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Big Data

When cities forget about pedestrians, big data and technology can serve as a friendly reminder

Bianca Bianchi Alves's picture
Photo: Lazyllama/Shutterstock
Paraisópolis, a nationally famous slum area in São Paulo, Brazil, is one of those bustling communities where everything happens. Despite being located in the middle of the city, it managed, unlike other poor slum areas, not to be reallocated to make room for more expensive housing or public infrastructure. The area boasts vibrant community life, with more than 40 active NGOs covering issues that range from waste management and health to ballet and cooking. Recently, the area also benefited from several community upgrading programs. In particular, investments in local roads have facilitated truck access to the community, bringing in large and small retailers, and generating lively economic activity along with job opportunities for local residents.

As we continue our efforts to increase awareness around on-foot mobility (see previous blog), today, I would like to highlight a project we developed for Paraisópolis.

While most of the community has access to basic services and there are opportunities for professional enhancement and cultural activities, mobility and access to jobs remains a challenge. The current inequitable distribution of public space in the community prioritizes private cars versus transit and non-motorized transport. This contributes to severe congestion and reduced transit travel speed; buses had to be reallocated to neighboring streets because they were always stuck in traffic. Pedestrians are always at danger of being hit by a vehicle or falling on the barely-existent sidewalks, and emergency vehicles have no chance of getting into the community if needed. For example, in the last year there were three fire events—a common hazard in such communities—affecting hundreds of homes, yet the emergency trucks could not come in to respond on time because of cars blocking the passage.

How does accessibility re-frame our projects?

Tatiana Peralta Quiros's picture
The increasing availability of standardized transport data and computing power is allowing us to understand the spatial and network impacts of different transportation projects or policies. In January, we officially introduced the OpenTripPlannerAnalyst (OTPA) Accessibility Tool. This open-source web-based tool allows us to combine the spatial distribution of the city (for example, jobs or schools), the transportation network and an individual’s travel behavior to calculate the ease with which an individual can access opportunities.

Using the OTPA Accessibility tool, we are unlocking the potential of these data sets and analysis techniques for modeling block-level accessibility. This tool allows anyone to model the interplay of transportation and land use in a city, and the ability to design transportation services that more accurately address citizens’ needs – for instance, tailored services connecting the poor or the bottom 40 percent to strategic places of interest.

In just a couple of months, we have begun to explore the different uses of the tool, and how it can be utilized in an operational context to inform our projects.
 
Employment Accessibility Changes in Lima,
Metro Line 2. TTL: Georges Darido

Comparing transportation scenarios
The most obvious use of the tool is to compare the accessibility impacts of different transportation networks. The tool allows users to upload different transportation scenarios, and compare how the access to jobs changes in the different parts of the city. In Lima, Peru, we were able to compare the employment accessibility changes that were produced by adding a new metro line. It also helped us understand the network and connectivity impacts of the projects, rather than relying on only travel times.

Understanding spatial form
However, the tool’s uses are not limited to comparing transport scenarios. Combining the tool with earth observation data to identify the location of slums and social housing, we are to explore the spatial form of a city and the accessibility opportunities that are provided to a city’s most vulnerable population.  We did so in Buenos Aires, Argentina, were we combined LandScan data and outputs from the tool to understand the employment accessibility options available to the city’s poorest population groups.

What does Big Data have to do with an owl?

Nak Moon Sung's picture
This is the story of an owl, but not any owl. This owl is from Seoul and it came into existence thanks to Big Data. How come, you may ask? Well, read on to find out.
 
 Meet your new friend: the owl bus

Officials in Seoul had long searched for a transport system for low-income workers who commute late at night. Although a taxi ride was an option, it was a very pricey one, particularly for a commute on a regular basis. Low-income workers do not make enough money to take a taxi regularly, and taxi fares are considerably higher at night. Furthermore, since low-income workers tend to live on the outskirts of the city, taxi drivers often are reluctant to go there mainly for distance and security reasons. 

These were some of the big challenges faced by policy makers in Seoul, a city regarded as a champion of public transportation. So what to do?

Part of the solution was the analysis and utilization of Big Data to come up with a suitable mode of transport that would serve the specific needs of late-night workers. The result was the creation of the “owl bus,” which operates late into the night until five o’clock in the morning.

In this context, Big Data has a considerable potential application in the transport sector, and for infrastructure development in general. In fact, World Bank and Korean officials will discuss on Tuesday, May 28 the theme “Leveraging Information Communication Technologies (ICT) in transport for greener growth and smarter development.”

“Smart mobility” for developing cities

Ke Fang's picture
Follow the author on Twitter: @KeFang2002
 

In many developing cities, transport infrastructure – whether it be roads, metro systems or BRT - is not growing fast enough, and cannot keep up with the ever-increasing demand for urban mobility. Indeed, constructing urban transport infrastructure is both expensive and challenging. First, many cities do not yet have the capacity to mobilize the large amount of funds needed to finance infrastructure projects. Second, planning and implementing urban transport infrastructure projects is tough, especially in dense urban areas where land acquisition and resettlement issues can be extremely complex. As a result, delays in project implementation are the norm in many places.

Therefore, solving urgent urban transport problems in these cities requires us to think outside the box. Fortunately, the rapid development of ICT-enabled approaches provides a great opportunity to optimize and enhance the efficiency of existing and new urban transport systems, at a cost much lower than building new infrastructure from the ground up.

Big Data comes to transport planning: how your mobile phone helps plan that rail line

Shomik Mehndiratta's picture
Understanding peoples’ travel activity patterns, and ideally understanding the motivations and choices underlying them, are at the heart of what transport planners do. 
 
An understanding of trip origins and destinations – and how trip-makers select routes, modes and destinations – are required to plan extensions or changes to a road or public transport network; to assess the viability of a new investment; and to assess how well the existing transport system is serving the population and businesses in a specific area.

​Indeed, in our roles as transport specialists in the World Bank, much of our job is about supporting clients’ ability to develop this understanding and to use the results to evaluate and appraise investments.
 
At the core of this process is an Origin-Destination (OD) survey: essentially a matrix of trips between different zones of a region (referred to as an OD matrix).  Traditionally, getting this information in the context of an urban area has been a difficult, expensive and time-consuming process. We are often talking about millions of dollars for trip activity surveys of thousands of households, complemented by extensive analysis of socio-economic data. We also count data at strategic points on major roadways and transit routes to calibrate the results. 
 
This process can take up to a year, and many stages need very specific technical skills and a lot of quality control.  Survey design, sample design, training the surveyors, ensuring they are accurate (not making up data, not entering data erroneously), and subsequent stages of analysis all require significant technical capacity to implement, as well as an almost equal level of technical skill to supervise the work. 
 
All of us who do this have horror stories from processes on which we have worked. The result is that the basic information needed to test alternatives and make decisions about transport investments is collected too rarely – at best no more than once every decade – and even the results of existing surveys have suspect deficiencies.