Land Use and Transportation Planning for Twin Cities Using a Genetic Algorithm

2000 ◽  
Vol 1722 (1) ◽  
pp. 67-74 ◽  
Author(s):  
Richard J. Balling ◽  
John Taber ◽  
Kirsten Day ◽  
Scott Wilson
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Transportation Planning ◽  
Fitness Function ◽  
High Growth ◽  
Pareto Set ◽  
Decision Makers ◽  
Twin Cities ◽  
New Approach ◽  
Future Plans

A new approach to future land use and transportation planning for high-growth cities is presented. The approach employs a genetic algorithm to efficiently search through hundreds of thousands of possible future plans. A new fitness function is developed to guide the genetic algorithm toward a Pareto set of plans for the multiple competing objectives that are involved. This set may be placed before decision makers. A Pareto set scanner also is described that assists decision makers in shopping through the Pareto set to select a plan. Some of the differences between simultaneous planning and separate planning of highly coupled twin cities also are examined.

Regional Land Use and Transportation Planning with a Genetic Algorithm

2003 ◽  
Vol 1831 (1) ◽  
pp. 210-218 ◽  
Author(s):  
Richard Balling ◽  
Michael Lowry ◽  
Mitsuru Saito
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Regional Planning ◽  
Traffic Congestion ◽  
Transportation Planning ◽  
Fitness Function ◽  
Search Space ◽  
Metropolitan Region ◽  
Search Spaces ◽  
Regional Land

A new approach to regional land use and transportation planning, which uses a genetic algorithm as an integrated optimization tool, is presented. The approach is illustrated by applying it to the Wasatch Front Metropolitan Region, which consists of four counties in the state of Utah. This genetic algorithm–-based approach was applied earlier to the twin cities of Provo and Orem in Utah, but here it is adapted to regional planning. Three issues make regional planning particularly difficult: ( a) individual cities have significant planning autonomy, ( b) the search space of possible plans is immense, and ( c) preferences between competing objectives vary among stakeholders. The approach used here addresses the first issue by the way the problem is formulated. The second issue is addressed with a genetic algorithm. Such algorithms are particularly well suited to problems with large search spaces. The third issue is addressed by using a multiobjective fitness function in the genetic algorithm. It was found that a genetic algorithm could produce a set of nondominated future land use scenarios and street plans for a region, from which regional planners can make a selection. Execution of the algorithm to produce 100 plans per generation for 100 generations took about 4 days with a high-end personal computer. Interesting trends for reducing change and traffic congestion were discovered.

scholarly journals Dynamic models of multicriteria to combine variables: the use of parametric model and genetic algorithm in the study of the World Heritage area recognized by UNESCO in Pampulha, Brazil

2019 ◽  
Vol 14 (1) ◽  
pp. 142-159
Author(s):  
Silvio Romero Fonseca Motta ◽  
Ana Clara Mourão Moura ◽  
Suellen Roquete Ribeiro
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Land Use Change ◽  
Dynamic Models ◽  
Fitness Function ◽  
Land Use Changes ◽  
Multicriteria Analysis ◽  
World Heritage ◽  
Parametric Model ◽  
Belo Horizonte

The present paper surveys dynamic models of multicriteria to combine variables using parametric model and genetic algorithm as a method of changing the adequacy level of variables in a multicriteria analysis (MCA). The aim is to simulate if-then scenarios of territorial occupation of commerce, housing and green areas. The case study is a MCA for the buffer zone of the modern assembly of Niemeyer in Pampulha region, Belo Horizonte, Brazil, declared World Heritage by UNESCO. The parametric model was developed in Grasshopper software. The level of adequacy/score of the territorial units to characterize attractiveness and vulnerabilities to land use change was defined by "knowledge-driven" in the layers: Safety Risks, Fragility in Infrastructure, Bus Stop and Centralities due to Interaction Potential. The land use change simulation "if-then" was defined by "objective-driven", due the use of fitness-function in genetic algorithm, with the goal to achieve the best distribution of land use changes, in order to result in a more balanced use of the territory (commerce, housing or vegetation), but also considering attractiveness and vulnerabilities defined by the characteristics of the neighborhoods (centralities, transportation, safety and fragilities in infrastructure). The parametric model generates “if-then” simulation, calculating an index of suitability for each territorial unit and changing the land use according to the objective-driven to be achieved in fitness-function.

scholarly journals An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Saeid Jafarzadeh Ghoushchi ◽  
Ramin Ranjbarzadeh ◽  
Amir Hussein Dadkhah ◽  
Yaghoub Pourasad ◽  
Malika Bendechache
Keyword(s):  
Genetic Algorithm ◽  
Diagnostic Method ◽  
Fitness Function ◽  
Jaccard Index ◽  
Diabetic Patients ◽  
Growth Region ◽  
New Approach ◽  
Fuzzy C Means ◽  
Extended Approach ◽  
Selection Of

The present study is developed a new approach using a computer diagnostic method to diagnosing diabetic diseases with the use of fluorescein images. In doing so, this study presented the growth region algorithm for the aim of diagnosing diabetes, considering the angiography images of the patients’ eyes. In addition, this study integrated two methods, including fuzzy C-means (FCM) and genetic algorithm (GA) to predict the retinopathy in diabetic patients from angiography images. The developed algorithm was applied to a total of 224 images of patients’ retinopathy eyes. As clearly confirmed by the obtained results, the GA-FCM method outperformed the hand method regarding the selection of initial points. The proposed method showed 0.78 sensitivity. The comparison of the fuzzy fitness function in GA with other techniques revealed that the approach introduced in this study is more applicable to the Jaccard index since it could offer the lowest Jaccard distance and, at the same time, the highest Jaccard values. The results of the analysis demonstrated that the proposed method was efficient and effective to predict the retinopathy in diabetic patients from angiography images.

From Mobility to Accessibility

2019 ◽  
Author(s):  
Jonathan Levine ◽  
Joe Grengs ◽  
Louis A. Merlin
Keyword(s):  
Land Use ◽  
Built Environment ◽  
Land Use Planning ◽  
Transportation Planning ◽  
Metropolitan Areas ◽  
Transportation Systems ◽  
Decision Makers ◽  
Modern Cities ◽  
Standard Models ◽  
Per Se

This book flips the tables on the standard models for evaluating regional transportation performance. It argues for an “accessibility shift” whereby transportation planning, and the transportation dimensions of land-use planning, would be based on people's ability to reach destinations, rather than on their ability to travel fast. Existing models for planning and evaluating transportation, which have taken vehicle speeds as the most important measure, would make sense if movement were the purpose of transportation. But it is the ability to reach destinations, not movement per se, that people seek from their transportation systems. While the concept of accessibility has been around for the better part of a century, the book shows that the accessibility shift is compelled by the fundamental purpose of transportation. It argues that the shift would be transformative to the practice of both transportation and land-use planning but is impeded by many conceptual obstacles regarding the nature of accessibility and its potential for guiding development of the built environment. By redefining success in transportation, the book provides city planners, decision makers, and scholars a path to reforming the practice of transportation and land-use planning in modern cities and metropolitan areas.

Genetic Algorithm for Assembly Sequences Planning Based on Heuristic Assembly Knowledge

2010 ◽  
Vol 44-47 ◽  
pp. 3657-3661 ◽  
Author(s):  
Hao Pan ◽  
Wen Jun Hou ◽  
Tie Meng Li
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Graph Model ◽  
Initial Population ◽  
New Approach ◽  
Assembly Sequences ◽  
Connection Graph ◽  
Assembly Sequences Planning

To improve the efficiency of Assembly Sequences Planning (ASP), a new approach based on heuristic assembly knowledge and genetic algorithm was proposed. First, Connection Graph of Assembly (CGA) was introduced, and then, assembly knowledge was described in the form of Assembly Rings, on that basis, the assembly connection graph model containing Assembly Rings was defined, and the formation of initial population algorithm was given. In addition, a function was designed to measure the feasible assembly and then the genetic algorithm fitness function was given. Finally, an example was shown to illustrate the effectiveness of the algorithm.

GIS-based genetic algorithm optimization tool for supporting land use and land management restructuring

2007 ◽  
Vol 13 (1s) ◽  
pp. 33-37
Author(s):  
V. Makarenko ◽  
◽  
G. Ruecker ◽  
R. Sommer ◽  
N. Djanibekov ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Land Management ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization

scholarly journals Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 115
Author(s):  
Andriy Chaban ◽  
Marek Lis ◽  
Andrzej Szafraniec ◽  
Radoslaw Jedynak
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Least Square ◽  
Distributed Parameter ◽  
Parameter Method ◽  
Analytical Mechanics ◽  
Parameter System ◽  
Rotational Systems ◽  
Elastic Elements

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton–Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained results confirm that application of the genetic algorithms allow one to replace the use of a complicated distributed parameter model of mechanical system by a considerably simpler model, and to eliminate sophisticated calculation procedures and identification of boundary conditions for wave motion equations of long elastic elements.

scholarly journals A Method for Integration of Preferences to a Multi-Objective Evolutionary Algorithm Using Ordinal Multi-Criteria Classification

2021 ◽  
Vol 26 (2) ◽  
pp. 27
Author(s):  
Alejandro Castellanos-Alvarez ◽  
Laura Cruz-Reyes ◽  
Eduardo Fernandez ◽  
Nelson Rangel-Valdez ◽  
Claudia Gómez-Santillán ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Selective Pressure ◽  
Optimization Problems ◽  
Region Of Interest ◽  
Decision Makers ◽  
Multiple Objective ◽  
Objective Functions ◽  
Multi Objective ◽  
Imperfect Knowledge ◽  
Real World Problems

Most real-world problems require the optimization of multiple objective functions simultaneously, which can conflict with each other. The environment of these problems usually involves imprecise information derived from inaccurate measurements or the variability in decision-makers’ (DMs’) judgments and beliefs, which can lead to unsatisfactory solutions. The imperfect knowledge can be present either in objective functions, restrictions, or decision-maker’s preferences. These optimization problems have been solved using various techniques such as multi-objective evolutionary algorithms (MOEAs). This paper proposes a new MOEA called NSGA-III-P (non-nominated sorting genetic algorithm III with preferences). The main characteristic of NSGA-III-P is an ordinal multi-criteria classification method for preference integration to guide the algorithm to the region of interest given by the decision-maker’s preferences. Besides, the use of interval analysis allows the expression of preferences with imprecision. The experiments contrasted several versions of the proposed method with the original NSGA-III to analyze different selective pressure induced by the DM’s preferences. In these experiments, the algorithms solved three-objectives instances of the DTLZ problem. The obtained results showed a better approximation to the region of interest for a DM when its preferences are considered.

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