Towards socially equitable public transport systems: The effect of COVID-19 on taxi trip behavior

•Change in daily average taxi trips and stations’ characteristics are modeled using taxi GPS data.•The analysis is conducted for four periods, namely total, off-peak, morning, and evening peaks.•K-means clustering is used to categorize the subway stations.•The estimation models are constructed by OLS, SAR, and GWR techniques.•Population, population density, socioeconomic level, and the number of shopping malls are found as significant. The COVID-19 pandemic emerged as a very influential occurrence with a profound impact on a global scale. The onset of the pandemic abruptly disrupted the regular course of everyday activities, primarily impacting urban regions. Hence, it is imperative to understand the effects of the COVID-19 pandemic on modern urban areas. This study seeks to analyze the effect of the pandemic on travel behavior by utilizing GPS data obtained from taxis, with a specific focus on spatial socioeconomic features. The M2 metro line in Istanbul has been selected for evaluation. In this analysis, four distinct periods are considered: total, off-peak, morning, and evening peaks. The stations are categorized using K-means clustering. The estimation models are constructed using ordinary least squares (OLS), spatial autoregression (SAR), and geographically weighted regression (GWR) techniques, which are applied to the variation in daily average cab trips and the characteristics of stations. The GWR models provide superior performance in comparison to the other two models, with notable distinctions observed in peak times, particularly morning peak when compared to total and off-peak counts. The findings indicate that factors such as population, population density, socioeconomic status, and the quantity of shopping malls are influential variables in elucidating and forecasting the fluctuations in taxi trip counts.