Next Article in Journal
Image Deblurring under Impulse Noise via Total Generalized Variation and Non-Convex Shrinkage
Next Article in Special Issue
Comparison and Interpretation Methods for Predictive Control of Mechanics
Previous Article in Journal
On Finding Two Posets that Cover Given Linear Orders
Open AccessArticle

Freeway Traffic Congestion Reduction and Environment Regulation via Model Predictive Control

by Juan Chen 1,2,*, Yuxuan Yu 1 and Qi Guo 1
1
SHU-UTS SILC Business School, Shanghai University, Shanghai 201899, China
2
Smart City Research Institute, Shanghai University, Shanghai 201899, China
*
Author to whom correspondence should be addressed.
Algorithms 2019, 12(10), 220; https://doi.org/10.3390/a12100220
Received: 30 August 2019 / Revised: 13 October 2019 / Accepted: 15 October 2019 / Published: 21 October 2019
(This article belongs to the Special Issue Model Predictive Control: Algorithms and Applications)
This paper proposes a model predictive control method based on dynamic multi-objective optimization algorithms (MPC_CPDMO-NSGA-II) for reducing freeway congestion and relieving environment impact simultaneously. A new dynamic multi-objective optimization algorithm based on clustering and prediction with NSGA-II (CPDMO-NSGA-II) is proposed. The proposed CPDMO-NSGA-II algorithm is used to realize on-line optimization at each control step in model predictive control. The performance indicators considered in model predictive control consists of total time spent, total travel distance, total emissions and total fuel consumption. Then TOPSIS method is adopted to select an optimal solution from Pareto front obtained from MPC_CPDMO-NSGA-II algorithm and is applied to the VISSIM environment. The control strategies are variable speed limit (VSL) and ramp metering (RM). In order to verify the performance of the proposed algorithm, the proposed algorithm is tested under the simulation environment originated from a real freeway network in Shanghai with one on-ramp. The result is compared with fixed speed limit strategy and single optimization method respectively. Simulation results show that it can effectively alleviate traffic congestion, reduce emissions and fuel consumption, as compared with fixed speed limit strategy and classical model predictive control method based on single optimization method. View Full-Text
Keywords: freeway transportation; congestion control; environment impact; dynamic multi-objective optimization; model predict control; clustering and prediction freeway transportation; congestion control; environment impact; dynamic multi-objective optimization; model predict control; clustering and prediction
Show Figures

Figure 1

MDPI and ACS Style

Chen, J.; Yu, Y.; Guo, Q. Freeway Traffic Congestion Reduction and Environment Regulation via Model Predictive Control. Algorithms 2019, 12, 220.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop