scholarly journals Density and Time based Traffic Control System using Video Processing

2020 ◽  
Vol 32 ◽  
pp. 03028
Author(s):  
Tanvi Sable ◽  
Nehal Parate ◽  
Dharini Nadkar ◽  
Swapnil Shinde
Keyword(s):  
Video Processing ◽  
Traffic Control ◽  
Automated System ◽  
Traffic Density ◽  
Car Detection ◽  
Control Framework ◽  
Haar Cascade ◽  
Smart Traffic ◽  
Object Count ◽  
And Control

Traffic is the serious issue which each nation faces due to the expansion in number of vehicles. One of the strategies to beat the traffic issue is to build up a smart traffic control framework which depends on computing the traffic density and about utilizing constant video and picture preparing procedures. The topic is to control the traffic by deciding the traffic density on each roadside and control the traffic signal smartly by utilizing the density data. In this paper, an automated system based on processing of real time videos is proposed for detection of vehicles and recording count of them. The System will consist of various stages which includes Object Car Detection and Signal variation based on density. Captured video will be converted into frames and which will be pre-processed for object detection using Haar-Cascade than detected object count will be used to obtain the density and manipulate the signal accordingly. The density count algorithm works by contrasting the ongoing edge of live video by the reference picture and via looking through vehicles just in the district of intrigue (for example street region). The figured vehicle thickness can be contrasted and other course of the traffic so as to perform control of the traffic flags in more smart and proficient manner.

scholarly journals Macroscopic Traffic Flow Control Via State Estimation

2015 ◽  
Vol 15 (5) ◽  
pp. 5-16
Author(s):  
H. Abouaïssa ◽  
H. Majid
Keyword(s):  
Flow Control ◽  
State Estimation ◽  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Density ◽  
Traffic State ◽  
Loop Detectors ◽  
Traffic State Estimation ◽  
Estimation And Control ◽  
And Control

Abstract The studies presented in this paper deal with traffic control in case of missing data and/or when the loop detectors are faulty. We show that the traffic state estimation plays an important role in traffic prediction and control. Two approaches are presented for the estimation of the main traffic variables (traffic density and mean speed). The state constructors obtained are then used for traffic flow control. Several numerical simulations show very promising results for both traffic state estimation and control.

scholarly journals Proposed Algorithm for Smart Traffic Control using Ultrasonic Sensors

2019 ◽  
Vol 8 (6) ◽  
pp. 3912-3915
Keyword(s):  
Control System ◽  
Traffic Control ◽  
Smart City ◽  
Data Analytics ◽  
Traffic Density ◽  
Signal Timing ◽  
Technical Solution ◽  
Ultrasonic Sensors ◽  
Traffic Control System ◽  
Smart Traffic

In India, the concept of smart city has evolved since last few years. Smart city includes smart electricity distributions, smart parking, smart lighting on streets, smart water distribution, smart drainage system, smart pipe gas system, smart traffic control system etc. All smart systems listed need smart use of technical solution so that all systems will play critical role in making city as smart. As far as smart traffic control is concerned, there were few solutions suggested and implanted such as sensor with CCTV, camera with IR sensor and tags etc. The technical solution may include software, hardware, communication models, networking, usage of data and of-course data analytics. As large amount of data may be generated by the objects/components involved in the system, it must be analyzed properly. The data may be in structured or un-structured format. In this paper, smart traffic control system with efficient algorithm has been proposed with data analytics to control traffic, which controls the timing of the signal dynamically. At a junction, there is need to control the traffic and signal timing such that air and noise pollution also will be monitored and controlled. In this model, IoT system has been proposed with ultrasonic sensors to control the traffic. The signal timing will be dynamically monitored and adjusted with traffic density within a region. This will give solution to control, monitor the traffic at every signal in a city

scholarly journals Adaptive Traffic Management System using CNN (YOLO)

2021 ◽  
Vol 9 (VI) ◽  
pp. 3726-3732
Author(s):  
B. Sowmya
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Object Identification ◽  
Traffic Signal ◽  
Traffic Density ◽  
Loop Control ◽  
Control Framework

The huge number of vehicles on the roadways is making congestion a significant problem. The line longitudinal vehicle waiting to be processed at the crossroads increases quickly, and the traditionally used traffic signals are not able to program it properly. Manual traffic monitoring may be an onerous job since a number of cameras are deployed over the network in traffic management centers. The proactive decision-making of human operators, which would decrease the effect of events and recurring road congestion, might contribute to the easing of the strain of automation.The traffic control frameworks in India are now needed as it is an open-loop control framework, without any input or detection mechanism. Inductive loops and sensors employed in existing technology used to detect the number of passing vehicles. The way traffic lights are adapted is highly inefficient and costly in this existing technology. The aim was to build a traffic control framework by introducing a system for detection ,which gives an input to the existing system (closed loop control system) in order to adapt to the changing traffic density patterns and to provide the controller with a crucial indication for ongoing activities. By this technique, the improvement of the signals on street is extended and thus saves time by preventing traffic congestion. This study proposes an algorithm for real-time traffic signal control, depending on the traffic flow. In reality, the features of competitive traffic flow at the signposted road crossing are used by computer vision and by machine learning. This is done by the latest, real-time object identification, based on convolutional Neural Networks network called You Look Once (YOLO). Traffic signal phases are then improved by data acquired in order to allow more vehicles to pass safely over minimal wait times, particularly the line long and the time of waiting per vehicle.This adjustable traffic signal timer is used to calculate traffic density utilizing YOLO object identification using live pictures of cameras in intervals and adjusts the signal timers appropriately, therefore decreasing the road traffic congestion, ensuring speedier transit for persons, and reducing fuel consumption. The traffic conditions will improve enormously at a relatively modest cost. Inductive loops are a viable but costly approach. This method thereby cuts expenses and outcomes quickly.

scholarly journals Intelligent Computing and Control Framework for Smart Automated System

2022 ◽  
Vol 33 (1) ◽  
pp. 173-189
Author(s):  
R. Manikandan ◽  
G. Ranganathan ◽  
V. Bindhu
Keyword(s):  
Automated System ◽  
Intelligent Computing ◽  
Control Framework ◽  
And Control

The Effective Use of Airspace

1958 ◽  
Vol 11 (3) ◽  
pp. 259-265
Author(s):  
E. J. Dickie
Keyword(s):  
Control System ◽  
Traffic Control ◽  
Air Traffic Control ◽  
Air Traffic ◽  
Traffic Density ◽  
Traffic Control System ◽  
State Of Affairs ◽  
The One ◽  
Effective Use ◽  
And Control

Whenever the subject of air traffic control is discussed reference is made to what are described as ‘areas of high traffic density’. This is a misleading expression because the areas referred to are those in which the traffic density is high in relation to the capacity of the air traffic control system, not to the airspace itself. It is probably true to say that there are in fact only three areas where traffic density is high in relation to the volume of airspace. These are the arrival and departure paths at busy aerodromes and the area occupied by a number of aircraft flying in close formation. Elsewhere the traffic density is not such as to create congestion in the air. It is the traffic control system which becomes overloaded, not the airspace. In this paper an attempt is made to isolate some of the factors giving rise to this state of affairs and to discuss ways of achieving a better state of balance between airspace capacity on the one hand and control capacity on the other.

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