Can Digital Twin help build Smart City?
By: Jash Mehta
Tags: Smart City, Digital Twin

Abstract

Smart cities are important for not only to benefit the life quality of the citizens but also to help plan and design the city perfectly with the analytical data to save power, time, and money. With the help of Digital twins making smart cities have become much easier and more efficient. This paper proposes a methodological framework for digital modelling of cities assessment of their performance and supporting decision making. Digital twin helps acts as a powerful tool for finding smallest solutions and get valuable insights over it. The framework shows how digital twin is adopted for implementation of basic smart city models.

Introduction

This article addresses the concepts of smart city and digital twin technology as means to overcome cities´ sustainability and urban metabolism issues. Smart city denotes the usage of digital technologies like IoT, big data and (AI) to improve socio-economic and environmental outcomes of the city. Digital twin technology illustrates both virtual and physical representation of an object. With IoT and sensor technologies, it is possible to form dynamic connectivity between virtual replica and its physical counterpart. Dynamic connectivity of digital twin enables combining data and observing digital twin in its real and virtual world.

What is Smart city

From Urban planning to Land optimization, smart city has the power to govern the city in efficient manner. These cities are managed to effective power outcomes in allocating resources, providing security, maximizing services, facilitating human activities, and preventing disruption, while continuously adapting to the changing behaviours of the citizens.

Smart city with digital twins

Digital twin mind map, Jash mehta, 2020

The digital twin city, cognizant of the city’s infrastructure performance, human dynamics, their interdependencies and compatibility, and their fluctuations in time and space, is progressively able to anticipate changes of state in the systems and predict possible future behaviours. The predictive conduct of the digital twin city relies on the real-time and aggregated historical performance of the human-infrastructure systems. Additionally, irrespective of the current state of the city, a digital twin city can simulate what-if scenarios in the system and anticipate emergent behaviour. This helps analysts understand how cities equipped with smart technologies will likely perform under various economic, environmental, and social conditions, and identify the drivers of possible disruptions.

Methodology

Digital twin concept- Real time data are converted and stored into the database which then is integrated into a software which presents the real-world data into a virtual simulation of that data with precision. Then these data are used to building models of the city.

Methodology stages

· Create- This stage includes of creating City Information Model (CMI) by collecting data from sensors

· Interact- Provides real time data between physical city and virtual city

· Aggregate- Saves data and then do pre-processing of the data

· Analysis- Variety of CMI are build and then compared

· Insight- Shows the real difference in physical and virtual city

· Decision- The strategies which should be applied to the physical world based of the results of virtual world

Conclusion

Building a digital footprint of the city with digital twin has never been easier to interconnect the physical world with the virtual world, this decreases the gap in between the world and helps provide theoretical solutions to the smart city. This paper focus of the main concept of implementations of the Smart city with the help of digital twin to make data-driven decisions, interconnect them with their possible effects and finally, get insight about city performance towards digital transition

References

[1] Timo Ruohomäki 2018 International Conference on Intelligent Systems (IS). Smart City Platform Enabling Digital Twin

[2] ITU-T Focus group, 2015; ISO, 2013

[3] Dessislava Petrova-Antonova 18–21 June 2019 Methodological Framework for Digital Transition and Performance Assessment of Smart Cities