Abstract:
Rising traffic congestion has turned into a certain
issue as the number of vehicles on roads are increasing. This
research study was conducted to develop ‘Google Map and
Camera Based Fuzzified Adaptive Networked Traffic Light
Handling Model’. The main road with six major junctions was
selected as the target route for the project. During this study, we
were able to plan a limit and control traffic congestion utilizing
two neural networks which process together to provide an
efficient, productive and optimized solution based on real-time
situations. Real-time video streams and Google Map traffic layer
were used as primary input sources to the system. The Main
algorithm was used to reduce traffic at a specific point whereas
secondary algorithm was used to produce optimum decisions for
the overall network. As a further advancement, REST endpoint
was implemented to get the best route considering all the
accessible data. With the aid of the previously mentioned
techniques, an optimal traffic management model was developed.
Citation:
A. Nirmani, L. Thilakarathne, A. Wickramasinghe, S. Senanayake and P. S. Haddela, "Google Map and Camera Based Fuzzified Adaptive Networked Traffic Light Handling Model," 2018 3rd International Conference on Information Technology Research (ICITR), 2018, pp. 1-6, doi: 10.1109/ICITR.2018.8736158.