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As in other countries of the world, the Turkish government is implementing many preventive partial and total lockdown practices against the virus's infectious effect. When the first virus case has been detected, the public authorities have taken some restriction to reduce people and traffic mobility, which has also turned into some positive affect in air quality. To this end, the paper aims to examine how this pandemic affects traffic mobility and air quality in Istanbul. The pandemic does not only have a human health impact. This study also investigates the social and environmental effects. In our analysis, we observe, visualize, compare and discuss the impact of the post- and pre-lockdown on Istanbul's traffic mobility and air quality. To do so, a geographic information system (GIS)-based approach is proposed. Various spatial analyses are performed in GIS with the statistical data used; thus, the environmental effects of the pandemic can be better observed. We test the hypothesis that this has reduced traffic mobility and improved air quality using traffic density cluster set and air monitoring stations (five air pollutant parameters) data for five months. The results shows that there are positive changes in terms of both traffic mobility and air quality, especially in April–May. PM10, SO2, CO, NO2 and NOx parameter values improved by 21.21%, 16.55%, 18.82%, 28.62% and 39.99%, respectively. In addition, there was a 7% increase in the average traffic speed. In order for the changes to be permanent, it is recommended to integrate e-mobility and sharing systems into the current transportation network. [ABSTRACT FROM AUTHOR]

Output targeting and capacity utilization issues are crucial but troublesome tasks for the new airport projects. This study aims to identify the output targets for the new-built airport in Istanbul to be considered operationally efficient when compared to regional and global counterparts and to investigate the utilization rate of input resources. To do that, a novel two-stage approach was proposed. The first stage involves setting output targets using a modified DEA approach and multi-layered perceptron algorithm. Once the output targets are determined individually by these two methods, slack variable analysis of DEA was applied in the second stage to define the input slacks related to infrastructure. Analysis results revealed the passenger, cargo, and aircraft movement targets for each stage of the project. Input slacks were also identified, and possible avenues for input reduction were explored that would enhance capacity utilization. • Output targeting and capacity utilization are crucial for the new airport projects. • This paper is based on a case study of the new airport project in Istanbul. • A modified DEA and multi-layered perceptron algorithm were applied. • Output targets are set and input slacks are identified for each stage of the project. [ABSTRACT FROM AUTHOR]