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Paper details
Number 4 - December 2017
Volume 27 - 2017
A robust algorithm to solve the signal setting problem considering different traffic assignment approaches
Ludovica Adacher, Andrea Gemma
Abstract
In this paper we extend a stochastic discrete optimization algorithm so as to tackle the signal setting problem. Signalized
junctions represent critical points of an urban transportation network, and the efficiency of their traffic signal setting
influences the overall network performance. Since road congestion usually takes place at or close to junction areas, an improvement in signal settings contributes to improving travel times, drivers’ comfort, fuel consumption efficiency, pollution
and safety. In a traffic network, the signal control strategy affects the travel time on the roads and influences drivers’ route
choice behavior. The paper presents an algorithm for signal setting optimization of signalized junctions in a congested road
network. The objective function used in this work is a weighted sum of delays caused by the signalized intersections. We
propose an iterative procedure to solve the problem by alternately updating signal settings based on fixed flows and traffic
assignment based on fixed signal settings. To show the robustness of our method, we consider two different assignment
methods: one based on user equilibrium assignment, well established in the literature as well as in practice, and the other
based on a platoon simulation model with vehicular flow propagation and spill-back. Our optimization algorithm is also
compared with others well known in the literature for this problem. The surrogate method (SM), particle swarm optimization
(PSO) and the genetic algorithm (GA) are compared for a combined problem of global optimization of signal settings
and traffic assignment (GOSSTA). Numerical experiments on a real test network are reported.
Keywords
genetic algorithms, surrogate method, traffic signal synchronization, traffic assignment, simulation