Using Big Data to Understand Movement Around Buenos Aires

At a glance






The World Bank, Secretary of Mobility and Transport of the City of Buenos Aires, Federal Ministry of Transport, Nommon Solutions and Technologies, S.L.


Urban mobility; sustainable transportation; infrastructure; data alliance


Data provided by Waze App, Claro Argentina, Municipality of Buenos Aires


SDG #8, SDG #9, SDG #11, SDG #13

The Challenge

Shifts in travel patterns in the Metropolitan Area of Buenos Aires (AMBA) were amplified by lockdown restrictions imposed in 2020 due to the COVID-19 pandemic. Yet, transport policy responses, like previous ones, were enacted without sufficient data to comprehend the extent to which travel patterns have shifted, the underlying reasons for changes or the likelihood of further shifts. Data-driven monitoring of mobility behaviour by both public and private transport users is needed to inform meaningful policy measures on public transport and infrastructure.

The Approach

To better understand the impact of the COVID-19 pandemic on urban mobility, new data sources were leveraged. The research team, led by representatives from the World Bank, drew on multiple data sources including: mobile phone Call Detail Records (CDR) data to capture movement, taxi app Waze for Cities data to track changes in traffic congestion and a survey of current users of private motorised transport.

The Benefits

This project’s insights into past and current mobility patterns facilitate evidence-based policymaking to target interventions and improve transport services and infrastructure in Buenos Aires. This leads to broader access to urban public transport networks, improvements to heavily trafficked route sections and incentives for people to move about the city more sustainably. The project team was also able to leverage private sector data to propose a roadmap for entering into data access agreements with private entities in Buenos Aires.  This has relevance for the SDGs, particularly SDG 11 (Sustainable cities and communities) but also SDG 9 (Industry, innovation and infrastructure) via infrastructural improvements. This use case also addresses SDG 8 (Decent work and economic growth): economic growth is stimulated, and jobs are created in the transport sector. Finally, the project addresses SDG 13 (Climate action) as the promotion of sustainable mobility options will reduce greenhouse gas emissions.

Private sector data agreements

This project proposed a roadmap for entering into data access agreements with private entities in Buenos Aires.

The context​

In light of ongoing changes in urban mobility in Buenos Aires, the city's Metropolitan Transport Agency (MTA) identified the need to shift towards more demand-driven approaches and evidence-based strategies. This transition required policymakers to gain access to new data sources.

Leveraging big data: How it was implemented

Buenos Aires was able to build on prior experience with several data sharing initiatives, including:

  • A cross-licensing agreement between the Government of Buenos Aires (GCBA) and Google Inc. allowing the city to access anonymised private data to improve mobility management.
  • A transportation application programming interface (API) for developers that allows private companies continuous access to public data. This API can be configured as a mechanism for information exchange between companies and the GCBA.
Various international initiatives have been established to facilitate data sharing. The Development Data Partnership is one such global initiative, led by the World Bank and co-founded by UNDP. These initatives provide advice on how to define agreements and access to data as well as to data-driven products.

The following sections outline how Buenos Aires leveraged the different data sources.

1. Using WAZE for Cities

Waze Data is typically used for traffic management purposes and was, in this case, applied to mobility planning. A congestion index was calculated, measured by the kilometres of roads that are congested during a given time period as a share of total road network kilometres. Collaborating with Waze for Cities through the Development Data Partnership (DDP), the project focused on changes in congestion intensity. To this end, a spatial comparison of congestion indices was made between the central administrative district (known as the Autonomous City of Buenos Aires (CABA)), the wider administrative area of Greater Buenos Aires (GBA) and finally the even wider overall area of AMBA. Three distinct time periods were considered, namely October 2019 (pre-pandemic), October 2020 (pandemic) and October 2021 ("new normal").

Figure 1: Waze for Cities data analysis to track congestion over time.
Source: World Bank Group


We wanted to determine whether traffic intensity during peak hours differed for certain types of streets in 2019-2021. Thus, the project team analyzed changes in traffic intensity separately for highways, roads with bike lanes and roads near subway or rail lines. In doing so, the team uncovered that congestion in CABA increased more on roads near rail/metro lines in 2021, while in GBA it did so on highways (Figure 2). Overall, it appeared that the high congestion index during peak hours in CABA was driven primarily by congestion on those streets that were near rail/metro lines and/or had bike lanes.

Figure 2: Congestion index at peak rush hour (17:00). Types of roads vs. CABA/GBA averages.
Source: World Bank Group

2. Using Origin-Destination (OD) Matrices
To understand mobility patterns more accurately, this project further developed updated Origin-Destination matrices using mobile phone CDR as the primary source and combining it with complimentary data as shown below.

Figure 3: Use of CDR data and complementary data to construct OD matrices
Source: The World Bank

Data processing and analysis process

1.     Pre-processing and cleaning of mobile data;

2.     Analysis of network event data;

3.     Analysis of the network typology based on the locations of the antennas;

4.     Compilation of activity and travel diaries;

5.     Sample expansion to the total population; and

6.     Generation of OD matrices, segmented according to specified criteria (age, purpose of the trip, mode of transport).

Again, mobility patterns were assessed before the pandemic (2019), during the pandemic (2020) and in the "new normal" (2021). Within each period, three OD matrices were generated corresponding to weekday, Saturday and Sunday averages in the month of October. “SUBE”  contactless payment validations provided data for all modes of public transportation; by cross referencing with this dataset, we were able to compile trip matrices that were specific to public transportation.


The total number of trips taken on an average weekday in AMBA was found to remain significantly lower in 2021 than in 2019.

Figure 4: Total daily trips in AMBA in October 2019, 2020 and 2021 (in million).
Source: World Bank Group

While overall mobility appears to be recovering from a steep decline in March 2020, public transit ridership remains well below pre-pandemic levels, though compensated by an increase in motorised mobility and cycling.

Figure 5: Change in SUBE transactions and private vehicle flows on AMBA's motorways, 2020-2022 (2019 = 100).
Source: World Bank Group

Beyond that, there has been a progressive shift in travel patterns in AMBA over the last decade, with trips being more local in nature with fewer trips between the central (CABA) and outer (GBA) areas. This shift has led to a consolidation of the role of buses relative to other modes of public transport, with urban and provincial bus routes gaining in importance. Finally, ridership on the CABA bike-share system has been steadily growing since its inception just over a decade ago, with the long-term upward trend in passenger numbers continuing in 2020 and particularly in 2021.

3. Using complementary survey of individual motorised transport

This study involved a comprehensive survey of individual motorised transportation users in AMBA in November and December 2021. This aimed to shed light on current travel patterns, changes compared to pre-pandemic times and primary reasons for travellers’ choice of transportation mode. The data collection was conducted in close collaboration with the City of Buenos Aires. Surveying took place at 25 locations (gas stations and parking lots) throughout CABA, adjusted to the approximate traffic volumes at those sites.

According to the mobile phone analysis, motorised personal transportation accounted for roughly 53 percent of all weekday trips in 2021. We now know that this increase came almost entirely at the expense of public transport as, according to the survey, about 11-13 percent of current car and motorcycle users in AMBA have switched to exclusively motorised transportation since the onset of the pandemic.

How can better data contribute to better policy? ​

Evidence from various analysis of the new data informed actionable recommendations for effective measures to shape mobility in Buenos Aires. The decisions transportation planning authorities make next will influence which travel patterns will remain permanent. Beyond investments in infrastructure being critical to building confidence in public and active transportation, pricing and regulatory policies may provide further incentives to promote less energy-intensive transportation behaviours.

Specifically, new data insights provide support for the following:

  1. In the past, price signals have favoured public transit, however, these incentives must be combined with service and infrastructure enhancements to succeed. Ridership in AMBA has positively responded to changes in traffic flow and improvements in punctuality in the past and the survey results indicate that a travel time savings of 5 minutes by public transportation over private alternatives would cause 56 percent of current private transportation users to switch to public transit.
  2. Middle-income neighbourhoods in AMBA that are located along freeways and public transit lines offer the best opportunities for the largest shifts to public transportation. But by targeting low-income populations, rail investments in the Belgrano Surline are likely to improve accessibility to jobs and services while promoting the use of public transportation.
  3. Our spatial analysis suggests reorganising services to avoid the current overlap of national jurisdiction bus lines in AMBA. The introduction of on-demand bus service in low-demand areas and improved integration with rail services could lead to a more effective and responsive transportation system. One could replace underperforming routes while maintaining service to citizens in those areas and increasing it in higher demand areas.
  4. One potential solution would be to implement cross-subsidisation through the Metropolitan Transport Agency. A small surcharge on road tolls could generate funds for infrastructure improvements that benefit public transport.

Where do we go from here?​

Ultimately, the project has demonstrated the value of evidence-driven decision making and collaborative efforts in achieving sustainable and demand-oriented urban transport systems. The project has made significant contributions to transport planning in Buenos Aires by establishing a data-driven approach that can be replicated in other cities in Argentina. The roadmap for data access agreements below highlights the potential for effective collaboration between the public and private sectors, underscoring the importance of leveraging commercial data.

Figure 6: Roadmap for the establishment of data access agreements with private entities in Buenos Aires.
Source: World Bank Group

Case Downloads

Buenos Aires Transport Demand Assessment: Leveraging big data to understand the changing mobility patterns

Further ressources

Argentina Transportation Statistics
Dec 2022
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