Data-driven analysis of the impact of COVID-19 on Madrid's public transport during each phase of the pandemic

Abstract

COVID-19 has become a major global issue with large social-economic and health impacts, which led to important changes in people's behavior. One of these changes affected the way people use public transport. In this work we present a data-driven analysis of the impact of COVID-19 on public transport demand in the Community of Madrid, Spain, using data from ticket validations between February and September 2020. This period of time covers all stages of pandemic in Spain, including de-escalation phases. We find that ridership has dramatically decreased by 95% at the pandemic peak, recovering very slowly and reaching only half its pre-pandemic levels at the end of September. We analyze results for different transport modes, ticket types, and groups of users. Our work corroborates that low-income groups are the most reliant on public transportation, thus observing significantly lower decreases in their ridership during pandemic. This paper also shows different average daily patterns of public transit demand during each phase of the pandemic in Madrid. All these findings provide relevant information for transit agencies to design responses to an emergence situation like this pandemic, contributing to extend the global knowledge about COVID-19 impact on transport comparing results with other cities worldwide.

Keywords: COVID-19; Madrid; Public transport; Ridership; Ticket validations.

Fig. 1

Different phases of COVID-19 timeline in the Community of Madrid, Spain.

Fig. 2

Daily number of validations in Madrid.

Fig. 3

Weekly relative change in ridership with respect to the baseline reference.

Fig. 4

Heatmap of topmost 50 stations ordered by ridership in the Community of Madrid.

Fig. 5

Weekly relative change of daily number of validations per transport mode compared to reference.

Fig. 6

Weekly relative change of daily number of validations per ticket type compared to reference.

Fig. 7

Weekly relative change of daily number of validations per user type compared to reference.

Fig. 8

Weekly relative change of the number of active cards per day and validations per card compared to reference.

Fig. 9

Different phases of COVID-19 timeline according to the State of Alarm.

Fig. 10

Average daily ridership patterns in the Community of Madrid (15-min validations).

Fig. 11

Average daily ridership patterns per transport mode in Community of Madrid (15-min validations).

Fig. 12

Weekly relative change of daily number of validations in Pozuelo (high-income municipality) and Fuenlabrada (low-income municipality) compared to reference.

Fig. 13

Average income vs. relative change in ridership per district in Madrid City (during the State of Alarm period).

Fig. 14

Evolution of public transport users in Community of Madrid since 15 January to 27 September (source: www.moovit.com).