The concept of digital twins is the accurate virtual representation of an object or objects to sim- ulate its behavior with the help of machine learning and digital reasoning. The digital twin was first introduced with the publication of Mirror Worlds by David Glenter, however Dr. Michael Grieves ( a faculty at University of Michigan) is credited for applying the concept in the year 2002. Digital twins can be attributed and utilized in various environments and have different types. Component twins/Parts twins, Asset twins, System or Unit twins and Process twins are the major types. A digital twin can consist of one or more than one of these types in a virtual environment then can utilize real-time or real-world data to run simulation and calculate faults or problems of those objects. On the other hand, Digital Twins are applicable to the concept of smart cities where we can make a very eco-friendly environment by advancing the cities infras- tructure. The research for social events classification, detection, patters, smart cities, are being done by many researchers in the field, however all of them despite their considerable amount of work lack a simulation of social events and their impacts in the context of digital twins of a smart city.
In this paper we consider applying digital in the context of social events for smart cities to simulate the available agents behaviour. More specifically a method will be introduced to simulate how social events can generate traffic based on digital reasoning. They key idea behind the platform is to employ and evaluate Digital Twins which are executable models of system components.