Culture Eats Transport Technology for Breakfast

A bus driver chats on his mobile phone in Bangkok, Thailand.
A bus driver chats on his mobile phone in Bangkok, Thailand.

By Katja Schechtner

Stop looking for the magic button. Demand that global transport technology is adapted to your local mobility culture, and help data scientists to make your city flow.

We are living in a data rich world. So we are told.

There are data about where we go, when we eat, and who we meet. What we like, how we spend, and when we sleep. Data in layers upon layers about ourselves, our cities, our every movement.

An abundance of media articles and research papers speak about sensors, apps and the opportunity to monitor and understand all human behavior. In 2013 there was one CCTV camera for every 11 people in the UK. As of October 2014 there existed more SIM cards than people, and a quarter of them were used for machines talking to other machines. More than 1,300 operational satellites roam the orbit, while 1.5 billion Facebook users and 540 million WeChat subscribers share their lives on social media.

And we are living in a people-rich world.

As we we can see ourselves every time we step out onto the busy streets of Tokyo, Manila, New Delhi, and Jakarta, teeming with people, bikes, jeepneys, cars, rickshaws, tricycles, pushcarts, trolleys, and many more.

According to the 2015 UN World Population report, 4.4 billion people—or 60% of the global population—live in Asia. By 2030 more than 55% of those people will be urban citizens, and 12 of the world’s 23 biggest cities and eight of the 10 most densely populated cities will be located in Asia. 


So it’s easy. Ask a question. Hit a button. Big Data will give you the right answer for all your transport problems.  

Why are we still waiting?

In some of the richest economies big data can already help to better understand, plan and operate transport, but in most developing countries there is a lack of data, a lack of tools and methods to understand the available data, and many political and institutional decision makers shy away from sharing the resulting information (easily readable maps and graphs) with the general public. 

Even if we ask relatively easy questions about urban transport—such as: “How many kilometers of roads, rail lines and shipping channels connect our cities?’, ‘How many buses, trains, rickshaws, cars, and bicycles are available to our citizens?’, or ‘How far and how long do they have to travel to reach their jobs, schools, shops and other community infrastructures?’—we quickly realize that we are essentially blindfolded.

Contrary to media-fueled expectations, data about Asia’s transport infrastructure, services and workforce are scarce, fragmented, and rarely shared. The few available data sets are heavily overused for questions that they cannot really help answer.

Culture eats transport technology for breakfast.

Most people, both experts and the general public alike, expect technology to work universally and systems to scale globally.

However, the failure to understand and react to the specifics of one urban population over the other and to underestimate how both sensors and software operate based on assumptions that are influenced by cultural experiences and expectations may ultimately have severe impacts of the daily lives of citizens.

There are places where ‘robots’ go to die – and we need to make sure to limit the number of those graveyards, when implementing projects for the poorest. Some the methods and tools that were developed for richer, Western countries cannot be simply transferred and used to understand urban mobility in poorer Asian countries.

One example is the use of mobile phone data for understanding daily mobility behavior in cities. The methods researchers have developed are based on a few assumptions that are intrinsically linked to the societies where they were developed.

Some of those cultural expectations are that a phone always belongs to the same individual, who keeps it with him/her, and that it is not shared with other members of the family nor is frequently bartered to get money for more urgent needs, etc. It is also assumed that people always sleep in the same spot, where also the phone rests for the night, thus identifying a stable ‘home’ location. There is another reason Western analytics fails – most mobile phone contracts in Asia are pre-paid, (85% of all connections in the People’s Republic of China and Southeast Asia were prepaid in 2013, and 95% in India, Pakistan and Afghanistan). Mobile phone operators do not store the data of each call, thus saving on storage cost and billing software. If nevertheless the available data from those service providers are analyzed to understand the transport behavior of an urban population, it just shows the trails of the few rich who can afford bank accounts and post-paid contracts, but it does not draw a full picture of the mobility flows of all citizens.

While we are still waiting for the magic Big Data button to deliver, local initiatives demonstrate a way forward.

Those projects pair technology with an in-depth local understanding of the system they want to improve and fully understand. Each of the three factors (data collection, data analysis and data visualization) requires locally adapted strategies for acquiring the data and interpreting the information, while still working hard to make their results available across larger scale platforms.

Successful examples are abundant and ready for scaling up: mapping informal transport via a tailored app in Cebu, Philippines, careful analysis of the mobile phone data records of Dhaka to plan future public transport corridors, and developing customized tools to reliably use low-cost satellite images for planning bus rapid transport systems in Pakistan. On a global scale MIT’s Civic Data Design Lab is collaborating with Google to put informal transit on the map, and ADB is currently spearheading the compilation of transport data covering Asia and the Pacific into a digital database.

So. Stop looking for the magic button. Demand that global transport technology is adapted to your local mobility culture and help data scientists to make your city flow.

This blog builds on the chapter “Big Data for Urban Change – Debunking the Myth & A Way Forward” in the forthcoming book: “Urban Change,” Anton Falkeis, De Gruyter (eds.), 2016.