The Big Data Transportation “Super-Network”

The Big Data Transportation “Super-Network”

by Lloyd Marino

Nam Jun Paik’s term “superhighway” is a getting a whole new subset of definitions—especially with reference to urban transportation. With the recognition that 60 percent of the world’s population will be living in urban areas within the next 20 or so years, the transportation “grid”—cars and mass transit—is ripe for new paradigms. Big Data will be key in the development of these approaches.

In the US, a very large number of people utilize personal automobiles to get to work. The car is foundational to the American way of life. It’s a truism that it’s a symbol of individuality and independence. However, it comes with a price: traffic jams, pollution, parking issues—huge wastes of time, money, and natural resources—with commuters losing 34 hours annually at a cost of 133 million dollars to the economy, all spent sitting still.


“The technological developments of the past couple of decades offer the prospect of a very different paradigm—mobility-centered around the user. According to Buzzcar and Zipcar founder Robin Chase: ‘The combination of the Internet, which holds the world’s knowledge; wireless, which gives us ubiquitous and low-cost access to it; and smartphones that make our interfaces portable and cheap, is transformational” (Digital-Age Transportation: The Future of Urban Mobility)

In other words, drivers (and ultimately all commuters) maintain their coveted independence while, at the same time, linking to an interdependent network of information that helps make that independence more efficient—helping both the individual and the communal reality.

Most people have experience with devices and applications that plot the shortest route to one’s destination with constant updates based on traffic flow: Google Maps, City Mapper, Qixxit are all examples. Other uses of Big Data to solve commuter issues include SFpark, where the city of San Francisco has installed networked parking meters—sensing the use of parking spaces in real time—allowing the user to know the availability of parking spaces and the parking manager to adjust prices accordingly(the goal here is to encourage people to take mass transit or drive at a different time of day).

A more encompassing and integrated application was designed by RideAmigos Corp. in Los Angeles:

Its dashboard allows users to compare alternatives—transit, ridesharing, bicycling, walking—for cost, time, distance, and carbon dioxide output; tracks commutes; provides options for buying transit passes; lists bike rack locations and shower facilities; matches carpools and vanpools within a user’s company or throughout the community; and provides business listings, weather, traffic alerts, and so on ( )

Big Data opens up the possibility of making the individual completely informed about all transport choices including costs and impacts. But, if you look beyond the surface here, it’s clear that while focused on the individual, Big Data opens up the possibility of linking the single car to public transport in a kind of transportation super-network. Ultimately such a goal will require the convergence of the public and private sectors with the Department of Transport hopefully working to promote and promulgate V2V and V2I technologies.

Today’s urban travelers are in the midst of a transportation revolution and Big Data is quietly leading it. Some things are clearly visible on the horizon:


  • Connecting individual cars to the public transportation network leading to more efficient ride sharing
  • Highways outfitted with sensors–information produced can be used to plan everything from pothole repair to traffic flow metering.
  • Wireless payment: apps that provide for swiping your smartphone for train and bus tickets and “pay as you drive” car insurance. London’s Oyster is an example of the viability of alternative forms of payment
  • Using data produced by cellphones to identify travel patterns—Google already uses location information for Google Maps—for advertising placement. This could be used to defray travelers’ costs.
  • Incentivizing transportation: for off-peak travel (UBER already does this); giving vouchers and certificates for restaurants and stores for using mass transit; sector surcharges and discounts for city travel (London already does this); using advertising revenues to make mass transit free;
  • Car ownership ultimately being replaced by “mobility services” –we see the beginnings of this with Zipcar.
  • Autopiloted cars—planes and trains already have mechanized systems piloting them. Google’s self-driving car and sensor activated safety systems (automatic braking for example) show us the long-range objective of removing human error from the transportation equation. (

Ultimately, what we are seeing is the nascent transformation of the urban transportation infrastructure. Given the inefficiency of the government, the private sector will have to do more to lead this transformation. It will most likely do so through the creation of new systems that replace “legacy systems” produced over decades of infrastructure building and repair and by developing applications that reward travelers and enhance their transportation experience. ( )

Image by: Matthew Wiebe

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