In mid-January, the Transportation Research Board (TRB) of the National Academies held its 94th Annual Meeting in Washington DC. The conference attracted around 12,000 transportation researchers and practitioners from around the world. During the meeting, the TRB Executive Committee held a policy session on the impact of big data on transportation systems. I was one of three panelists invited to the session. Each of us made a short presentation which was then followed by an extensive discussion between the panel and the Committee.
Big data and related information-based disciplines, - e.g., data science, artificial intelligence, - are everywhere. Why are we so excited about them? Is it mostly hype, or is there something truly profound going on? In my presentation, I tried to briefly address these questions based on three key observations.
Big data is part of both the digital revolution of the past few decades and the scientific revolution of the past few centuries. Big data is now central to so many areas for the simple reason that there is so much more digital information than ever before. This could not have possibly happened without the digital revolution, which thanks to Moore’s Law has made it possible to drastically lower the costs of storing and analyzing the oceans of information we now have access to, - from mobile devices, smart sensors, social media, Web interactions and just about anything and anyone.
The Internet of Things (IoT) is truly taking off, already comprising about 25 billion smart devices, a number that’s expected to more than double over the next decade, and continue to keep growing for the foreseeable future. These devices generate massive amounts of data, with a lot more to come.
But big data should not only be framed as part of the digital revolution of the past few decades. It’s also a major part of the scientific revolution of the past few centuries. Scientific revolutions are launched when new tools make possible all kinds of new measurements and observations, e.g., the telescope, the microscope, spectrometers, DNA sequencers. Our new big data tools now have the potential to usher an information-based scientific revolution.
“Computers and the Internet certainly aid big data by lowering the cost of collecting, storing, processing, and sharing information,” write Economist editor Kenneth Cukier and Oxford professor Viktor Mayer-Schönberger in The Rise of Big Data: How It's Changing the Way We Think About the World, a 2013 article in Foreign Affairs. “But at its heart, big data is only the latest step in humanity’s quest to understand and quantify the world… Ultimately, big data marks the moment when the information society finally fulfills the promise implied by its name… All those digital bits that have been gathered can now be harnessed in novel ways to serve new purposes and unlock new forms of value.”
We can now leverage science and technology to help us better understand people-centric, sociotechnical systems. Beyond things and the physical world all around us, big data makes it possible to address complex sociotechnical systems, that is, systems that involve people as well as technology. Such systems have to deal not only with tough technology, hardware and software issues, but with the even tougher issues involved in human behaviors and interactions.
Sociotechnical systems have major societal, political and economic implications, e.g., health care, education, government and cities. They exhibit dynamic, unpredictable behaviors as a result of their highly complex interactions, which make them really hard to understand and control. But, increasingly, we’ve been developing the technologies, tools, engineering methodologies and scientific principles to be able to better design and manage such systems.
“This is the first time in human history that we have the ability to see enough about ourselves that we can hope to actually build social systems that work qualitatively better than the systems we’ve always had,” said MIT Professor Alex “Sandy” Pentland in an online conversation, Reinventing Society in the Wake of Big Data.
“I believe that the power of Big Data is that it’s information about people’s behavior - it’s about customers, employees, and prospects for your new business, . . . This Big Data comes from location data from your cell phone and transaction data about the things you buy with your credit card. It’s the little data breadcrumbs that you leave behind you as you move around in the world… and by analyzing this sort of data, scientists can tell an enormous amount about you. They can tell whether you are the sort of person who will pay back loans. They can tell you if you’re likely to get diabetes.”
Everything is now connected and smart. My third observation is that we are increasingly living in a smart, connected planet. We now have the ability to instrument, measure, sense and interconnect just about anything we care about, including physical things like cars, appliances, roadways and pipelines, as well as entire ecosystems like supply chains, business processes, cities and healthcare networks.
This gives us the ability to gather huge amounts of information, much of it in real-time, about the state of the world. And then, by analyzing all that information using powerful supercomputers and sophisticated algorithms, we can infuse all these things, processes, and ecosystems with intelligence. This kind of information-based intelligence will hopefully help us make companies, industries, organizations and economies more efficient, productive and responsive. Not surprisingly, this is turning data science into a hot new profession and academic disciplines, and it has finally unleashed the long awaited success of artificial intelligence.
In a recent Wired article, The Three Breakthroughs That Have Finally Unleashed AI on the World, author and publisher Kevin Kelly wrote that this ubiquitous intelligence will be a kind of “cheap, reliable, industrial-grade digital smartness running behind everything, and almost invisible except when it blinks off… It will enliven inert objects, much as electricity did more than a century ago. Everything that we formerly electrified we will now cognitize. There is almost nothing we can think of that cannot be made new, different, or interesting by infusing it with some extra IQ.”
The policy session discussions were summarized for the TRB Executive Committee by Stewart Fotheringham, professor in the School of Geophysical Sciences and Urban Planning at Arizona State University. His summary included eight major big-data-related issues that require further attention given their potential impact on transportation systems of all kind.
- Privacy. “We have the ability to track people 24/7. How do we weigh the pros vs the cons of this? How much surveillance are individuals willing to put up with?”
- Representativeness. “Social media data is NOT representative of the general population. Cell phone data is NOT representative either, given provider coverage and demographics.” There are also a number of other important data sources to be considered.
- Insight vs Data Volume. “Does more data always equal better insight? Can data confuse rather than clarify? Using large, hyper-dimensional data sets to make more informed decisions is the real challenge.”
- Data Quality. “How do we ensure we get good quality data from unregulated sensors, e.g. openstreetmap vs National Mapping Agencies, cf Wikipedia?”
- Moving from Deductive Reasoning to Inductive Reasoning. “Data-driven analysis is now becoming the norm and theory is taking a back seat – good or bad?”
- Just because you identify a problem, doesn’t mean you solve it. “With traffic congestion, for example, how do you get people to modify their behavior? And then what happens if everyone does?”
- There will be winners and losers in the Big Data Era. “Winners: those who take advantage of new technology and data e.g. Taxis – Uber; Delivery services; tourist industry. Losers: Those who are currently gatekeepers to data, information and knowledge, e.g. travel agents, retailers national mapping agencies.”
- How do we ensure that Big Data is a force for Good? “Given most data relate to locations, we are entering Big Brother Era. Some surveillance is good; some is bad. How do we decide limits? Who decides?”
“The Big Data era is here,” was Professor Fotheringham’s overall conclusion. “Big Data in transportation typically involves knowing where things and/or people are. Knowing where things are is fairly uncontroversial; knowing where people are isn’t. There is a need for further research.”