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Smart City: An Integrated Approach Using System Dynamics


Cities are drivers of growth for any country, and especially for a developing country like India. More than 50% of the world's population lives in cities, and this will increase to 66% by 2050 (World Bank, 2015). In the Indian context, 31% of the current population lives in urban areas, and contributes 63% of India's gross domestic product (GDP) (Census, 2011). This data is estimated to increase to 40% of India's population and 75% of India's GDP by 2030. India's annual population growth is 1.2% (World Bank, 2015). Due to the increase in the overall population, more people will migrate to the cities. According to one study, 25-30 people migrate from a rural area to an urban area in search of jobs and better lifestyles per day, and the urban population will reach nearly 843 million by 2050 (SM Conference, 2015). This increase in population requires comprehensive development of the city in terms of physical, social, and economic infrastructure. These terms are important pillars to improve the quality of life and the creation of jobs in the city. At the same time, these developments will negatively affect the environment in terms of pollution, and society in terms of traffic congestion due to the large number of vehicle ownership. This puts pressure on policy makers to develop a sustainable city. The smart city concept is one of the stepping stones in this direction.

The Indian government recently launched a smart city mission to develop 100 smart cities by 2024. This is the real challenge for policy makers and all the stakeholders involved in this smart transformation. Despite the attractiveness of smart cities, there is still fuzziness in the definition and dimensions of the smart city, so it is difficult to quantify the impact of the dimensions on the smart city. Nevertheless, the International Organization for

Standardization (ISO) developed an ISO 37120 in 2014 to measure city services and quality of life. In India, the Indian School of Business (ISB, 2015) also developed a smart city index to measure smartness. All these indexes will measure the smartness at a static level but due to the complexity of the smart city, it's difficult to capture the real scenario. System dynamics (SD) is one of the best tools to capture these nonlinear behavior-type scenarios (Forrester, 1997). The aim of this chapter is to analyze the impact of various dimensions on smartness and to assist policy makers in planning for smart cities.

This chapter addresses the complex structure of the smart city in an attempt to answer the following research questions:

  • 1. How can a smart city be modeled in order to support decision makers in appropriate planning?
  • 2. What is the structure of smart mobility with the application of an SD technique?

The remainder of the chapter is organized as follows: a literature review, followed by methodology, causal loop diagram (CLD), stock and flow diagram (CFD), assumption, results and analysis, recommendations and conclusions, limitations, and finally scope of future work.

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