scholarly journals Model Predictive Control for Ecological Driving Using Mixed Integer Programming Based on Traffic Signal Information

2015 ◽  
Vol 51 (12) ◽  
pp. 866-872
Author(s):  
Masakazu MUKAI ◽  
Hiroshi AOKI ◽  
Taketoshi KAWABE
Keyword(s):  
Integer Programming ◽  
Model Predictive Control ◽  
Mixed Integer Programming ◽  
Predictive Control ◽  
Traffic Signal ◽  
Mixed Integer

Network-Wide Optimization of Traffic Signals Using Mixed Integer Programming

2014 ◽  
Vol 26 (5) ◽  
pp. 607-615 ◽  
Author(s):  
Md. Abdus Samad Kamal ◽  
◽  
Jun-ichi Imura ◽  
Tomohisa Hayakawa ◽  
Akira Ohata ◽  
...  
Keyword(s):  
Integer Programming ◽  
Model Predictive Control ◽  
Mixed Integer Programming ◽  
Predictive Control ◽  
Road Network ◽  
Traffic Signals ◽  
Continuous Variable ◽  
Traffic Signal ◽  
Mixed Integer ◽  
Traffic Flows

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00260005/09.jpg"" width=""300"" />Network with four intersections</div> In this paper a network-wide traffic signal control scheme in a model predictive control framework using mixed integer programming is presented. A concise model of traffic is proposed to describe a signalized road network considering conservation of traffic. In the model, the traffic of two sections that belong to a traffic signal group of a junction are represented by a single continuous variable. Therefore, the number of variables required to describe traffic in the network becomes half compared with the models that describe section wise traffic flows. The traffic signal at the junction is represented by a binary variable to express a signal state either green or red. The proposed model is transformed into a mixed logical dynamical system to describe the traffic flows in a finite horizon, and traffic signals are optimized using mixed integer linear programming (MILP) for a given performance index. The scheme simultaneously optimizes all traffic signals in a network in the context of model predictive control by successively extending or terminating a green or red signal of each junction. Consequently, traffic signal patterns with the optimal free parameters, i.e., the cycle times, the split times and the offsets, are realized. Use of the proposed concise traffic model significantly reduces the computation time of the scheme without compromising the performance as it is evaluated on a small road network and compared with a previously proposed scheme. </span>

Robust Connectivity for Multiagent Systems using Mixed-Integer Programming

2020 ◽  
Author(s):  
Angelo Caregnato-Neto ◽  
Marcos R. O. A. Maximo ◽  
Rubens J. M. Afonso
Keyword(s):  
Communication Network ◽  
Integer Programming ◽  
Model Predictive Control ◽  
Mobile Robots ◽  
Mixed Integer Programming ◽  
Multiagent Systems ◽  
Network Model ◽  
Predictive Control ◽  
Mixed Integer ◽  
Connectivity Problem

This work is concerned with the problem of planning trajectories for a group of mobile robots operating in a obstacle-filled environment. The objective of the robots is to cooperatively reach targets sets, avoiding collisions between themselves and with obstacles, while maintaining the robust connectivity of their communication network. Model predictive control with mixed-integer encoding is used to plan the trajectories in a centralized fashion. Two novel constraints are introduced to cope with the robustness connectivity problem. Simulations are provided to evaluate the proposal.

Traffic Signal Control in an MPC Framework Using Mixed Integer Programming

2013 ◽  
Vol 46 (21) ◽  
pp. 645-650 ◽  
Author(s):  
M.A.S. Kamal ◽  
J. Imura ◽  
T. Hayakawa ◽  
A. Ohata ◽  
K. Aihara
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
Traffic Signal ◽  
Mixed Integer ◽  
Signal Control ◽  
Traffic Signal Control
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