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>

A framework for dynamic smart traffic light management system

2021 ◽  
Author(s):  
Awad Alharbi ◽  
George Halikias ◽  
Adnan Ahmed Abi Sen ◽  
Mohammad Yamin
Keyword(s):  
Management System ◽  
Traffic Light ◽  
Light Management ◽  
Smart Traffic

Smart Traffic Light Management System for Emergency Vehicle

2021 ◽  
pp. 471-483
Author(s):  
Nitin Vohra ◽  
Pranjal Kandhari ◽  
Abhinav Gupta ◽  
Shilpa Gupta ◽  
Arpit Shrotiya ◽  
...  
Keyword(s):  
Management System ◽  
Emergency Vehicle ◽  
Traffic Light ◽  
Light Management ◽  
Smart Traffic

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 An amalgamation of YOLOv4 and XGBoost for next-gen smart traffic management system

2021 ◽  
Vol 7 ◽  
pp. e586
Author(s):  
Pritul Dave ◽  
Arjun Chandarana ◽  
Parth Goel ◽  
Amit Ganatra
Keyword(s):  
Waiting Time ◽  
Traffic Management ◽  
Green Light ◽  
Gradient Boosting ◽  
Optimal Time ◽  
Traffic Light ◽  
Ensemble Technique ◽  
Extreme Gradient Boosting ◽  
On The Road ◽  
Smart Traffic

The traffic congestion and the rise in the number of vehicles have become a grievous issue, and it is focused worldwide. One of the issues with traffic management is that the traffic light’s timer is not dynamic. As a result, one has to remain longer even if there are no or fewer vehicles, on a roadway, causing unnecessary waiting time, fuel consumption and leads to pollution. Prior work on smart traffic management systems repurposes the use of Internet of things, Time Series Forecasting, and Digital Image Processing. Computer Vision-based smart traffic management is an emerging area of research. Therefore a real-time traffic light optimization algorithm that uses Machine Learning and Deep Learning Techniques to predict the optimal time required by the vehicles to clear the lane is presented. This article concentrates on a two-step approach. The first step is to obtain the count of the independent category of the class of vehicles. For this, the You Only Look Once version 4 (YOLOv4) object detection technique is employed. In the second step, an ensemble technique named eXtreme Gradient Boosting (XGBoost) for predicting the optimal time of the green light window is implemented. Furthermore, the different implemented versions of YOLO and different prediction algorithms are compared with the proposed approach. The experimental analysis signifies that YOLOv4 with the XGBoost algorithm produces the most precise outcomes with a balance of accuracy and inference time. The proposed approach elegantly reduces an average of 32.3% of waiting time with usual traffic on the road.

A Novel Self-Organizing Approach to Automatic Traffic Light Management System for Road Traffic Network

2019 ◽  
Vol 110 (3) ◽  
pp. 1303-1321
Author(s):  
Amita Jain ◽  
Sudesh Yadav ◽  
Sonakshi Vij ◽  
Yogesh Kumar ◽  
Devendra Kumar Tayal
Keyword(s):  
Management System ◽  
Road Traffic ◽  
Traffic Network ◽  
Traffic Light ◽  
Light Management ◽  
Self Organizing
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