Development of intelligent transportation system for improving average moving and waiting time with artificial intelligence

Abstract

Background: Real time traffic control is an important tool of Intelligent Transportation System (ITS). The development of system for controlling the urban traffic dynamically provides not only the safety for traffic, but also saves the time, money and provides polluted free environment. This paper describes the development of dynamic and robust traffic management system based on fuzzy logic approach. Method: Knowledge based system have been extensively adopted as approach for real time decision making system. Findings: As the conventional dynamic controllers were used sensors which are having certain limitations, so these limitations can be overcome by vision sensors i.e. camera. Also image and vision computing plays an important role in monitoring and measuring the traffic density on road. Problems were identified with the current traffic control system at the intersection on road and this necessitated the design and implementation of a new system to solve the congestion problems. Improvements: The performance of the proposed framework is evaluated with LabVIEW and MATLAB test bed. The results of extensive simulations using the proposed approach indicate that the system improves the average moving time and decrease the average waiting time than the controllers with conventional sensors.