Smart Traffic Light System

2021 ◽  
Vol 9 (9) ◽  
pp. 679-686
Author(s):  
Rashi Maheshwari
Keyword(s):  
Traffic Congestion ◽  
Traffic Signals ◽  
Traffic Monitoring ◽  
Traffic Signal ◽  
Traffic Signal Control ◽  
Traffic Light ◽  
Mathematical Functions ◽  
Light System ◽  
Smart Traffic ◽  
And Control

Abstract: Traffic signal control frameworks are generally used to monitor and control the progression of cars through the intersection of roads. Moreover, a portable controller device is designed to solve the issue of emergency vehicles stuck in overcrowded roads. The main objective of this paper is to design and implement a suitable algorithm and its simulation for an intelligent traffic signal simulator. The framework created can detect the presence or nonappearance of vehicles within a specific reach by setting appropriate duration for traffic signals to react accordingly. By employing mathematical functions and algorithms to ascertain the suitable timing for the green signal to illuminate, the framework can assist with tackling the issue of traffic congestion. The explanation relies on recent fixed programming time. Keywords: Smart Traffic Light System, Smart City, Traffic Monitoring.

Modeling and Simulation of Urban Arterial Traffic Signal Coordinated Control

2014 ◽  
Vol 543-547 ◽  
pp. 1417-1422
Author(s):  
Wei Li ◽  
Xin Bi ◽  
Yun Xia Cao ◽  
Jin Song Du
Keyword(s):  
Traffic Congestion ◽  
Control Method ◽  
Coordinated Control ◽  
Traffic Monitoring ◽  
Traffic Signal ◽  
Traffic Signal Control ◽  
Monitoring And Control ◽  
Total Delay ◽  
Arterial Traffic ◽  
Urban Arterial

Traffic congestion is a major concern for many cities throughout the world. Developing a sophisticated traffic monitoring and control system would result in an effective solution to this problem. In order to reduce traffic delay, a novel urban arterial traffic signal coordinated control method is presented. The total delay of downstream and upstream vehicles is considered and the function describing the relationship between vehicles delay and signal offset among intersections is established. Finally, comparing the performance of traffic signal under method proposed in this paper with the traditional isolated traffic signal control method, the microscopic simulation results show that the method proposed in this paper has better performance in the aspect of reducing the vehicles delay. The offset model is tested in a simulation environment consisting of a core area of three intersections. It can be concluded that the proposed method is much more effective in relieving oversaturation in a network than the isolated intersection control strategy.

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 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.

scholarly journals Traffic Light Optimization using OpenCV

2020 ◽  
Vol 6 (12) ◽  
pp. 171-175
Author(s):  
Aditya Lahoty
Keyword(s):  
Real Time ◽  
Waiting Time ◽  
Traffic Congestion ◽  
Computer Software ◽  
Green Light ◽  
Traffic Signals ◽  
Signalized Intersections ◽  
Traffic Signal ◽  
Traffic Light ◽  
Real Time Traffic

Traffic Light Optimization aims to find the solution for an increased amount of unnecessary waiting time on traffic signals. Traffic Signal Optimization is the process of changing the timing parameters relative to the length of the green light for each traffic movement and the timed relationship between signalized intersections using a computer software program. Our project aims to set the timer of green light based on real-time traffic congestion i.e. number of vehicles in a particular direction of the traffic light. To work in this project, we are using the OpenCV method to detect vehicles and then perform our calculation in the algorithm to predict the time for the green light to be in an active state.

scholarly journals Dynamic Control of Traffic Signals using Traffic Data from Google Maps and Road Cameras

2019 ◽  
Vol 8 (2S3) ◽  
pp. 686-690
Keyword(s):  
Waiting Time ◽  
Traffic Congestion ◽  
Waiting Times ◽  
Dynamic Control ◽  
Traffic Signals ◽  
Traffic Signal ◽  
Traffic Signal Control ◽  
Traffic Data ◽  
Google Maps ◽  
Signal Light

Traffic congestion is a condition that occurs due to over usage of roads and results in increased waiting time, slower speeds and increased vehicle queuing. Every year billions of dollars are lost due to time wasted by waiting at traffic signals. In this paper, the problem of dynamic control of traffic signals using traffic data obtained from Google Maps and Road Cameras is explored. The traffic signal control algorithm described will adjust both the ordering of different lights in real time as well as the duration of each of them. The ordering (sequence) and length of each traffic signal light for a period of time will be affected by factors such as traffic volume, waiting time and vehicle queuing characteristics. The results from the above algorithm’s simulations are compared to the average waiting times the vehicles experience in the case of static traffic signals which employ a fixed duration and sequence policy for each of the colors of the traffic signal.

scholarly journals The Necessity for a Sustainable Traffic Light System: The Case Study of el-Koura District Traffic Light System

2017 ◽  
Vol 2 (9) ◽  
pp. 42
Author(s):  
Najib Nicolas Gerges ◽  
Samer Mohammad Fawaz
Keyword(s):  
Traffic Congestion ◽  
Constant Speed ◽  
Speed Limit ◽  
Traffic Light ◽  
Negative Effects ◽  
Light System ◽  
Smart Traffic

Traffic congestion is one of the major problems that Lebanon agonizes from. In this case study, the main objective is to provide a smart traffic light system that can reduce congestion in addition to all of its negative effects. The method used for design was the Webster Method. The results are very favorable. The system was optimized to reduce traffic light stops and idle running times provided that the drivers abide by the speed limit on the roadway and try to maintain a constant speed. 

scholarly journals Dynamic Traffic Light Control

2020 ◽  
Vol 8 (6) ◽  
pp. 3228-3231
Keyword(s):  
Traffic Congestion ◽  
Traffic Management ◽  
Red Light ◽  
Traffic Monitoring ◽  
Intelligent Transport System ◽  
Light Control ◽  
Traffic Light ◽  
Traffic Lights ◽  
Traffic Light Control ◽  
Traffic Management System

Intelligent Transport System (ITS) is blooming worldwide. The Traditional Traffic management system is a tedious process and it requires huge man power, to overcome this we have proposed an automatic Traffic monitoring system that has effective fleet management. The current transportation system at intersections and junctions has Traffic Lights with Fixed durations which increase the unnecessary staying time which intern harms the environment. An Adaptive traffic light control is implemented using SUMO simulator, that changes the duration of Green and Red light according to the traffic flow. This is an effective and efficient way to reduce the Traffic congestion. The traffic congestion is determined by taking the object count using deep learning approach (Convolutional Neural Network).

scholarly journals Software-defined Architecture for Urban Regional Traffic Signal Control

2020 ◽  
Vol 32 (2) ◽  
pp. 229-236
Author(s):  
Songhang Chen ◽  
Dan Zhang ◽  
Fenghua Zhu
Keyword(s):  
Traffic Congestion ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Signal Control ◽  
Traffic Signal Control ◽  
The Novel ◽  
Simulation Experiments ◽  
Microscopic Traffic Simulation ◽  
Control Devices ◽  
Innovation Platform

Regional Traffic Signal Control (RTSC) is believed to be a promising approach to alleviate urban traffic congestion. However, the current ecology of RTSC platforms is too closed to meet the needs of urban development, which has also seriously affected their own development. Therefore, the paper proposes virtualizing the traffic signal control devices to create software-defined RTSC systems, which can provide a better innovation platform for coordinated control of urban transportation. The novel architecture for RTSC is presented in detail, and microscopic traffic simulation experiments are designed and conducted to verify the feasibility.

scholarly journals Design and Implementation of a Smart Traffic Light Management System Controlled Wirelessly by Arduino

2020 ◽  
Vol 14 (07) ◽  
pp. 32
Author(s):  
Abdul Hadi M. Alaidi ◽  
Ibtisam A. Aljazaery ◽  
Haider TH. Salim Alrikabi ◽  
Ibrahim Nasir Mahmood ◽  
Faisal Theyab Abed
Keyword(s):  
Waiting Time ◽  
Management System ◽  
Traffic Light ◽  
Economic Problems ◽  
Design And Implementation ◽  
Traffic Jams ◽  
Light System ◽  
Car Accidents ◽  
Light Management ◽  
Smart Traffic

<p>In Iraq, the number of people who own vehicles has grown up significantly. However, this increment in vehicles number doesn’t accomplished by a study of roads and intersections expansion. As a result, traffic jams became a big problem that led to long waiting time at each intersection, increased car accidents, pollution, and economic problems. To solve this problem a Smart Traffic Light System (STLS) has been implemented using Arduino, camera, IR sensor to overcome traffic jams problems in Kut city – Iraq.</p>

scholarly journals An Edge Based Multi-Agent Auto Communication Method for Traffic Light Control

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4291 ◽  
Author(s):  
Qiang Wu ◽  
Jianqing Wu ◽  
Jun Shen ◽  
Binbin Yong ◽  
Qingguo Zhou
Keyword(s):  
Reinforcement Learning ◽  
Control Method ◽  
Communication Protocol ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Light Control ◽  
Traffic Light ◽  
Traffic Light Control ◽  
Multi Agent

With smart city infrastructures growing, the Internet of Things (IoT) has been widely used in the intelligent transportation systems (ITS). The traditional adaptive traffic signal control method based on reinforcement learning (RL) has expanded from one intersection to multiple intersections. In this paper, we propose a multi-agent auto communication (MAAC) algorithm, which is an innovative adaptive global traffic light control method based on multi-agent reinforcement learning (MARL) and an auto communication protocol in edge computing architecture. The MAAC algorithm combines multi-agent auto communication protocol with MARL, allowing an agent to communicate the learned strategies with others for achieving global optimization in traffic signal control. In addition, we present a practicable edge computing architecture for industrial deployment on IoT, considering the limitations of the capabilities of network transmission bandwidth. We demonstrate that our algorithm outperforms other methods over 17% in experiments in a real traffic simulation environment.

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