Authors: Mr. Sunil S. Sonawane, Bhagyashree Kenchannvar, Kajal Kumbhar, Pranjali Salunkhe
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Nowadays, holdup has become one in every of the foremost critical problem thanks to increasing population and automobiles in cities. Hold up causes delay and stress for drivers and increases pollution and carbonic acid gas emissions. The traffic controller is one in all the critical factors affecting the traffic flow. This paper proposes a control system supported image processing including video processing within which traffic signals change accordingly the density of traffic and it will make use of Arduino UNO board for Traffic Lights, Emergency Vehicles and Barrier. A video camera and traffic lights are interfaced with Arduino UNO. The video is processed and Arduino enables the traffic lights to vary when required. Along with this, barrier at zebra crossing and emergency vehicle passing are the best concept for today’s smart city. Keywords: Image Processing, Time, Signals, Emergency Vehicles, Barrier.
One of the most important problems in India is Traffic. Most countries have automobiles, buses, trucks, motor vehicles, motors, scooters and bicycles. However, in India, additionally to the current routine urban transportation, and contributing substantially to the congestion, are networks of auto-rickshaw, two wheelers still as heavy vehicles. This has led to the explosion of traffic, higher number of accidents, deaths and increase in commuting time over the years.
If there is an accident in India, people block the roads as they need and begin fighting, taking law into their own hands. This ends up in a roadblock and makes it very difficult for ambulance to succeed in the spot, sometimes even for hours. Nowadays, the traffic in India is controlled by Traffic signals and secondly by Policeman. These two methods are most effectively but now as India is using smart technologies this two methods may be switch to Automatic light Controlling System. Our Project mostly works on reducing the waiting time for empty road. For doing this, Arduino is employed by capturing a video of every lane. Number of vehicles present on road and traffic density are calculated by applying appropriate Arduino functions. Therefore, the timing for Green Light is set supported the density. The road that has more vehicles, longer is allocated for those roads. It is not time dependent. Time is allocated as per the traffic of vehicles. Just in case if vehicle having Red Signal tries to interrupt the rule and passes the lane then the Barrier is opened and therefore the vehicle stops. This project also works for emergency vehicles.
II. PROBLEM STATEMENT
Conventional traffic controller uses pre-defined time to manage the duration of signal in one particular direction or in some places human physically must do the task. While their system is somehow convenient but isn’t efficient and, in some cases, costly since large human forces is required to take care of traffic rules similarly as tie up control. We purpose system that is able to tackle above stated problems using Image Processing.
III. LITERATURE SURVEY
IV. CONVENTIONAL TRAFFIC CONTROL SYSTEM
A. Manual Controlling
During this type of traffic management Manpower is included. Policeman/men is standing at each cross section and controls the traffic by using different signs.
B. Drawbacks of Conventional System
This method requires an outsized number of Manpower. It also uses a timer for every phase, which is fixed and does not adopt in keeping with the real-time traffic on it road. Due to that, the control signals may end in a re-entrant collision of vehicles and it should cause delay in quick movement of traffic.
C. Automatic Controlling
This can be most suited method nowadays because it reduces Manpower. During this variety of method, the time is allocated as per the amount of vehicles present within the lane. Less number of vehicles has less number of your time. This method can even identify emergency vehicles, fire brigade vehicles also as VIP cars, etc. and in keeping with that, the signal will change.
A. Real time traffic switching in line with the emergency vehicle.
B. Decreases Manpower.
C. Reduces Accident.
D. Number of violators will be reduced.
The problem with the timer based traffic system is that it will allocate a hard and fast time to any or all the lanes. Therefore, if the actual lane has no traffic or dense traffic will have the quadruple time (as 120/60 sec). This might make people waiting at other roads intolerant and that they tend to maneuver even they are having red signal. This might cause accident. Similarly, a road with high amount of traffic would require more green-signal time for the vehicles to clear, which is not available. This is often leading to confusion and accidents. A possible solution to the current is density based Smart light Control.
This technology works supported the density of traffic near the stoplight. A webcam has been accustomed capture the live video of the road. The camera is connected to the Arduino board. Allow us to think about road junction containing 4 lanes as shown in fig 3.
As considering above fig 3. Allow us to contemplate the lane 2 (L2) has dense traffic as compared to other three lanes. So, the image are captured for lane 2 by the camera and in line with the vehicles captured within the image using Fast R-CNN Technique of image processing the time (40 sec) are visiting be allocated to lane 2. After passing vehicles from lane 2 the signal will switch to Red Signal and Barrier are get opened for lane 2, lane 3 and lane 1.
More Density of Traffic for Lane = longer allocated for Green Signal.
There are certain special cases that arises during this proposed system, they are:
The study showed that image processing is a better technique to control the state change of the traffic light. It shows that it can reduce the traffic congestion and avoids the time being wasted by a green light on an empty road. It is also more consistent in detecting vehicle presence because it uses actual traffic images. It visualizes the reality so it functions much better than those systems that rely on the detection of the vehicles’ metal content. Overall, the system is good but it still needs improvement to achieve a hundred percent accuracy.
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Copyright © 2022 Mr. Sunil S. Sonawane, Bhagyashree Kenchannvar, Kajal Kumbhar, Pranjali Salunkhe. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.