An intelligent transportation system for future smart world

Traffic congestions are a norm in cosmopolitan cities. This is due to the rapid increase of vehicles. It is crucial that the authorities are able to monitor different traffic conditions to reduce the congestions. Hence, intelligent transportation is needed to be developed for this purpose. This is the objective of this project. Moreover, this project is hoped to be one of the critical studies and analysis on the intelligent transportation monitoring system using artificial intelligence. This intelligent monitoring system should be able to be deployed at any location to monitor the traffic situations. Artificial intelligence is adopted as it can be used easily and better. This is due to the advancement in computing power, as it can detect vehicles under different lighting conditions when compared to conventional image processing techniques such as edge detection. Videos of six different traffic environmental conditions were captured and fed into ten different neural network algorithms coded using Python Programming Language. These videos were used to compare the performance of different algorithms under different environmental conditions. It is proven that YOLOv3 is the best performing algorithm after a comparative analysis. It is known to be able to operate with real time videos under different lightings. It can be used by the authorities to deploy along the expressways and major roads to study the traffic conditions.Master of Science (Signal Processing