<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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.</p></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.

A robust algorithm to solve the signal setting problem considering different traffic assignment approaches

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

In this paper we extend a stochastic discrete optimization algorithm so as to tackle the signal setting problem. Signalized junctions represent critical points...

A robust algorithm to solve the signal setting problem considering different traffic assignment approaches

Online veröffentlicht: 13. Jan. 2018

Seitenbereich: 815 - 826

Eingereicht: 06. Okt. 2016

Akzeptiert: 04. Mai 2017

DOI: https://doi.org/10.1515/amcs-2017-0057

Schlüsselwörter
genetic algorithms, surrogate method, traffic signal synchronization, traffic assignment, simulation

© by Ludovica Adacher

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

A robust algorithm to solve the signal setting problem considering different traffic assignment approaches

Online veröffentlicht: 13. Jan. 2018

Seitenbereich: 815 - 826

Eingereicht: 06. Okt. 2016

Akzeptiert: 04. Mai 2017

DOI: https://doi.org/10.1515/amcs-2017-0057

Schlüsselwörtergenetic algorithms, surrogate method, traffic signal synchronization, traffic assignment, simulation

© by Ludovica Adacher

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
genetic algorithms, surrogate method, traffic signal synchronization, traffic assignment, simulation