Abstract:
The increasing population of big cities and hence the increasing rate of vehicle use with the population bring important environmental and economic problems. Traf- c congestion is one of the main causes of these problems. The presence of factors that may cause tra c to slow down or even stop locally increases the density of tra c, especially in highly populated cities, and the e ect of these factors can cease to be local and a ect the entire road network. Therefore, the e ective management of tra c plays an essential role in reducing these negative e ects. In this thesis, the real-time management using the connected autonomous vehicles, namely SWSCAV, [1] was tested in the 11 km long road network using the SUMO (Simulation of Urban Mobility) environment. Then, SWSCAV [1] with and without the prediction was compared with two real-time tra c management methods, namely the Variable Speed Limits and Lane Control Systems. 2400 di erent scenarios were created changing the parameters: the control distance and the percentage of the connected autonomous vehicles in the tra c ow. SWSCAV [1] with prediction where there are 50% connected autonomous vehicles decreased the density by an average of 58.18%. This scenario provided a 61.61% decrease in the density locally with a control distance of 1250 meters.
