A new approach for transport network design and optimization

The solution of the transportation network optimization problem actually requires, in most cases, very intricate and powerful computer resources, so that it is not feasible to use classical algorithms. One promising way is to use stochastic search techniques. In this context, Genetic Algorithms (GAs) seem to be - among all the available methodologies- one of the most efficient methods able to approach transport network design and optimization. Particularly, this paper will focus the attention on the possibility of modelling and optimizing Public Bus Networks by means of GAs. In the proposed algorithm, the specific class of Cumulative GAs(CGAs) will be used for solving the first level of the network optimization problem, while a classical assignment model ,or alternatively a neural network approach ,will be adopted for the Fitness Function(FF) evaluation. CGAs will then be utilized in order to generate new populations of networks, which will be evaluated by means of a suitable software package. For each new solution some indicators will be calculated .A unique FF will be finally evaluated by means of a multicriteria method. Altough the research is still in a preliminary stage, the emerging first results concerning numerical cases show very good perspectives for this new approach. A test in real cases will also follow.