Predicting vertical urban growth using genetic evolutionary algorithms in Tokyo’s Minato ward

Abstract

This article explores the use of evolutionary genetic algorithms to predict scenarios of urban vertical growth in large urban centers. Tokyo’s Minato Ward is used as a case study because it has been one of the fastest growing skylines over the last 20 years. This study uses a genetic algorithm that simulates the vertical urban growth of Minato Ward to make predictions from pre-established inputted parameters. The algorithm estimates not only the number of future high-rise buildings but also the specific areas in the ward that are more likely to accommodate new high-rise developments in the future. The evolutionary model results are compared with ongoing high-rise developments in order to evaluate the accuracy of the genetic algorithm in simulating future vertical urban growth. The results of this study show that the use of genetic evolutionary computation is a promising way to predict scenarios of vertical urban growth in terms of location as well as the number of future buildings.

Omar A. Mures

Instructor

My research interests include Deep Learning, Computer Vision and Computer Graphics.