Congestion Trajectories Using Fuzzy Gaussian Travel Time Based on Mesoscopic and Cellular Automata Traffic Model

In the literature on engineering and traffic theory, numerous measures have been proposed to represent the state of traffic conditions urban networks sections. Again, the classical approach distinguishes two approaches to describe traffic congestion. One is based on the correlation between traffic supply and demand. The other is supported by a quality of service measures. In both cases, the congestion measures are influenced by uncertainty due to the imprecision of the measurement, the road user’s perception and the data variation depending on the weather, etc. To take the uncertainty into account, a process for generating fuzzy Gaussian variables has been proposed. This process requires the average speed of movement on a link, the speed of free traffic flow and the dispersion of speed measurements on the road. In this work we design a travel time model by the Gaussian fuzzy numbers. We propose modeling of the traffic in urban network. The generating ideas are based on the integration of mesoscopic modeling taking into account macroscopic variables and microscopic traffic behaviors by means of cellular automata. We develop a simple model on links that we generalize on networks. We then derive two processes to compute travel time on a link. Thereafter, the travel time on a segment is modeled by a Gaussian fuzzy variable. Developments resulting from this work contribute to generating the fuzzy congestion trajectories and to simulate congestions propagation in urban network and specially to analyze travel time in a smart city.