Fixed-time traffic signal optimization using a multi-objective evolutionary algorithm and microsimulation of urban networks

Abstract

Large cities have been facing serious problems in the management of traffic, owing to the increasing number of vehicles and pedestrians. Traffic engineering is essential in managing traffic and improving urban mobility. This paper deals with the problem of fixed-time signal programming on traffic networks. A new bi-objective optimization model is proposed to maximize the average and minimize the variance of the vehicle speeds in the network. Although the first function is commonly discussed in the literature, the second one is novel, and its aim is to provide flow balance along the network. This combination of functions is optimized by the Memory-Based Variable-Length Nondominated Sorting Genetic Algorithm 2 (MBVL-NSGA2), which avoids the revaluation of candidate solutions. This approach was validated through experiments using the microscopic simulator GISSIM, in a multi-intersection real network, using measured data from Belo Horizonte traffic engineering company (BHTRANS). The practical results of MBVL-NSGA2 were compared with four approaches: (1) current BHTRANS solutions; (2) a genetic algorithm optimizing the first function; (3) a genetic algorithm optimizing the second function, and; (4) the traditional NSGA2. Analysis showed that this proposal is able to generate better traffic signal plans, at the same time that it generates a diversified set of efficient candidate solutions.

A Coordination Algorithm for Signalized Multi-Intersection to Maximize Green Wave Band in V2X Network

“…To assess the performance of the proposed MICA-V, we made comparisons with the fixed-time control based on the genetic algorithm (FTC-GA) [9] and a cooperative deep Qnetwork (QT-CDQN) for multi-intersections signal control [18]. According to simulation circumstances, we adopted the following metrics to evaluate the performance of the mechanism.…”

“…Traffic lights, first used in 1928 [7], are still the most extensive application of traffic management systems by now [8][9][10][11]. One of the ways to manage traffic flow at urban intersections is to provide traffic light signal optimization.…”

“…One of the ways to manage traffic flow at urban intersections is to provide traffic light signal optimization. In [9], C Costa et al proposes a bi-objective optimization of fixed-time traffic signals using an improved genetic algorithm to improve the performance of vehicle speed. Kohler and Strehler [8,10] have developed a model that optimizes the fixed-time signal plan in a cyclically expanded network.…”

Multi-objective optimization of urban road intersection signal timing based on particle swarm optimization algorithm

“…In addition, multi-objective optimization problem for intersection signal timing can regard as NP-complete. 25 This makes it a complex combinatorial optimization problem. Thus, multiobjective optimization problem for intersection signal timing is more challenging.…”

Optimisation of Signal Timings in a Road Network

“…One practical way to make a given transportation network more efficient is to apply a suitable signal control strategy. As an efficient approach for enhancing the capacity of current transportation networks, optimisation of signal timing parameters of traffic lights, such as cycle time [10,9,18], green time [4,16,29], offset time [2,18,16], and phase sequence [22,15,17], has received a great deal of attention in studies of transportation networks. From an optimisation perspective, the problem of optimum signal timings of a transportation network is highly nonlinear and non-convex, in which the modification of a timing plan of a given traffic light can significantly affect the traffic flow in the other areas of the network.…”