Thinking About the “Gaps” in Urban Mobility Research: Susan Shaheen on Data Security, Privacy and Sovereignty

Susan Shaheen, iomob advisor and shared mobility expert.

Lily Maxwell, smart cities content writer for, interviews Susan Shaheen Ph.D., a pioneer in future mobility strategies. She was among the first to observe, research, and write about changing dynamics in shared mobility and the likely scenarios through which automated vehicles will gain prominence. She is an internationally recognized expert in mobility and the sharing economy.

People talk a lot about data — and in particular, data security, privacy, self-sovereignty, and big data — at the moment. How can the academic and research sector go about collecting data in the right way to make urban mobility more equitable and sustainable?

Susan: The data question is a really important one. First of all, big data are not nearly as “amazing” as they are messy. It helps to know what you are looking for in a big dataset, and you almost always end up discarding a lot of erroneous data.

There are two key fundamental issues at play in private-sector mobility data: first, the proprietary interests of the private sector and second, PII (or the Personal Identifiable Information of the individual). These are both real issues that have to be carefully navigated. These considerations apply to most, if not all, entities working with these data: academics, companies, individuals, and governments.

In research, figuring out how to access and manage sensitive data is something we need to work on. At present, we have to introduce a lot of obscurity to protect privacy and proprietary considerations. In other words, we have to go to higher levels of aggregation in our analysis. This offers benefits but can limit our understanding.

I have some ideas about how we might work on this. One way is to find new methods of collecting data from individuals at the community or civic level. We could ask people to opt-in to data collection on their mobile devices to allow us to access their travel behavior data. This would remove proprietary concerns because individuals would directly choose to share their travel data to support the public good. This would also allow us to link their movements and socio-demographics, which would help in furthering our understanding.

The idea of using geofencing could also help. Many airports in the U.S. “geofence” around the airport and have a great deal of detail about what’s happening within their limits. In a similar vein, we could potentially use geofencing to collect data on travel patterns within a city. Here again, the user could opt-in, and this could help to avoid proprietary concerns. With these approaches, we would not have to ask companies to provide the data. Instead, we could communicate directly with citizens and ask them to share their data to benefit the transportation system, accrue incentives, or both.

What do you think of iomob’s potential plans to use a tokenised opt-in system for their data? How do you think this will work?

Susan: The data sovereignty question is a big one now, and a lot of people are coming up with different ways to give people back ownership of their data. “Tokenising” data, for example through blockchain-based technologies, is one way for us to transition to a world with greater data sovereignty.

Within the tokenising realm, there are different approaches. The first approach is to ask citizens to “philanthropically” give access to their data. The individual could decide: “I opt-in and give my data to support the overall good.” This is the current model that we see with many online mobility platforms, which ask users to “agree” to give access to their data — most often in exchange for access to something. The second approach takes a more economic perspective, enabling customers to commodify the value of their data and sell it for tokens or real money. Trying to change the system raises difficult questions though. Iomob is essentially asking if people should be involved in the data value chain.

At present, a lot of data are being extracted from people, and they’re not receiving anything in return for it or even understanding how it is being used. A classic example of this is social media: every post we make, everything we click on, is recorded, and the tech company owns all of these data points. They can then sell it to third-party intermediaries and/or use it to advertise and sell products to us based on our “perceived” preferences. Perhaps this is the price of connectivity.

While tech has the potential to unite us, many questions remain over whether it is also being used to divide us.

While tech has the potential to unite us, many questions remain over whether it is also being used to divide us. Are we being exploited by giving our data away for free without thinking about it? These kind of questions are driving new models of collaboration in terms of how we do business or how we build ecosystems or platforms. The idea of iomob is to give people back control of their data — to choose if they turn it into tokens, volunteer it, or sell it. The philosophy is that this is not for big tech companies to decide. This is the spirit of blockchain: it’s a decentralising, democratising technology (in principle), so it is not surprising that is looking to prioritise data sovereignty.

You’ve talked in the past about addressing the ‘gaps’ in the data and making sure that the research being done on mobility, and particularly shared mobility, is asking the right questions. How and why do we have to ensure these things?

Susan: If we do not peel back the layers of data to discover gaps or blind spots (including possible built-in biases), we can miss a lot. In other words, when we aggregate at too high a level or generalize too much, we can lose critical insights, particularly on a geospatial scale.

The scale at which you examine data affects what we learn. When we were working on a smart cities proposal with the city of San Francisco, we found that the city’s smart mobility initiatives had different spatial dimensions, which varied according to the problems the city wanted to solve. Some were very neighbourhood-focused, some citywide, and others regional. Each approach was based on a different type of thinking and methods for planning and implementation.

We also need to take into account other factors when trying to understand how different mobility strategies are impacting different societies or demographic groups in the same nation. For example, when Uber entered Saudi Arabia, it opened up a whole new world of mobility for women there because they could suddenly travel ‘on their own’ (with a male Uber driver) for the first time. In contrast, we’ve also seen studies that suggest that women are being taken on longer ridesourcing/TNC rides in the U.S — perhaps so the drivers can talk with them longer. Gender is resulting in different outcomes in these two cases.

The big data age is exciting because we now have the ability to conduct more sophisticated analysis, giving us a more robust understanding. This understanding should help us to develop policies that are more nuanced. To make sure we are not missing key elements or gaps, we can benefit from frameworks to help guide us.

Awareness is an important first step, but then we can employ more concrete approaches, such as the STEPS framework, to help flag possible gaps. STEPS stands for Social — Temporal — Economic — Physiological — Social. You can learn more about this framework for examining transportation equity in USDOT’s report “Shared Mobility and Transportation Equity”.

Read Part 1 of this interview:

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