A combined algorithm for solving and calibrating the stochastic traffic assignment model

This paper studies Fisk’s stochastic user equilibrium assignment model in which travellers are assumed not to have perfect knowledge of the traffic system. It is shown that the stochastic properties of this model completely depend on the calibration parameter. After giving the searching method of an ‘efficient paths’ set, a modified combined algorithm for solving and calibrating Fisk’s stochastic traffic assignment model is developed. In this algorithm, the path enumeration is achieved automatically in the preliminary phase and renewed in the iterative processes, and the number of alternative paths generated can be controlled by a predetermined function. Two numerical examples are presented to illustrate the model’s properties and the algorithm’s efficiencies.