuels as staying more in the traffics, which affects the environment by increasing pollution.

scholarly journals 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)

KOMTEKINFO ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 176-185
Author(s):  
Dentik Karyaningsih ◽  
Robby Rizky
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Traffic Congestion ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Light Control ◽  
Traffic Light ◽  
Traffic Lights ◽  
Traffic Jams ◽  
Traffic Light Control

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

scholarly journals Smart Traffic Lights over Vehicular Named Data Networking

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 83 ◽  
Author(s):  
Majed Al-qutwani ◽  
Xingwei Wang
Keyword(s):  
Traffic Congestion ◽  
Fixed Time ◽  
Light Signal ◽  
Vehicular Traffic ◽  
Named Data Networking ◽  
Traffic Light ◽  
Traffic Lights ◽  
Light System ◽  
Smart Traffic ◽  
Data Networking

The existing traffic light system fails to deal with the increase in vehicular traffic requirements due to fixed time programming. Traffic flow suffers from vehicle delay and congestion. A new networking technology called vehicular ad hoc networking (VANET) offers a novel solution for vehicular traffic management. Nowadays, vehicles communicate with each other (V2V), infrastructure (V2I), or roadside units (V2R) using IP-based networks. Nevertheless, IP-based networks demonstrate low performance with moving nodes as they depend on communication with static nodes. Currently, the research community is studying a new networking architecture based on content name called named data networking (NDN) to implement it in VANET. NDN is suitable for VANET as it sends/receives information based on content name, not content address. In this paper, we present one of VANET’s network applications over NDN, a smart traffic light system. Our system solves the traffic congestion issue as well as reducing the waiting time of vehicles in road intersections. This system replaces the current conventional system with virtual traffic lights (VTLs). Instead of installing traffic lights at every intersection, we utilize a road side unit (RSU) to act as the intersection controller. Instead of a light signal, the RSU collects the orders of vehicles that have arrived or will arrive at the intersection. After processing the orders according to the priority policy, the RSU sends an instant message for every vehicle to pass the intersection or wait for a while. The proposed system mimics a human policeman intersection controlling. This approach is suitable for autonomous vehicles as they only receive signals from the RSU instead of processing many images. We provide a map of future work directions for enhancing this solution to take into account pedestrian and parking issues.

Smart Traffic Light Management Systems

2020 ◽  
Vol 11 (3) ◽  
pp. 22-47
Author(s):  
Aws Abed Al Raheem Magableh ◽  
Mohanad A. Almakhadmeh ◽  
Nawaf Alsrehin ◽  
Ahmad F. Klaib
Keyword(s):  
Law Enforcement ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Video Data ◽  
Traffic Light ◽  
Infrared Sensors ◽  
Traffic Lights ◽  
Loop Detection ◽  
Light Management ◽  
Smart Traffic

Traffic congestion is a major concern in many cities. Failure to heed signals, poor law enforcement, and bad traffic light management are main factors that have led to traffic congestion. One of the most important problems in cities is the difficulty of further expanding the existing infrastructures. Having that in mind, the main accessible and available alternatives that could provide better management of the traffic lights is to use technological systems. There are many methods available for traffic management such as video data analysis, infrared sensors, inductive loop detection, wireless sensor networks, and a few other technologies. This research is focused on reviewing all these existing methods and studies using a systematic literature review (SLR). The SLR was intended to improve the synthesis of research by introducing a systematic process. This article aims at analyzing and assessing the existing studies against selected factors of comparison. The study achieves these aims by analyzing 78 main studies. The research outcomes indicated that there are decent numbers of studies that have been proposed in the area of smart traffic light management. However, less attention has been paid on the possibility of investigating the use of live traffic data to improve the accuracy of traffic management.

scholarly journals Network Traffic Lights with Telemetry and Wireless Connectivity Perimeter

2019 ◽  
Vol 256 ◽  
pp. 05002
Author(s):  
España Víctor ◽  
Chuchon Eddy ◽  
Caytuiro David ◽  
Iván Advincula ◽  
Mario Chauca
Keyword(s):  
Control Systems ◽  
Real Time ◽  
Network Traffic ◽  
Traffic Congestion ◽  
Vehicle Control ◽  
Traffic Light ◽  
Traffic Lights ◽  
The Creation ◽  
Research Document ◽  
Smart Traffic

The following research document seeks to show an alternative to vehicle control systems using existing technologies to develop a system that is efficient and reliable. The creation and operation of a traffic light network will be presented, which will be located in an area where there is traffic congestion. The following network will reorganize, optimize, and measure the vehicular flow in real time. In some countries, intelligent traffic lights have been implemented, with which they have obtained satisfactory benefits by improving the vehicular flow of the places where these systems are located; for this reason we consider it necessary to use smart traffic lights in our country.

scholarly journals TRAFFIC LIGHT CONTROL USING FUZZY LOGIC MAMDANI METHOD

2019 ◽  
Vol 3 (1) ◽  
pp. 1-10
Author(s):  
Paula Juniana ◽  
Lukman Hakim
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Traffic Congestion ◽  
Green Light ◽  
Light Control ◽  
Traffic Light ◽  
Additional Time ◽  
Traffic Lights ◽  
Traffic Jams ◽  
Traffic Light Control

Traffic congestion is a common occurrence in Indonesia. Traffic congestion is increasing from year to year, causing many things to happen, such as longer travel time, increased transportation costs, serious disruptions to transporting products, decreasing levels of work productivity, and wasteful use of labor energy. Congestion is also caused by a traffic light control system that is made with a fixed time so it can not detect the density of certain paths. Traffic lights in Indonesia, frequent damage that makes the density and the flow of his road vehicles can not be controlled. From these problems, conducted research to reduce the density of vehicles using infrared sensors and see the waiting time of the vehicle when the red light. The traffic light control system will use Fuzzy Logic Mamdani method. In Mamdani method by applying fuzzy into each variable and will be done matching between rule with condition which fulfilled to determine contents of output to be executed by prototype. This congestion detection will help the system in controlling the green light time by looking at stable, medium, and traffic jams. When the bottleneck starts to detect, the prototype will add a green light time according to the condition that is 0 seconds, 5 seconds, 10 seconds, and 15 seconds. However, when the streets are not detected by traffic jams, the green light will be back to normal at 15 seconds without additional time

scholarly journals An Agent-Based Simulation to Explore Communication in a System to Control Urban Traffic with Smart Traffic Lights

2021 ◽  
Vol 10 (3) ◽  
pp. 209-225
Author(s):  
Marcos De Oliveira ◽  
Robson Teixeira ◽  
Roberta Sousa ◽  
Enyo José Tavares Gonçalves
Keyword(s):  
Traffic Control ◽  
Traffic Congestion ◽  
Urban Traffic ◽  
Transport Infrastructure ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Traffic Lights ◽  
Intelligent Software ◽  
Smart Traffic ◽  
Urban Traffic Control

Populational growth increases the number of cars and makes the transport infrastructure increasingly saturated. The control of traffic lights by intelligent software is a promising way to solve the problem caused by this situation. This article addresses this problem that occurs in urban traffic. An agent-based simulation of an urban traffic control system is proposed. The solution is offered as intelligent traffic lights as agents to alleviate traffic congestion at a given location. Each agent controls a crossing and maintains communication with agents from other corners. Thus, they can have greater control of a larger area and identify patterns that can help them to solve congestion problems. The results of our simulated experiments point to the improvement of the urban traffic when using the proposed Multiagent System, in comparison with an approach that uses crossing agents without communication and other that implements static traffic lights.

scholarly journals Detecting Different Road Infrastructural Elements Based on the Stochastic Characterization of Speed Patterns

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Mario Muñoz-Organero ◽  
Ramona Ruiz-Blázquez
Keyword(s):  
Total Variation ◽  
Traffic Congestion ◽  
Automatic Detection ◽  
Mass Function ◽  
Total Variation Distance ◽  
Statistical Moments ◽  
Probability Mass Function ◽  
Traffic Lights ◽  
Traffic Conditions ◽  
Variation Distance

The automatic detection of road related information using data from sensors while driving has many potential applications such as traffic congestion detection or automatic routable map generation. This paper focuses on the automatic detection of road elements based on GPS data from on-vehicle systems. A new algorithm is developed that uses the total variation distance instead of the statistical moments to improve the classification accuracy. The algorithm is validated for detecting traffic lights, roundabouts, and street-crossings in a real scenario and the obtained accuracy (0.75) improves the best results using previous approaches based on statistical moments based features (0.71). Each road element to be detected is characterized as a vector of speeds measured when a driver goes through it. We first eliminate the speed samples in congested traffic conditions which are not comparable with clear traffic conditions and would contaminate the dataset. Then, we calculate the probability mass function for the speed (in 1 m/s intervals) at each point. The total variation distance is then used to find the similarity among different points of interest (which can contain a similar road element or a different one). Finally, a k-NN approach is used for assigning a class to each unlabelled element.

Sign in / Sign up

Export Citation Format

Share Document