Freeway traffic density control based on improved particle swarm optimization algorithm

A cell transmission model (CTM) and an improved particle swarm algorithm are employed for density control in freeway mainlines. Firstly, a CTM is built, which can accurately describe the actual status of freeway traffic flow. Secondly, the principle and algorithm of an improved particle swarm optimization (PSO) are formulated with details. Thirdly, freeway density controllers based on the improved PSO algorithm and the nonlinear process control technique are designed. This improved PSO algorithm is applied to optimize the proportional and integral (PI) parameters. The searching ability for finding the optimal PI parameters is enhanced by means of varying the inertia weight value and using simulated annealing algorithm at the same time. Finally, two simulation experiments are carried out to show the superiority of this approach. Compared to the genetic algorithm or the standard PSO algorithm, the improved PSO algorithm has smaller density errors. The optimization algorithm, as well as the nonlinear process control technique, provides a new way for freeway density control.