With the ever increasing number of vehicles on the road , traditional methods of traffic signals fail to serve the need to avoid traffic congestion and effective flow. Thus , our project aims at providing a solution to this exponential problem.
With the help of image processing , machine learning , IOT and cloud computing , we were able to come up with a unique solution.
ReciverArdu.py :- For NRF (long range communication) b/w the signal and Ambulance .
index.php :- map gui interface showing live images of traffic junction with predicted traffic densities
pi.sh :- takes the image of the traffic and sends it to cloud server (PESU in this case) and gets processed data from server , sends it to pi for sequencing of signal
ser.sh :- takes the image from pi , detects the density of vehicles , process the image , send signal sequence to pi based on density of traffic
traffic_light.ino :- the sequence we plan to use for normal operation , yet to design dynamic operation signal sequencing
Pi1.py :- Meant for the dynamic operation of the signal based on vehichle density
seq.py :- Finds the Maximum Density of the 4 roads at that signal junction.
