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Taking advantage of the disruption that COVID-19 had on urban mobility, Barcelona has engaged in tactical urbanism through a series of rapid infrastructural changes to address the abrupt shift in societal behavior that has taken place in the city.
In a study published at the Sustainable Cities and Society journal with Ryan Federo and Xavier Fernández-i-Marin, we analyzed how a comprehensive set of factors influenced the use of Bicing before and after the COVID-19 lockdown in Barcelona, identified the relevant factors that help predict Bicing usage, and unpacked how the relevance of these factors changed as the global pandemic unfolded. Ultimately, we uncovered evidence of the effectiveness of tactical urbanism in riding the wave of the global pandemic.
In a quasi-experimental research design, we use probabilistic machine learning to identify the key factors in predicting the use of Bicing (the bike-sharing system in Barcelona) before and during COVID-19.
We find that increasing bike-related built infrastructure, trip distance, and the income levels of neighborhoods are the most relevant predictors of the increase in bicing usage. Interestingly, tactical urbanism is effective to increase the use of BSS.
Moreover, we find that the relevance of the factors in predicting Bicing usage has generally decreased during the global pandemic, suggesting altered societal behavior during COVID-19. For example, neighborhoods with a higher percentage of women and from lower-income neighborhoods have increased their use of Bicing as the pandemic unfolded.
Our study is important for developing resilient programs for cities to adopt sustainable practices through transport policy, infrastructure planning, and urban development. In particular, we uncover evidence of the effectiveness of the city’s implementation of tactical urbanism during the pandemic, as the influence of the changes in the bike-related built infrastructure in increasing Bicing usage throughout the city is highly relevant.
The following figures represent the key findings of the study.
Total number of Bicing trips from June 24 to December 31
The following graph shows the evolution of bicing trips before and during COVID-19 (the COVID-19 period being the area in gray).
The trends between the two periods are similar, where the peaks of the trip waves are on working days while the troughs are on nonworking days. Bicing movements during 2020 were clearly higher in terms of total daily trips than in 2019.
Bicing trips and bike-related infrastructure before and during COVID-19
The following figures display the movement of Bicing trips and the changes in bike-related built infrastructure (i.e., bike lanes and bike-friendly streets) throughout Barcelona as the pandemic unfolded. The circles represent the bike stations; circle colors show the number of daily trips from the origin station. Blue lines are bike lanes, while green lines are bike-friendly streets (also called carrers pacificats).
We observe that bike lanes (the blue lines in the maps) have increased from 2019 to 2020. In 2019, bike-friendly streets (the green lines in the maps) were more prominent at the outskirts of the city where Bicing movements were low. After the lockdown in 2020, Barcelona has increased the number of bike-friendly streets by setting a maximum speed limit of 30km/hour in many streets in highly populated areas of the city, such as the districts of El Raval, Barri Gòtic, and Gracia. With regard to Bicing trips, the increase in the number can also be observed at many stations as is represented by the circles on the map with their colors darkened in the periods shown.
Before COVID-19
During COVID-19
Coefficient plot of the linear and odds effects of variables on Bicing usage
The following figure summarizes the results, providing the model estimate (in terms of the log of the expected count of trips), the odds, the parameter uncertainty, and the credible intervals for the expected effects. The effects are considered to have higher relevance if the linear effect (odds ratio) is farther than zero (one).
The most relevant predictors of Bicing usage are bike-related built infrastructure (e.g., tactical urbanism), trip distance, and the income levels of neighborhoods.
For more details, click here to access the paper (open access).