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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.