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.

Congestion Control by Reducing Wait time at the traffic junction using fuzzy logic controller

2020 ◽  
Vol 10 ◽  
Author(s):  
Manpreet Singh Bhatia ◽  
Alok Aggarwal
Keyword(s):  
Real Time ◽  
Traffic Congestion ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Detection System ◽  
Green Light ◽  
Wait Time ◽  
Arrival Rate ◽  
Traffic Signal ◽  
Traffic Light

Traffic congestion is one of the most severe problems especially in metro cities due to ever increasing number of vehicles on roads by 20% per year even with well-planned road management system and sufficient infra. Most of the existing traffic signal controllers use fixed cycle type, giving a constant green/red/yellow phase for each traffic signal cycle. These traditional controllers cannot adapt the dynamics of traffic at real time which a traffic man can do. Deploying traffic men at every traffic light junction is not feasible due to manpower shortage and cost considerations. In this work a three input fuzzy controller is proposed which can adapt the dynamics of real time traffic and reduce the congestion at the traffic light junction. Proposed fuzzy controller has three inputs namely; queue length, arrival rate and peak hours and one output parameter, time extension which is to be controlled by the use of the three input parameters. All four lanes have been allocated a fixed green signal time of 60 seconds at the start. Extension/decrease of the green light is done dynamically with ±28 seconds. Compared to conventional fixed cycle type, proposed approach gives a minimum improvement of 6% and a maximum of 47% depending on various traffic conditions at the junction. In terms of CO2 emission improvement of 20% and 42.12% and in terms of fuel consumption improvement of 34.73% and 57.18% has been observed compared to UCONDES (Urban CONgestion DEtection System) and OVMT (Original Vehicular Mobility Trace) respectively.

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.

scholarly journals Traffic signal coordination for Wellington Street West, Toronto, Ontario

2021 ◽  
Author(s):  
Sharareh Shadbakhsh
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Green Light ◽  
Traffic Signals ◽  
Traffic Signal ◽  
Continuous Series ◽  
Signal Timing ◽  
Signal Coordination ◽  
Road Users ◽  
Levels Of Service

The increasing volume of traffic in cities has a significant effect on road traffic congestion and the travel time it takes for road users to reach their destinations. Coordinating traffic signals, which is a system of light that cascade in sequence where a platoon of vehicles can travel through a continuous series of green light without stopping, can improve the driver's experience significantly. This report covers the development of a coordinated traffic signal system along Wellington Street West from Church Street to Blue Jays Way Street as part of a City of Toronto signal coordination project. The objective of this study is to improve coordination through modification of signal timing plans while maintaining reasonably minimal impacts to the side street levels of service and delays. The overall goal is to reduced travel times, delays, number of stops and fuel consumption, resulting in public benefit.

scholarly journals Traffic signal coordination for Wellington Street West, Toronto, Ontario

2021 ◽  
Author(s):  
Sharareh Shadbakhsh
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Green Light ◽  
Traffic Signals ◽  
Traffic Signal ◽  
Continuous Series ◽  
Signal Timing ◽  
Signal Coordination ◽  
Road Users ◽  
Levels Of Service

The increasing volume of traffic in cities has a significant effect on road traffic congestion and the travel time it takes for road users to reach their destinations. Coordinating traffic signals, which is a system of light that cascade in sequence where a platoon of vehicles can travel through a continuous series of green light without stopping, can improve the driver's experience significantly. This report covers the development of a coordinated traffic signal system along Wellington Street West from Church Street to Blue Jays Way Street as part of a City of Toronto signal coordination project. The objective of this study is to improve coordination through modification of signal timing plans while maintaining reasonably minimal impacts to the side street levels of service and delays. The overall goal is to reduced travel times, delays, number of stops and fuel consumption, resulting in public benefit.

scholarly journals Traffic Light Controller using Image Processing

2021 ◽  
Vol 12 (2) ◽  
pp. 405-411 ◽  
Author(s):  
Lakshmanan M, Et. al.
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Real Time ◽  
Traffic Congestion ◽  
Traffic Signals ◽  
Daily Basis ◽  
Traffic Density ◽  
Traffic Light ◽  
Traffic Lights ◽  
Traffic Systems

Traffic congestion at junctions is a serious issue on a daily basis. The prevailing traffic light controllers are unable to manage the different traffic flows. Most of the current systems operate on a timing mechanism that changes the signal after a particular interval of time. This may cause frustration and result in motorist's time waste. Traffic congestion is a major problem in the currently existing systems. Delays, safety, parking, and environmental problems are the main issues of current traffic systems that emit smoke and contribute to increasing Global Warming. Sensor-based systems reduce the waiting time and maximize the total number of vehicles that can cross an intersection. Our proposed system can control the traffic lights based on image processing without the need for traffic police. This can reduce congestion, delay, road accidents, need for manpower. Under image processing, we use sub techniques like RGB to Gray conversion, Image resizing, Image Enhancement, Edge detection, Image matching, and Timing allocation. A real-time image is captured for every 1 second. After edge detection procedure for both reference and real-time images, these images are compared using SURF Algorithm. Then the amount of traffic is detected and the details are stored in the server. Arduino is used for a traffic signal in the hardware part. It consists of a Wi-Fi module. The micro-controller used in the system Arduino. Four cameras are placed on respective roads and these cameras are used to capture images to analyze traffic density. Then the traffic signals are decided according to the density of traffic. Our technique can be effective to combat traffic on Indian Roads. A lot of time can be saved by deploying this system and also it conserves a lot of resources as well as the economy

scholarly journals A Particle Swarm Optimisation with Linearly Decreasing Weight for Real-Time Traffic Signal Control

Machines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 280
Author(s):  
Yanjun Shi ◽  
Yuhan Qi ◽  
Lingling Lv ◽  
Donglin Liang
Keyword(s):  
Real Time ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Particle Swarm ◽  
Traffic Signal ◽  
Particle Swarm Optimisation ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Interval Model ◽  
Real Time Traffic

Nowadays, traffic congestion has become a significant challenge in urban areas and densely populated cities. Real-time traffic signal control is an effective method to reduce traffic jams. This paper proposes a particle swarm optimisation with linearly decreasing weight (LDW-PSO) to tackle the signal intersection control problem, where a finite-interval model and an objective function are built to minimise spoilage time. The performance was evaluated in real-time simulation imitating a crowded intersection in Dalian city (in China) via the SUMO traffic simulator. The simulation results showed that the LDW-PSO outperformed the classical algorithms in this research, where queue length can be reduced by up to 20.4% and average waiting time can be reduced by up to 17.9%.

Traffic Signal Control for a Single Intersection-Based Intelligent Transportation System

2020 ◽  
pp. 159-180
Author(s):  
Nouha Rida ◽  
Mohammed Ouadoud ◽  
Abderrahim Hasbi
Keyword(s):  
Real Time ◽  
Waiting Time ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Information ◽  
Traffic Signal Control ◽  
Traffic Light ◽  
The Road ◽  
Real Time Traffic

Traffic optimization at an intersection, using real-time traffic information, presents an important focus of research into intelligent transportation systems. Several studies have proposed adaptive traffic lights control, which concentrates on determining green light length and sequence of the phases for each cycle in accordance with the real-time traffic detected. In order to minimize the waiting time at the intersection, the authors propose an intelligent traffic light using the information collected by a wireless sensors network installed in the road. The proposed algorithm is essentially based on two parameters: the waiting time in each lane and the length of its queue. The simulations show that the algorithm applied at a network of intersections improves significantly the average waiting time, queue length, fuel consumption, and CO2 emissions.

scholarly journals A Sarsa(λ)-Based Control Model for Real-Time Traffic Light Coordination

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaoke Zhou ◽  
Fei Zhu ◽  
Quan Liu ◽  
Yuchen Fu ◽  
Wei Huang
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Traffic Control ◽  
Learning Algorithm ◽  
Control Model ◽  
Traffic Signal ◽  
Signal Timing ◽  
Traffic Light ◽  
Real Time Traffic ◽  
Traffic Signal Timing

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control.

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.

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