ABSTRACT
This chapter reviews existing traffic models for Electric Vehicles with particular emphasis on an introduced Markov chain based model. It describes some applications based on this traffic model. It discusses number of traffic applications that can be planned on the basis of the proposed model and outlines interesting lines of research to further extend the work. A Markov chain is a discrete time stochastic process with a finite or countable number of states. The transition probabilities depend only on the state of the chain at the previous time step and not on the past history of the process. Graphs and Markov chains can be used quite naturally to model urban traffic networks. The starting point of the Markovian model describes the transitions between road segments. A simple strategy to regulate the Perron eigenvector of the chain is to influence the diagonal entries of the transition matrix via diagonal scaling.
