In this paper we propose an Ant Colony Optimisation (ACO) algorithm for optimising the signal settings on urban networks following a local approach. This problem, also known as LOSS (Local Optimisation of Signal Settings), has been widely studied in the literature and can be formulated as an asymmetric assignment problem (Cascetta et al., 2006). The problem consists in optimising the signal settings of each intersection of an urban network as a function only of traffic flows at the accesses to the same intersection, taking account of the effects of signal settings on costs and on user route choices. The proposed ACO algorithm is based on two kinds of behaviour of artificial ants which allow the LOSS problem to be solved: traditional behaviour based on the response to pheromones for simulating user route choice, and innovative behaviour based on the pressure of an ant stream for solving the signal setting definition problem. Our results on a real-scale network show that the proposed approach allows the solution to be obtained in less time but with the same accuracy as in traditional MSA approaches.

An Ant Colony Optimisation (ACO) algorithm for solving the Local Optimisation of Signal Settings (LOSS) problem on real-scale networks

GALLO M;
2010

Abstract

In this paper we propose an Ant Colony Optimisation (ACO) algorithm for optimising the signal settings on urban networks following a local approach. This problem, also known as LOSS (Local Optimisation of Signal Settings), has been widely studied in the literature and can be formulated as an asymmetric assignment problem (Cascetta et al., 2006). The problem consists in optimising the signal settings of each intersection of an urban network as a function only of traffic flows at the accesses to the same intersection, taking account of the effects of signal settings on costs and on user route choices. The proposed ACO algorithm is based on two kinds of behaviour of artificial ants which allow the LOSS problem to be solved: traditional behaviour based on the response to pheromones for simulating user route choice, and innovative behaviour based on the pressure of an ant stream for solving the signal setting definition problem. Our results on a real-scale network show that the proposed approach allows the solution to be obtained in less time but with the same accuracy as in traditional MSA approaches.
978-989-96986-1-1
Ant Colony Optimisation; signal settings; stochastic assignment
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12070/12228
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