Adaptive multi-objective traffic signal control using NLRMNSGA-II algorithm

2018 ◽  
Vol 45 (11) ◽  
pp. 973-985
Author(s):  
Yuan-Yang Zou ◽  
Xue-Guo Xu ◽  
Gui-Hua Lin
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Efficient Algorithm ◽  
Numerical Experiments ◽  
Adaptive System ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Multi Objective Optimization ◽  
New Model ◽  
Multi Objective

In this paper, we consider an adaptive system for controlling green times at junction. For this adaptive system, we present a multi-objective optimization model, which is much easier to solve than some existing models. Furthermore, to solve the new model, we suggest an algorithm, called NLRMNSGA-II, which is based on the nonlinear least regression and a modified non-dominated sorting genetic algorithm. Our numerical experiments indicate that the NLRMNSGA-II is an efficient algorithm for the considered adaptive system.

scholarly journals Multi-Objective Optimization of Traffic Signal Timing for Oversaturated Intersection

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yan Li ◽  
Lijie Yu ◽  
Siran Tao ◽  
Kuanmin Chen
Keyword(s):  
Simulation Software ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Simulation Environment ◽  
Multi Objective Optimization ◽  
Good Efficiency ◽  
Multi Objective ◽  
Traffic Signal Timing

For the purpose of improving the efficiency of traffic signal control for isolate intersection under oversaturated conditions, a multi-objective optimization algorithm for traffic signal control is proposed. Throughput maximum and average queue ratio minimum are selected as the optimization objectives of the traffic signal control under oversaturated condition. A simulation environment using VISSIM SCAPI was utilized to evaluate the convergence and the optimization results under various settings and traffic conditions. It is written by C++/CRL to connect the simulation software VISSIM and the proposed algorithm. The simulation results indicated that the signal timing plan generated by the proposed algorithm has good efficiency in managing the traffic flow at oversaturated intersection than the commonly utilized signal timing optimization software Synchro. The update frequency applied in the simulation environment was 120 s, and it can meet the requirements of signal timing plan update in real filed. Thus, the proposed algorithm has the capability of searching Pareto front of the multi-objective problem domain under both normal condition and over-saturated condition.

scholarly journals Multi-objective Optimization Model of Multi-modal Transport Based on Regional Sustainability Indicators

2020 ◽  
Vol 325 ◽  
pp. 03001
Author(s):  
Shengjiao Yang ◽  
Zuoling Song
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Social Indicators ◽  
Free Trade Area ◽  
Third Party ◽  
Local Optimum ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Freight Forwarder ◽  
Population Genetic Algorithm

With the development of “One Belt, One Road” initiative and free trade area, the volume of cross-border international logistics involving multiple modes of transport has surged. Meanwhile, the proportion of using integrated transportation system in domestic trunk transport has increased. Multi-modal transport (MMT) based on green transport can realize intensive utilization of transport capacity resources, and implement sustainable transport management with three bottom lines of economic, environmental and social aspects. In this paper, the carbon emission index and regional transportation infrastructure utilization index are introduced to construct a multi-objective optimization model with sustainable goals of environmental protection, cost saving and social contribution. The poly-population genetic algorithm (PPGA) is used to overcome the limitation of the traditional genetic algorithm running to the local optimum. The model proposed by this paper quantifies environmental and social indicators, balances comprehensive performance of environment, economy and society, and provides quantitative decision making support for carriers, international freight forwarder or third party logistics to carry out green MMT.

Research on the Multi-Objective Optimization Model of System-Level BIT Testability Index Determination

2011 ◽  
Vol 121-126 ◽  
pp. 2223-2227 ◽  
Author(s):  
Chun Sheng Zhu ◽  
Qi Zhang ◽  
Fan Tun Su ◽  
Hong Liang Ran
Keyword(s):  
Genetic Algorithm ◽  
Life Cycle ◽  
Mathematical Models ◽  
Optimization Model ◽  
System Reliability ◽  
Life Cycle Cost ◽  
System Level ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Life Circle

By weighing reliability, maintainability, availability and life-cycle cost of equipment which are influenced by testability，the testability indexes of system level BIT are determined on the basis of maximum system reliability & maintainability and minimum the life-circle cost. The influence mathematical models of system reliability, maintainability, availability and life-circle cost are established. According to these mathematical models, the multi-objective optimization model of system-level BIT testability indexes is established. The multi-objective optimization model is solved using Non-dominated Sorting Genetic Algorithm II, and the validity of the multi-objective optimization model is proved through an example.

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