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Data analytics for transport planning: five lessons from the field

Tatiana Peralta Quiros's picture
Photo: Justin De La Ornellas/Flickr
When we think about what transport will look like in the future, one of the key things we know is that it will be filled and underpinned by data.

We constantly hear about the unlimited opportunities coming from the use of data. However, a looming question is yet to be answered: How do we sustainably go from data to planning? The goal of governments should not be to amass the largest amount of data, but rather “to turn data into information, and information into insight.” Those insights will help drive better planning and policy making.

Last year, as part of the Word Bank’s longstanding engagement on urban transport in Argentina, we started working with the Ministry of Transport’s Planning Department to tap the potential of data analytics for transport planning. The goal was to create a set of tools that could be deployed to collect and use data for improved transport planning.

In that context, we lead the development of a tool that derives origin-destination matrices from public transport smartcards, giving us new insight into the mobility patterns of Buenos Aires residents. The project also supported the creation of a smartphone application that collects high-resolution mobility data and can be used for citizen engagement through dynamic mobility surveys. This has helped to update the transport model in Buenos Aires city metropolitan area (AMBA).

Here are some of the lessons we learnt from that experience:

1. Technology and innovation require political champions

As in any other sector, innovation in transport requires time, and genuine commitment. Using innovation to leapfrog development does not mean automatically translate into quick and easy results. Instead, sustainably integrating technology into the transport planning process takes a lot of time, strong leadership, and adequate resources.

In our experience, continued support from Argentina’s Ministry of Transport was instrumental.

2. Cutting-edge innovation calls for cutting-edge skills

Leveraging data analytics for transport planning requires specific skills and knowledge that might not be available in the local job market.

The success of our activity was due in part to the creation of a specific unit within the Ministry of Transport, which brought together people with different profiles but a common interest interested in using data analytics to improve the planning process. The team worked side by side with technical consultants to develop data analytics tools and put them to the test in real interventions, allowing for an effective transfer of technology and knowledge.

3. Public agencies and private tech companies need to work hand in hand

Venturing into disruptive technologies can pose a number of challenges to government agencies. The world of technology and innovation is as volatile as it is fascinating, and it may be hard to justify the risk of investing public funds in solutions whose success is still uncertain. Likewise, many governments lack the technical capacity to experiment with cutting-edge technologies. To overcome these hurdles, partnerships with development agencies and specialized tech firms are essential.

This was certainly the case for us in Buenos Aires, where we teamed up with the Spanish Fund for the Latin American and Caribbean (SFLAC) and with companies like Korbato (data analytics), Vizonomy (software development), and Logit (transport planning models).

4. Changing environments require to move from linear to spiral development

Innovation is risky. However, there are ways to mitigate risks by changing the way products and ideas are developed. Traditional project development has been conducted using a linear method, which leads to underestimating turnaround times by an average 20-30%, and costs by as much as 100%. Our projects now are much more dynamic and full of unexpected challenges. To manage risk better, we included continuous prototype testing at various stages of project development (spiral method), allowing teams to make adjustments as they go along, minimizing risks and costs, and creating usable end products. 

5. Technology is the means, not the end

Innovation has little value unless it serves a greater purpose. In this project, technology is meant to support a comprehensive planning process with concrete outcomes:
  • The data we gathered for the project is now helping us assess the impact of the integrated fare system that Argentina implemented earlier this year.

  • We also used our models to evaluate the benefits of the forthcoming Regional Express Railway network, a transformative project that will connect all the main commuter rail lines serving the Buenos Aires Metropolitan Area through a new downtown tunnel.

  • The app will be utilized to complement the upcoming AMBA mobility survey. This could lead to a revolution in the way these surveys are conducted, providing more accurate results at a fraction of the cost. In addition, the new mechanism will make it easier to collect opinions from specific user groups on issues that are directly relevant to them (for example, asking women about their security concerns).
We cannot wait to see these transport projects come to fruition: advanced data analytics has played a key part in their design, and we are confident they will translate into better results. There is no doubt this approach can make a difference for the millions of Porteños who rely on public transport every day. That’s what “technology for purpose” is all about.