scholarly journals A Review on Minimization of Ambulance Response Time Using Image Processing and Critical path Mapping Based on Traffic Con-trol

2021 ◽  
Vol 2 (2) ◽  
pp. 1-7
Author(s):  
Shuja Rafiq ◽  
Keyword(s):  
Image Processing ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Critical Path ◽  
Machine Learning Techniques ◽  
Emergency Vehicle ◽  
Real Time Traffic ◽  
Traffic Management System ◽  
Process Work ◽  
Economic Developments

India is a developing country; the population of India is growing exponentially. India ranks 2nd in the world in terms of population. As there will be a gradual increase in population there will be an increase in the number of vehicles, as a result of which traffic congestion is increasing and as a result, emergency vehicles such as ambulances, fire-fighters, etc. are having difficulty getting to their destination on time. Vehicle use is growing rapidly due to recent technological and economic developments, and at the same time, the lack of infrastructure against demand is leading to an increase in the number of accidents and fatalities. Minor problems in our health system have prompted us to come up with a petition to make this process work and save lives. Through book reviews and reflections, I have proposed a project in a smart traffic management system using image processing. The aim of this project is to improve simulation to determine traffic congestion, to detect a crash/accident, and to obtain an ambulance using image processing and machine learning techniques. The proposed independent work is simulated in the form of an experimental setup using Arduino and LED displays that mimic real-time traffic. These simulation results reflect the terms of the acquisition as it provides an emergency vehicle pass to catch up on peak hours.

scholarly journals Design and Development of Traffic Control System using Image Processing

2021 ◽  
Vol 9 (VI) ◽  
pp. 3247-3252
Author(s):  
G. Kalyan
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Red Light ◽  
Traffic Density ◽  
The Road ◽  
Real Time Traffic ◽  
On The Road

Traffic congestion is now a big issue. Although it seems to penetrate throughout the world, urban towns are the ones which are most effected. And it is expanding in nature that it is necessary to understand the density of roads in real time to better regulate signals and efficient management of transport. Various traffic congestions, such as limited capacity, unrestricted demand, huge Red Light waits might occur. While insufficient capacity and unlimited demand are somehow interconnected, their delay in lighting is difficult to encode and not traffic dependant. The necessity to simulate and optimise traffic controls therefore arises in order to better meet this growing demand. The traffic management of information, ramp metering, and updates in real-time has been frequently used in recent years for image processing and monitoring systems. An image processing can also be used for the traffic density estimation. This research describes the approach for the computation of real-time traffic density by image processing for using live picture feed from cameras. It focuses also on the algorithm for the transmission of traffic signals on the road according to the density of vehicles and therefore aims to reduce road congestion, which reduces the number of accidents.

Dynamic Routing of Emergency Vehicles with Traffic Management System and Implementation on CupCarbon for Smart Cities

2021 ◽  
Vol 9 (11) ◽  
pp. 1407-1412
Keyword(s):  
Traffic Congestion ◽  
Traffic Management ◽  
Management System ◽  
Smart Cities ◽  
Maximum Speed ◽  
Emergency Vehicle ◽  
Traffic Light ◽  
Emergency Vehicles ◽  
Traffic Management System ◽  
On The Road

The traffic congestion is one of the major problems in crowded cities, which causes people to spend hours on the road. In traffic congestion situations, finding alternate routes for emergency vehicles, which provides shortest travel time to nearby hospital is critically life-saving issue. In this paper, we propose a traffic management system and an algorithm for routing of an emergency vehicle. The algorithm uses distance between source and destination, maximum vehicle count, maximum speed, average speed, traffic light conditions on the roads, which are assumed to support vehicle-to-infrastructure (V2I) communication in 5G IoT network. Simulations are performed on CupCarbon IoT simulator platform for various test scenarios. The performance of the proposed emergency vehicle routing algorithm is compared against well known Link State algorithm. And, the results demonstrate the effectiveness of the proposed method.

scholarly journals Enhanced Traffic Management System using Artificial Intelligent Congestion Control

2020 ◽  
Vol 9 (8) ◽  
pp. 1015-1018
Keyword(s):  
Urban Communities ◽  
Traffic Congestion ◽  
Smart City ◽  
Traffic Management ◽  
Traffic Information ◽  
Real Time Traffic ◽  
Suitable Site ◽  
Traffic Management System ◽  
Online Traffic ◽  
The Internet Of Things

The smart city proposed by government is providing better infrastructure with possible automated device. Every smart city proposes to provide smart transport through automated traffic management .The peak hours face the congestion road and many traffic irregularities. The congested road aids in poor Travel experience, environmental pollution and health hazards by vehicular fuel. The solution to aforesaid issues leads to traffic Automation in urban communities. To implement the traffic automation need access to real time traffic congestion information, best possible route and alternate strategy with online traffic information applicable to specific traffic stream. An more suitable site visitors manipulate and MF has been mentioned to finish short information transmission and their corresponding motion performed via artificial intelligence. The VANET scenario, congestion manage algorithm executed through mobile agent controller uniformly organizes the traffic glide by way of heading off the congestion at the smart visitors zone ,The law-enforcement bodies ,the fire opponents and the clinical and/or paramedical teams consciousness on elevated quantity of crime in addition to lifestyles losses through site visitors irregularities. The benefits of adopting the internet of things(iot)provide a new prospect for intelligent site visitors improvement.

scholarly journals Efficient Traffic Management System

2018 ◽  
Vol 7 (3.12) ◽  
pp. 926
Author(s):  
Suraj Kumar G Shukla ◽  
Aadithya Kandeth ◽  
D Sai Santhiya ◽  
Kayalvizhi Jayavel
Keyword(s):  
Image Processing ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Management System ◽  
Large Scale ◽  
Traffic Signals ◽  
Input Image ◽  
Traffic Density ◽  
Use Of Technology ◽  
Traffic Management System

Traffic Management is a big issue which impacts us almost daily. Use of technology such as IoT and image processing can lead to a smooth traffic management system. The common reason for traffic congestion is due to the lack of an efficient traffic prioritization system. The internet of things is a network of devices. The embedded systems includes sensors, actuators, and electronics.With software and connectivity locally or over internet  helps in  transfer of data. Each of these devices is uniquely identifiable in the network and are highly interoperable. Image processing using OpenCV is a technique that is used to process an input image and obtain the traffic densities along various lanes in a junction. Existing traffic management solutions include using RFID tags on vehicles to obtain a vehicle count. This can also be done using ultrasonic sensors. The problem with these methods is that when implemented in a large scale, the cost of the entire system can be exponentially higher than an image processing approach as each vehicle will have to be fitted with an RFID tag. Hence, implementing this model at a largescale level, for example in a metropolitan city will be time consuming and expensive. This has led to the development of our algorithm, which uses image processing and IoT along with CCTV cameras. This system is efficient, as it uses CCTV cameras that are already present in traffic signals of most major metropolitan cities. Hence implementing the system at a largescale level is feasible. This algorithm also takes care of corner cases like heavy utility vehicles and motorbikes. It can also be used at night and during unfavorable weather conditions.The algorithm used detects the density of traffic as opposed to the count of vehicles by taking input images from a CCTV camera, comparing it with a sample image of an empty road and obtaining a match percentage. The traffic density can be found easily using this since it has a disproportionate relation with the match percentage. The traffic signals can be altered accordingly using the traffic density. The output is then sent to the ThingSpeak cloud where it can be analyzed.

scholarly journals Image Processing Based Intelligent Traffic Controller

2012 ◽  
pp. 13-17
Author(s):  
Vikramaditya Dangi ◽  
Amol Parab ◽  
Kshitij Pawar ◽  
S.S Rathod
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Traffic Management ◽  
Detection System ◽  
Emergency Vehicle ◽  
Pollution Levels ◽  
Real Time Image ◽  
Wide Scale ◽  
Traffic Management System ◽  
Time Image

The frequent traffic jams at major junctions call for an efficient traffic management system in place. The resulting wastage of time and increase in pollution levels can be eliminated on a city-wide scale by these systems. The paper proposes to implement an intelligent traffic controller using real time image processing. The image sequences from a camera are analyzed using various edge detection and object counting methods to obtain the most efficient technique. Subsequently, the number of vehicles at the intersection is evaluated and traffic is efficiently managed. The paper also proposes to implement a real-time emergency vehicle detection system. In case an emergency vehicle is detected, the lane is given priority over all the others.

scholarly journals Mobile Road Traffic Management System Using Weighted Sensor

2017 ◽  
Vol 11 (5) ◽  
pp. 147 ◽  
Author(s):  
Solomon Adegbenro Akinboro ◽  
Johnson A Adeyiga ◽  
Adebayo Omotosho ◽  
Akinwale O Akinwumi
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Urban Areas ◽  
Traffic Management ◽  
Management System ◽  
Road Traffic ◽  
Traffic Information ◽  
Traffic Light ◽  
Real Time Traffic ◽  
Traffic Management System

<p><strong>Vehicular traffic is continuously increasing around the world, especially in urban areas, and the resulting congestion ha</strong><strong>s</strong><strong> be</strong><strong>come</strong><strong> a major concern to automobile users. The popular static electric traffic light controlling system can no longer sufficiently manage the traffic volume in large cities where real time traffic control is paramount to deciding best route. The proposed mobile traffic management system provides users with traffic information on congested roads using weighted sensors. A prototype of the system was implemented using Java SE Development Kit 8 and Google map. The model </strong><strong>was</strong><strong> simulated and the performance was </strong><strong>assessed</strong><strong> using response time, delay and throughput. Results showed that</strong><strong>,</strong><strong> mobile devices are capable of assisting road users’ in faster decision making by providing real-time traffic information and recommending alternative routes.</strong></p>

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 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.

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