Comparing Ant Colony Optimization and Genetic Algorithm Approaches for Solving Traffic Signal Coordination under Oversaturation Conditions

Abstract

This article proposes to solve the oversaturated network traffic signal coordination problem using the Ant Colony Optimization (ACO) algorithm. The traffic networks used are discrete time models which use green times at all the intersections throughout the considered period of oversaturation as the decision variables. The ACO algorithm finds intelligent timing plans which take care of dissipation of queues and removal of blockages as opposed to the sole cost minimization usually performed for undersaturation conditions. Two scenarios are considered and results are rigorously compared with solutions obtained using the genetic algorithm (GA), traditionally employed to solve oversaturated conditions. ACO is shown to be consistently more effective for a larger number of trials and to provide more reliable solutions. Further, as a master-slave parallelism is possible for the nature of ACO algorithm, its implementation is suggested to reduce the overall execution time allowing the opportunity to solve real-time signal control systems.

Modeling and simulating traffic congestion propagation in connected vehicles driven by temporal and spatial preference

“…The traffic light also plays an important role in the effective operation of the traffic network. But an improper timed signal may lead to the traffic congestion [15] and thus seriously affects the performance of the entire traffic network. Recently, the new emerging paradigm of decentralized congestion control (DCC) [16] is put forward to solve and alleviate the traffic congestion in connected vehicles, and one of the key factors in such algorithm is the transmission power [17].…”

Design Task Modeling and Task Allocation Method Research Based Multi-granularity Space in Distributed Network Environment

“…In order to avoid the local optimal, crossover and mutation operation [17] have to be carried out to the components of solution after pheromone updating to achieve the global optimal.…”

Design and Evaluation of a Mathematical Optimization Model for Traffic Signal Plan Transition Based on Social Cost Function

“…However, recently it has been applied to combinatorial optimization problems such as distribution Journal of Advanced Transportation 5 scheduling problem, vehicle routing problem, and railroad blocking problem, among other applications [34][35][36][37][38]. ACO has been applied to transportation systems optimization, but these works have been oriented to find optimal signal setting in function of traffic demand [39][40][41].…”