Author(s)

L. D’Acierno, M. Gallo & B. Montella

Abstract

In this paper, we propose an ACO-based algorithm that can be used to simulate mass-transit networks; this algorithm imitates the behaviour of public transport users. In particular, we show that the proposed algorithm, which is an extension of that proposed by D’Acierno et al. (A stochastic traffic assignment algorithm based on Ant Colony Optimisation, Lecture Notes in Computer Science 4150, pp. 25–36, 2006), allows mass-transit systems to be simulated in less time but with the same accuracy compared with traditional assignment algorithms. Finally, we state theoretically the perfect equivalence in terms of hyperpath choice behaviour between artificial ants (simulated with the proposed algorithm) and mass-transit users (simulated with traditional assignment algorithms). Keywords: Ant Colony Optimisation, traffic assignment models, hyper-path approach, mass-transit system simulation. 1 Introduction In analyses of real dimension networks, simulation models need to provide rapid solutions so that a large number of alternative projects may be explored or consequences of a strategy in terms of future (minutes or hours) network conditions may be simulated beforehand. Hence, in this paper we verify the possibility of developing a meta-heuristic algorithm that allows user flows on the mass-transit system to be calculated more quickly than by using traditional algorithms. In particular, we steered our research into ant-based algorithms. Such algorithms, based on the food source search of ant colonies, have in many cases

Keywords

Ant Colony Optimisation, traffic assignment models, hyper-path approach, mass-transit system simulation