In our analysis, we utilize such large-scale datasets to explore structural changes in transportation networks arising from taxi trips in Chicago. Large public sector organizations in Chicago are providing comprehensive open data to the public. Due to recent advances in creating, transferring, storing, and analyzing digital data, a high volume of data on the daily mobility in smart cities data is generated and captured at multiple sources. To facilitate better operations management of transportation networks in smart cities, large-scale datasets on daily travel usage and end-user feedback need to be collected. Further, this helps various decision-makers deploy appropriate resources in certain city localities so that social distancing measures are properly implemented in high-density traffic zones. Understanding daily mobility pattern changes due to the pandemic helps policymakers analyze how various communities respond to social distancing measures. This is because the spread of the disease has halted everyday life, and therefore, urban transportation activities. During the COVID-19 crisis, the public-transient ridership has fallen from 70 percent to 90 percent.

Moreover, it is important to understand the human transportation networks as this may help policymakers easily control the spreading of the disease by limiting contact among individuals. Since the mobility of infected people from one location to another may lead to the spread of infection, the transportation networks are changing during the current crisis.

Lockdown measures aimed at mitigating the spread of the virus, have significantly affected human mobility. Recent studies on the COVID-19 pandemic have shown how the global crisis has impacted global supply chains, bed capacity for patients in hospitals, mobile service operations, and omnichannel retailers. The COVID-19 outbreak has drastically led to various operational issues in society.

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