Where did bike-share boom? Analyzing impact of infrastructure lockdowns on bike-sharing in Chicago

Abstract

The COVID-19 pandemic and the subsequent measures taken to control its spread had deep impacts on transportation behavior. This paper presents a methodology for evaluating the changes in bike-share use and focuses on the identification of extraordinary trip-making activity seen in Chicago’s Divvy bike-share program prior to and over the course of the COVID-19 pandemic. An analysis period from January 2018 to April 2021 was subdivided into three time periods: pre-pandemic, citywide lockdown, and post-lockdown for all census tracts within the city of Chicago. Over the analysis window defined, an anomaly detection algorithm was used to characterize the nature of bike-share usage where anomalous trips were those that fell outside of the forecasted ranges. The changes in census tract-level bike-share usage were used to interpret how static census tract-level attributes—land use, transit connectivity, demographics, and bike infrastructure—may have contributed to varying responses across the city’s bike-share system. Principal component analysis was used to interpret the relationship between the dynamic trip-making characterizations and the static census tract-level attributes. Census tracts that transitioned from exhibiting below average to above average bike-share trip-making tended to have highly correlated bike infrastructure metrics. On the other hand, census tracts with consistently above average trip-making had highly correlated demographic characteristics, the values of which were consistent with their standing as key communities for service and essential workers. Overall, results indicate that post-lockdown, bike-sharing activity had grown significantly across the entire city, supporting the claim that the pandemic worked to accelerate bicycling uptake in urban areas.

Keywords

• Bike-share
• Anomaly detection
• COVID-19
• Infrastructure
• Geospatial
• Mobility