Freeway ramp metering based on PSO-PID control

Abstract

Ramp metering on freeway is one of the effective methods to alleviate traffic congestion. This paper advances the field of freeway ramp metering by introducing an application to the on-ramp, capitalizing on the macro traffic follow theory and improved the freeway traffic flow. The Particle Swarm Optimization (PSO) based on Proportional Integral Derivative (PID) controller is further developed to single ramp metering as well as to optimize the PID parameters. The approach is applied to a case study of the Changyi Freeway(G5513) in Hunan, China. The simulation is conducted by applying the actual profile traffic data to PID controller to adjust the entering traffic flow on the freeway on-ramp. The results show that the PSO-PID controller tends to converge in about 80 minutes, and the density tends to be stable after 240 iterations. The system has smaller oscillation, more accurate adjustment of ramp regulation rate, and more ideal expected traffic flow density. The traffic congestion on mainline is effectively slowed down, traffic efficiency is improved, and travel time and cost are reduced. The nonlinear processing ability of PSO-PID controller overcomes the defects of the traditional manual closing ramp, and can be successfully applied in the field of intelligent ramp metering.

A Highway On-Ramp Control Approach Integrating Percolation Bottleneck Analysis and Vehicle Source Identification

“…In Australia, a heuristic ramp-metering coordination (HERO) algorithm was implemented on the Monash highway to improve traffic conditions [19]. In China, on-ramp control approaches were widely implemented on many highways (e.g., the Changsha-Yiyang Highway [20]) to facilitate highway transportation management. On-ramp control played an important role in Turkey's highway traffic management systems [21].…”

“…The on-ramp control approaches were widely studied to alleviate highway traffic congestion [8]. Many countries implemented on-ramp control strategies in practice to facilitate highway transportation management [9][10][11][12][13][14][15][16][17][18][19][20][21]. Yet, despite the theoretical and practical achievements, existing on-ramp control approaches were usually developed for small theoretical networks or localized highway segments.…”

A stochastic numerical approach for a class of singular singularly perturbed system

“…PSO is used to cooperate the swarm performances of birds flocking as well as fish schooling [ 16 ]. Recently, PSO is used to predict the moisture contents of poplar fibers [ 17 ], communication systems [ 18 ], solar photovoltaic system [ 19 ], freeway ramp metering [ 20 ], real-time measurement of microgrid islanding [ 21 ], formulation of computer model with economic measures [ 22 ] and parameter identification with control [ 23 ]. Every particle contains the fitness performances describing the problem standards is known as merit function.…”

CFHBA-PID Algorithm: Dual-Loop PID Balancing Robot Attitude Control Algorithm Based on Complementary Factor and Honey Badger Algorithm

“…The Dual-loop PID control algorithm [4] is often used in the control of balancing robots [5], while it is difficult to tune the PID parameters simply by manual. Many studies [6][7][8][9][10][11][12][13][14][15][16][17][18] have proposed different methods to optimize the PID algorithm, which enable the PID algorithm to have better performance and to be applied to various scenarios. The merit of the PID parameters determines the effectiveness of the control algorithm as well as the accuracy and stability of balancing robot control.…”

“…The classical traditional metaheuristics are the particle swarm optimization algorithm (PSO) [20], ant colony optimization algorithm (ACO) [21], and genetic algorithms (GAs) [22], which have been frequently used in the PID control algorithm in recent years. In [8], the PSO-PID control algorithm is proposed to achieve freeway ramp control regulation. The results show that the convergence speed of the PSO-PID control algorithm is faster than that of the BP neural network in the process of ramp control, thus obtaining the best ramp control.…”