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Data Analysis

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.

To measure the real impact of transport services, affordability needs to be part of the equation

Tatiana Peralta Quiros's picture

Differentiating between effective and nominal access

A couple of months ago, one of our urban development colleagues wrote about the gap between effective and nominal access to water infrastructure services. She explained that while many of the households in the study area were equipped with the infrastructure to supply clean water, a large number of them do not use it because of its price. She highlighted a “simple fact: it is not sufficient to have a service in your house, your yard, or your street. The service needs to work and you should be able to use it. If you can’t afford it or if features—such as design, location, or quality—prevent its use, you are not benefiting from that service.” To address this concern, the water practice has been developing ways to differentiate between “effective access” and “nominal access”—between having access to an infrastructure or service and being able to use it.

In transport, too, we have been exploring similar issues. In a series of blog posts on accessibility, we have looked at the way accessibility tools—the ability to quantify the opportunities that are accessible using a transit system—are reframing how we understand, evaluate, and plan transport systems. We have used this method that allows us to assess the effectiveness of public transport in connecting people to employment opportunities within a 60-minute commute.

Incorporating considerations of cost

Yet, time is not the only constraint that people face when using public transport systems. In Bogota, for example, the average percentage of monthly income that an individual spends on transport exceeds 20% for those in the lowest income group. In some parts of the city, this reaches up to 28%—well above the internationally acceptable level of affordability of 15%.

Media (R)evolutions: Global Mobile Data 2014 - Traffic Growth and Forecast

Roxanne Bauer's picture

New developments and curiosities from a changing global media landscape: People, Spaces, Deliberation brings trends and events to your attention that illustrate that tomorrow's media environment will look very different from today's, and will have little resemblance to yesterday's.

Every day, people create 2.5 quintillion bytes of data- this astonishing rate means that 90% of the data in the world today was created in just the last two years! 

Sources of data include mobile phones, tablets, the Internet of Things, and social media. Mobile technologies, in particular, have contributed to the growth of mobile data as new apps are created and used every day to to send text, make mobile payments, watch multimedia, or shop to name a few.  These activities all leave a digital footprint-- big data that can be analyzed. 

The graphic below illustrates recent global mobile data traffic growth by region and provides a forecast for the coming years: