Abstract
The growth of cities in the 21st century has put more pressure on resources and conditions of urban life. There are several reasons why the health-care industry is the focus of this investigation. For instance, in the UK various studies point to the lack of failure of basic quality control procedures and misalignment between customer needs and provider services and duplication of logistics practices. The development of smart cities and big data present unprecedented challenges and opportunities for operations managers; they need to develop new tools and techniques for network planning and control. Our paper aims to make a contribution to big data and city operations theory by exploring how big data can lead to improvements in transport capacity sharing. We explore using Markov models the integration of big data with future city (health-care) transport sharing. A mathematical model was designed to illustrate how sharing transport load (and capacity) in a smart city can improve efficiencies in meeting demand for city services. The results from our analysis of 13 different sharing/demand scenarios are presented. A key finding is that the probability for system failure and performance variance tends to be highest in a scenario of high demand/zero sharing.