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.

scholarly journals Automatic traffic light controller for emergency vehicle using peripheral interface controller

2019 ◽  
Vol 9 (3) ◽  
pp. 1788 ◽  
Author(s):  
Norlezah Hashim ◽  
Fakrulradzi Idris ◽  
Ahmad Fauzan Kadmin ◽  
Siti Suhaila Jaapar Sidek
Keyword(s):  
Traffic Congestion ◽  
Traffic Management ◽  
Emergency Situation ◽  
Light Signal ◽  
Emergency Vehicle ◽  
Traffic Light ◽  
Traffic Lights ◽  
The Road ◽  
Light Area ◽  
On The Road

Traffic lights play such important role in traffic management to control the traffic on the road. Situation at traffic light area is getting worse especially in the event of emergency cases. During traffic congestion, it is difficult for emergency vehicle to cross the road which involves many junctions. This situation leads to unsafe conditions which may cause accident. An Automatic Traffic Light Controller for Emergency Vehicle is designed and developed to help emergency vehicle crossing the road at traffic light junction during emergency situation. This project used Peripheral Interface Controller (PIC) to program a priority-based traffic light controller for emergency vehicle. During emergency cases, emergency vehicle like ambulance can trigger the traffic light signal to change from red to green in order to make clearance for its path automatically. Using Radio Frequency (RF) the traffic light operation will turn back to normal when the ambulance finishes crossing the road. Result showed the design is capable to response within the range of 55 meters. This project was successfully designed, implemented and tested.

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 Ant Colony Optimization for Real Time Traffic Lights Control on a Single Intersection

2020 ◽  
Vol 14 (02) ◽  
pp. 196 ◽  
Author(s):  
Nouha Rida ◽  
Mohammed Ouadoud ◽  
Aberrahim Hasbi
Keyword(s):  
Road Traffic ◽  
Green Light ◽  
Optimal Solution ◽  
Ant Colony ◽  
Traffic Data ◽  
Traffic Light ◽  
Traffic Lights ◽  
The Road ◽  
Real Time Traffic ◽  
On The Road

In this paper, we present a new scheme to intelligently control the cycles and phases of traffic lights by exploiting the road traffic data collected by a wireless sensor network installed on the road. The traffic light controller determines the next phase of traffic lights by applying the Ant Colony Optimazation metaheuristics to the information collected by WSN. The objective of this system is to find an optimal solution that gives the best possible results in terms of reducing the waiting time of vehicles and maximizing the flow crossing the intersection during the green light. The results of simulations by the SUMO traffic simulator confirm the preference of the developed algorithm over the predefined time controller and other dynamic controllers.

scholarly journals IoT based smart traffic signal monitoring system using vehicles counts

2018 ◽  
Vol 7 (2.21) ◽  
pp. 309 ◽  
Author(s):  
Senthil Kumar Janahan ◽  
M R.M. Veeramanickam ◽  
S Arun ◽  
Kumar Narayanan ◽  
R Anandan ◽  
...  
Keyword(s):  
Waiting Time ◽  
Clustering Algorithms ◽  
Traffic Signal ◽  
Traffic Network ◽  
The Road ◽  
Real Time Traffic ◽  
Proposed Model ◽  
Timing Interval ◽  
On The Road ◽  
Smart Traffic

Traffic signal management is one of the major problematic issues in the current situation. Such scenarios, every signal are getting 60 seconds of timing on the road at a regular interval, even when traffic on that particular road is dense. As per this proposed model in this article, which will be optimized the timing interval of the traffic signal purely depends on the number of vehicles on that particular roadside. The major advantage of this system is that it can able to decrease the more waiting time for the drivers to cross road signal.  In this model, we are using the clustering algorithms model which is based on KNN algorithm. Using this algorithm new model will be liable to determine expected required timing as per provided inputs to the signal which is vehicles count. The input of these systems is vehicles counts on each side of the road from crossing signal.  And this input will be determined on much time is to be provided. “Case studies on this system are traffic network and real-time traffic sub-networks are organized to get the effectiveness of the proposed model.”  

scholarly journals Analytical Models for Traffic Congestion and Accident Analysis

2021 ◽  
Author(s):  
Hongrui Liu ◽  
Rahul Ramachandra Shetty
Keyword(s):  
Traffic Congestion ◽  
Regression Tree ◽  
Machine Learning Algorithms ◽  
Analytical Models ◽  
Gradient Boosting ◽  
Decision Tree Classifier ◽  
The Road ◽  
Tree Classifier ◽  
Extreme Gradient Boosting ◽  
On The Road

In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accidents data consisting of 49 variables describing 4.2 million accident records from February 2016 to December 2020, as well as logistic regression, tree-based techniques such as Decision Tree Classifier and Random Forest Classifier (RF), and Extreme Gradient boosting (XG-boost) to process and train the models. These models will assist people in making smart real-time transportation decisions to improve mobility and reduce accidents.

scholarly journals Automated Density Based Traffic Light Control System using Arduino Platform

2019 ◽  
Vol 9 (1) ◽  
pp. 5269-5271
Keyword(s):  
Control System ◽  
Green Light ◽  
Fixed Time ◽  
Traffic Density ◽  
Light Control ◽  
Traffic Light ◽  
Light System ◽  
Time Period ◽  
Traffic Light Control ◽  
On The Road

In recent years, traffic Jams has become a serious problem across the globe. Current statistics reveals that, an average person spends around 4-6 months of his/her life by simply waiting for green light during traffic. Also when delay increases, it affects the commuters reach their destination so late resulting in severe consequences on day and day basis. In common, traffic can be controlled in several main junctions by incorporating either automated traffic light control system or through manual intervention by traffic police. However conventional traffic light system which involves fixed time slot allotted to each side of the junction is found to be poor efficient since it does not consider the varying traffic density. At certain instances, priority of the traffic system has to be changed dynamically based on more number of vehicles waiting on the road, arrival of VIP vehicles and ambulance vehicles etc. By considering the above facts, we have proposed an automated traffic light system which has inbuilt potential to prioritize the lane which is heavily congested. Our proposed system includes timer which runs for a specific time period and IR sensor is used to count the number of vehicles passing by during that time period. It also includes LED which is turned green on the lane with more number of vehicles. These peripherals were actuated based on the programming logic that is embedded in Arduino Mega platform. Finally, implementation results for the proposed system are provided in this paper.

scholarly journals OPTIMIZATION OF MANAGEMENT OF URBAN LIGHTS WITH THE USE OF NEURAL NETWORKS

THE BULLETIN ◽  
2021 ◽  
Vol 389 (1) ◽  
pp. 14-17
Author(s):  
A.А. Suleimen ◽  
G.B. Kashaganova ◽  
G.B. Issayeva ◽  
B.R. Absatarova ◽  
M.C. Ibraev
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Traffic Management ◽  
Urban Traffic ◽  
Control Parameters ◽  
Traffic Flows ◽  
Traffic Light ◽  
Traffic Lights ◽  
The Road ◽  
On The Road

One of the most pressing problems of large cities is the problem of traffic management of vehicles. The reason for this problem is an imperfect way to manage traffic flows. Traffic light regulation is of particular importance in traffic management. Most modern traffic light control systems operate at set time intervals and are not able to cope with the constantly changing situation on the road. A promising direction for solving this problem is to optimize the system using artificial neural networks. The advantage of neural networks is self-learning, which allows the system to adapt to the changing situation on the road. Despite numerous attempts, it has not yet been possible to obtain a high-quality mathematical model of urban traffic management. This model should determine the functional dependence of transport flow parameters on control parameters. Nowadays, traffic flows are regulated everywhere by means of traffic lights. If we can get a fairly accurate mathematical model of traffic flows, we can determine the optimal duration of the traffic signal phases to achieve the maximum capacity of the road network node. A fairly accurate mathematical model of traffic management that works in predictive mode will display an estimate of the optimal control parameters, as well as make correct decisions in emergency situations. Well-known mathematical models of road traffic take into account only the average values of traffic flows, and not the exact number of cars on each road section at a particular time.

scholarly journals Optimization of Traffic Light Control Using Fuzzy Logic Sugeno Method

2020 ◽  
Vol 1 (2) ◽  
pp. 51-61
Author(s):  
Ria Yuliani Kartikasari
Keyword(s):  
Fuzzy Logic ◽  
Green Light ◽  
Major Influence ◽  
Traffic Light ◽  
The Road ◽  
Traffic Conditions ◽  
Smooth Flow ◽  
Traffic Light Control ◽  
Light Duration ◽  
On The Road

Congestion is one of the big problems around the world, especially for big cities. Intersections are the scene of congestion because the lane is the meeting point of two or more roads which has a major influence on the smooth flow of vehicles on the road network. This congestion occurs due to various factors, one of which is the statistical traffic light duration, which does not match traffic conditions. Based on this, there needs to be a development in the timing of a more adaptive green light. This study describes the design of a traffic light controller using the Sugeno method fuzzy logic. This study aims to design a green light duration calculation by applying fuzzy logic that results in adaptive traffic light duration at intersections, by entering the density of each intersection path, which is divided into 4 inputs, namely regulated lane density, opposing lane density I, and opposite lane density. II, the density of the opposite lane III, with the aim of the system being able to produce a duration that is in accordance with the current traffic situation with an output in the form of a green light duration on the regulated lane.

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.

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