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

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.

Analysing urban traffic volumes and mapping noise emissions in Rome (Italy) in the context of containment measures for the COVID-19 disease

“…Among these, 322 links represent the Grande Raccordo Anulare (GRA), which is a ring-shaped, 68.2 km long freeway that encircles Rome [30]. Further developments will deal with the analysis of the evolution over time through the use of a quasi-dynamic simulation [31], whose input are the same used in a static assignment, and with the study along urban arteries affected by traffic signals [32]. The simulations run on an Intel Xeon E5-2640 v3(2.60 GHz) processor and all the three scenarios converged (user equilibrium), on average, in 3-5 min.…”

Performance Analysis of Decentralized VS Centralized Control for the Traffic Signal Synchronization Problem

“…In addition, also being the simulation computationally expensive, the extensive exploration of the entire solution domain would imply unacceptable calculation time [33]. To avoid these problems, the Surrogate Method is introduced, first for manufacturing problem and then also for transportation problem [1,9,34]. is method combines the advantages of stochastic approximation type of algorithm with the ability to obtain sensitivity estimates.…”

“…In previous studies, it is demonstrated the capacity of the Surrogate Method (SM) to find central optimal solution to problems concerning signal setting and combined signal setting, due to its ability to jump out of local minimum. Hence, it is showed the efficacy and the efficiency of the SM with respect to the Projected Gradient Algorithm (PGA), the Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA) (see [9,35]). Given the complexity of this problem, a spatial decomposition method is introduced.…”

“…end if (14) end for ALGORITHM 3: Dynamic gradient step estimation. algorithm is { (1,2), (7,8,9),( 4), (3,5,6) }, in bold the main subnetwork and the nodes with the highest Priority in each subnetwork).…”

“…At the same time, for all intersections, they optimize various signal timing parameters (i.e., cycle length, green times, and offsets). However, network signal timing optimization is an NP-hard problem and a central optimization technique will not be scalable and applicable to large transportation networks [1,[9][10][11][12].…”