Agent-based Modeling in Challenges of Urban Studies and Metropolitan Policies

Ali Esmaeili Fard¹ Mahmood Golabchi²

1) Iran University of Tehran Kish International Campus Email:
2) Iran University of Tehran Email:

Publication : 3rd International Congress On Engineering, Technology and Innovation(eticong.com/3rd)
Abstract :
The development of science and research in interdisciplinary and between majors and its influence in different science have recently led to the development of new ways in the study of urbanism and decision-making policy in this field. As a matter of fact, to achieve a reliable answer to urbanism’s questions in general and urbanism’s and metropolitan’s policy in specific, a large number of studies has been necessary in this field for many years. There are many problems that researchers in the field of urbanism should deal with in this process such as longitudinal studies, the possibility of the expiration of answers when it is going to be operationalized and the expenses of its practicality in different economic, social and urban issues. Today, these problems have decreased due to the advancement of research in the field of computer science, computer programming, and artificial intelligence. This development results in the production of different ways of computational modeling in the economic ways during limited time. One of these ways is Agent-based Modeling (ABM). This computational modeling is applied as an efficient instrument for simulation in a virtual environment. Then, some new parameters are given to a designed model in terms of ABM in order to examine it before the actual performance. This model can be very beneficial in all aspects of urbanism. For example, this way was used in the design of the routes of transportation in subways in Germany. In addition, it has really been helpful in the study of society, economics and crisis management in industrial countries. It can decrease the occurrence of problems in the field of design in cities. Therefore, the usage of this model is very crucial to avoid internal or unintentional mistakes which can affect user’s lives.
Keywords : Agent-based Modeling Artificial intelligence Computational modeling Metropolitan’s policy