Extracting Spatio-Temporal Patterns of Taxi Trips During COVID-19: A Case Study of Chicago

Authors: Ben Wang*, Texas State University, Yihong Yuan, Texas State University
Topics: Geographic Information Science and Systems, Temporal GIS, Transportation Geography
Keywords: Human mobility, Floating car data, Time series decomposition, COVID-19
Session Type: Virtual Paper
Day: 4/9/2021
Start / End Time: 8:00 AM / 9:15 AM
Room: Virtual 8
Presentation File: No File Uploaded


In 2020, the whole world experienced unprecedented challenges caused by COVID-19, and the United States is one of the most impacted areas. With the high contagiousness of COVID-19, human mobility became the catalyst of the pandemic. The rapid growth of big geo-data also provides an opportunity and a challenge to explore human mobility during a pandemic. This research extracts spatio-temporal urban dynamics from floating car data (FCD) in Chicago during and before COVID-19. The taxi trip records are aggregated by the hour at the community area level, so there is a taxi trip time series in each community area. We applied a time series decomposition method, Seasonal-Trend decomposition using LOESS (STL), to analyze taxi trips' spatio-temporal patterns. STL can divide the original time series into different components, including trend, seasonality, and residuals. We also clustered the trend and seasonal effects of time series in different community areas of Chicago. The results show that time series decomposition is useful for understanding the various aspects of temporal patterns when studying urban dynamics. The comparison of the spatio-temporal patterns of taxi trips during and before COVID-19 provides an insight into understanding human mobility during a pandemic.

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