Abstract:
COVID-19 has become one of the most signi cant events in this century and the e ects are felt in daily life all around the world economically and socially. Therefore, investigating the e ects of COVID-19 must be prioritized to minimize future damages. The goal of this thesis is to reveal di erent travel reactions to COVID-19 based on spatial and socioeconomic characteristics such as public transport connectivity, education level, female percentage, etc. Towards this goal, taxi GPS data is used and the M2 subway line in Istanbul is selected as the case study area since the line covers many residential and commercial centers. The prepared COVID-19 timeline is divided into ve phases based on critical events such as the announcement of governments or unexpected peaks in daily case numbers. The analyses are conducted for the average trip counts in four time periods of a day, namely total, o -peak, morning, and evening. K-means clustering is used to observe the relationship between stations and the data is analyzed by ordinary least squares (OLS), spatial auto regression (SAR), and geographically weighted regression (GWR) models based on daily average trip counts and characteristics of stations. The best results are obtained by the GWR model. According to the results, the population size is one of the most signi cant parameters, that explains the change in trip counts. The morning peak shows a unique characteristic that can be explained by the socioeconomic index, which is a weighted average of many parameters including education and income level. In general, the decrease in taxi trips is higher for the areas with a higher socioeconomic index. Other signi cant variables are the number of shopping malls, the existence of another public transportation option, and the population density.
