The increasing deployment of fast charging infrastructure is coupling the operation of power and transportation systems. However, how to evaluate the coupling relationship between these two systems is less studied. This study proposes a look-ahead decentralised framework to solve the integrated power-traffic flow problem using the optimality condition decomposition (OCD) technique. By exploring the similarity between the iterative procedure of the algorithm and the interactive decision-making process of the systems, this study adds two original contributions to this literature. First, the authors show that the convergence performance of the OCD algorithms is closely related to the interdependency between the power and transportation systems. Numerical oscillations or failure of convergence could occur in highly interdependent systems. This can be used as a metric for assessing whether the coordinated operation of the two systems is essential. Second, they employ a dynamic multiplier-based OCD algorithm to solve the integrated flow problem in a decentralised manner with improved convergence performance. Their proposed algorithm considers the sensitivity of the electricity prices to the charging demand, which reduces the price fluctuations at some congested electrical nodes to enhance the convergence speed. The case study demonstrates factors that influence the coupling relationship between the power and transportation systems.