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. (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).
Ant Colony Optimisation approaches for the transportation assignment problem / D'Acierno, Luca; Gallo, M.; Montella, Bruno. - 111:(2010), pp. 37-48. (Intervento presentato al convegno 16th International Conference on Urban Transport and the Environment tenutosi a Limassol, Cyprus nel May 2010) [10.2495/UT100041].
Ant Colony Optimisation approaches for the transportation assignment problem
D'ACIERNO, LUCA;MONTELLA, BRUNO
2010
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. (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).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
