Traffic flow is considered as a stochastic process in road traffic modelling. Computer simulation is a widely used tool to represent traffic system in engineering applications. The increased traffic congestion in urban areas and their impacts require more efficient controls and management. While the effectivenesses of control schemes highly depend on accurate traffic model and appropriate control settings, optimization techniques play a central role for determining the control parameters in the planning and management applications. However, there is still a lack of research effort on scientific computing frameworks for optimizing traffic control and operations and facilitating real planning and management applications. To this end, the present study proposes a model-based optimization framework to integrate essential components for solving road traffic control problems in general. In particular, the framework is based on traffic simulation models, while the solution needs extensive computation during the engineering optimization process. In this study, an advanced genetic algorithm, extended by an external archive for storing globally elite genes, governs the computing framework, and it shows superior performance than the ordinary genetic algorithm because of the reduced number of fitness function evaluations in engineering applications. To evaluate the optimization algorithm and validate the whole software framework, this paper also illustrates a detailed case study for optimization of traffic light controls. The study optimizes a simple road network of two intersections in Stockholm to demonstrate the modelbased optimization processes as well as evaluate algorithm and software performance.
展开▼
