scholarly journals Ambulance detection using image processing and neural networks

2021 ◽  
Vol 2115 (1) ◽  
pp. 012036
Author(s):  
K Agrawal ◽  
M K Nigam ◽  
S Bhattacharya ◽  
G Sumathi
Keyword(s):  
Image Processing ◽  
Traffic Congestion ◽  
Tracking System ◽  
Emergency Vehicle ◽  
Traffic Light ◽  
The Road ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Pass Through ◽  
Physical Address

Abstract Ambulance Detection using Image Processing and Neural Network is a vehicle detection and tracking system, which recognizes the vehicle (i.e., Ambulance in this case) amidst the traffic congestion. Due to the fact from past few years, the range of vehicles usage of the road is growing each day that results in traffic congestion, for better management of this traffic this system is useful. Traffic Congestion, as mentioned above, can be observed at an ever-growing pace in countries like India and Thailand, where the roads’ width and length make it impossible to make a separate lane for the emergency vehicle (like that of ambulance); Hence making it hard for the vehicle to pass through the traffic at the earliest possible time. The Ambulance tracking system is activated at the mapped junctions and that program detects the ambulance coming close to it and turns the traffic light to Green for the next 15 seconds. Geocoding is the practice of transforming addresses (like a physical address) to location information (like longitude and latitude) that can be used to locate a label on a map or to mark a grid. They plan to provide ambulances with this software to make it easy to transform addresses into a programmable format for review and retrieval. This data is converted to a system that shows all the crossings it must pass to meet the endpoint.

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.

Vehicle Detection and Tracking in Complex Traffic Circumstances with Roadside LiDAR

2019 ◽  
Vol 2673 (9) ◽  
pp. 62-71 ◽  
Author(s):  
Zhenyao Zhang ◽  
Jianying Zheng ◽  
Hao Xu ◽  
Xiang Wang
Keyword(s):  
High Resolution ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Vehicle Detection ◽  
Traffic Data ◽  
The Road ◽  
Detection And Tracking ◽  
Area Of Interest ◽  
Vehicle Detection And Tracking ◽  
On The Road

The problem of traffic safety has become increasingly prominent owing to the increase in the number of cars. Traffic accidents often occur in an instant, which makes it necessary to obtain traffic data with high resolution. High-resolution micro traffic data (HRMTD) indicates that the spatial resolution reaches the centimeter level and that the temporal resolution reaches the millisecond level. The position, direction, speed, and acceleration of objects on the road can be extracted with HRMTD. In this paper, a LiDAR sensor was installed at the roadside for data collection. An adjacent-frame fusion method for vehicle detection and tracking in complex traffic circumstances is presented. Compared with the previous research, objects can be detected and tracked without object model extraction or a bounding box description. In addition, problems caused by occlusion can be improved using adjacent frames fusion in the vehicle detection and tracking algorithms in this paper. The data processing procedure are as follows: selection of area of interest, ground point removal, vehicle clustering, and vehicle tracking. The algorithm has been tested at different sites (in Reno and Suzhou), and the results demonstrate that the algorithm can perform well in both simple and complex application scenarios.

scholarly journals GSM And IoT Based Smart Traffic System With Geofencing

10.31645/16 ◽  
2020 ◽  
Author(s):  
Muhammad Fahees Ghouri ◽  
Keyword(s):  
Traffic Control ◽  
Traffic Congestion ◽  
Location Based Services ◽  
Daily Routine ◽  
Emergency Vehicle ◽  
Traffic System ◽  
The Road ◽  
The World ◽  
Cloud Computation ◽  
Smart Traffic

Abstract— This project is aimed at resolving severe traffic congestion in most cities across the world by using latest technologies. The world is heading towards IoT and shifting daily routine manual processes to automatic systems. Current traffic control system is based on fixed timer which becomes one of the main reasons of transport blockage. In order to overcome this problem, a framework has been designed to introduce the concept of smart traffic system which includes Internet of Things. The road side sensors attached to the Arduino Mega send information to the cloud using NodeMCU where decision is taken based on density, hence involving cloud computation to turn a particular signal green. Moreover, we have also dealt with emergency vehicles which bears the facility of turning signal green using either RFID system or GSM based mobile. Sound sensors are placed to confirm that the signal return to normal condition once the emergency vehicle has crossed the signal. Lastly, a geofencing based marketing app called “Brando” has been designed using android studio to provide location-based services from the nearest stations like shopping malls to the people on road on their respective mobile phones.

Smart Cities: Study and Comparison of Traffic Light Optimization in Modern Urban Areas Using Artificial Intelligence

2018 ◽  
Vol 8 (2) ◽  
pp. 10
Author(s):  
Mustapha Kabrane ◽  
Salah-ddine Krit ◽  
Lahoucine El Maimouni
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Road Network ◽  
Smart Cities ◽  
Optimization Techniques ◽  
Traffic Light ◽  
Large Cities ◽  
Traffic Lights ◽  
The Road ◽  
Road Signs

In large cities, the increasing number of vehicles private, society, merchandise, and public transport, has led to traffic congestion. Users spend much of their time in endless traffic congestion. To solve this problem, several solutions can be envisaged. The interest is focused on the  system of road signs: The use of a road infrastructure is controlled by a traffic light controller, so it is a matter of knowing how to make the best use of the controls of this system (traffic lights) so as to make traffic more fluid. The values of the commands computed by the controller are determined by an algorithm which is ultimately, only solves a mathematical model representing the problem to be solved. The objective is to make a study and then the comparison on the optimization techniques based on artificial intelligence1 to intelligently route vehicle traffic. These techniques make it possible to minimize a certain function expressing the congestion of the road network. It can be a function, the length of the queue at intersections, the average waiting time, also the total number of vehicles waiting at the intersection

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