Can Big Data Make the Hyperloop Free?

Can Big Data Make the Hyperloop Free?

The Hyperloop, the super speed tram that would shuttle passengers between Los Angeles and San Francisco in about the time it takes to shower and grab a cup of coffee, is one of the most highly anticipated projects on the high tech transportation horizon. Conceptually speaking, a futuristic, ultra-high-speed transport system should be a luxury available only to the uber-rich. But it’s conceivable that the Hyperloop could be free for travelers, even in the face of an estimated $8 billion-plus price tag and endlessly rising transportation costs.

In a televised interview last spring on CNBC, Hyperloop Transportation Technology co-chief Dirk Ahlborn hinted that he was toying with the idea of a free-to-ride enterprise (based on a free-to-play video game system, which makes money through in-game purchases and upgrades).

On the surface, this seems like an unattainable—albeit laudable—goal since it’s only normal to assume that we’d pay through every orifice for a sci-fi worthy technology capable of zipping people along at 700 miles per hour. Couple this with the reality that few rail lines worldwide are profitable and even require government subsidies to stay afloat, the thought that a multi-billion dollar, space age transportation system could ever be free hardly registers.


Though a commercially viable Hyperloop is still some years off—the first excursions are slated for 2019 and a test track could be completed sometime next year—the talk about free high-speed rides is an intriguing one. But with such a crushing price tag, this futuristic ride model is going to have to quickly find some major revenue sources to make the project profitable.


In fact, used correctly, Big Data—information collected from multiple channels, and then combined and analyzed in such a way to new insights that can lead to better, more informed decisions—could make the dream of a free Hyperloop a reality. Here’s how.


From airlines to railways, the transportation industry is already hard at work collecting unprecedented amounts of data from sources such name records, transaction histories and shopping habits, pricing data, customer surveys, call center logs, twitter feeds, FB check ins and Instagram photos. Much of it is unstructured data, predictably. The transportation industry is behind the proverbial eight ball when it comes to predictive modeling, simulations and next-best-action decision making, three areas that could help operations runs more efficiently and target customers more effectively, making the free transportation dream a reality. The opportunity is there for the Hyperloop and its parent company, Hyperloop Transportation Technology, to derive more insights—and profits—from Big D.


Below are my top five ways for leveraging Big Data analytics to produce visualizations and predictive metrics that could turn all that raw, unstructured data into insights, creating both value and profit, making free high speed tram travel more than just a pipe dream.


  1. Knowing the customer and loyalty marketing: To the millions of weary folks forced to commute daily from point A to point B, travel is probably viewed more as a commodity than a luxury, or source of enjoyment. Used wisely, Big D can give us a comprehensive, 360-degree view of these commuters—gathering information about their likes and dislikes, habits, preferences, tastes, what they’re reading, what they search for online, etc.—helping create a seamless start-to-finish journey, but also creating multiple revenue streams. Simply having a company like ATT, Verizon or other wireless provider who could benefit from using the infrastructure provided by the Hyperloops mere construction could in return offer free Internet, or contracting with retailers to place targeted smart digital advertising in individual trams could improve commuter engagement at every touch point across the travel experience, fostering customer loyalty and heightening brand awareness, while also bringing in some serious revenue streams (The same wireless providers could also provide Internet access to remote areas where the Hyperloop travels just by having access to the infrastructure). Comprehensive customer profiles can help determine new ways to interact with individual commuters, as well as improve service delivery and develop targeted marketing and advertising strategies. A predictive analytics solution that personalizes and optimizes the commuter experience could deliver major insights into an individual’s behavior and habits. Leveraging these analytics will allow the Hyperloop Company (parent company) to target regular commuters with personalized, profitable offers—discount coupons to Starbucks, Barnes & Noble, Hudson News and other popular commuter haunts—creating a memorable travel experience, along some serious revenue.


  1. Capacity assessment and management: People are going to want to ride the Hyperloop, probably far more than the train can possibly hold. Since you can’t strap commuters to the roof of a train traveling almost as fast as the speed of sound, it goes without saying that capacity assessment and management will be an issue facing the Hyperloop, Indeed, capacity assessment and management are historically an transportation industry Achilles’ heal. The ability to analyze information with higher frequency — in near real time — can allow for optimized capacity planning and effective yield management. Advanced analytics can be used to optimize capacity planning, helping to detect and rectify gaps between planned capacity (the train will be 90% full during the two weeks leading up to the Christmas holiday) and actual demand (we have a waiting list for that same period).


  1. Predictive maintenance analytics: In the asset-intensive transportation industry, success depends on the safe and reliable performance of those assets. Capturing and analyzing operational data can help organizations/companies manage and maintain their assets to improve safety, performance and equipment life. With millions of passengers expected to ride the Hyperloop annually, there will be a need to expand infrastructure, while refining maintenance along thousands of miles of track down to a science.


Predictive maintenance analytics can lead to cost savings over routine or time-based preventive maintenance because tasks will only be performed when needed, allowing for the regular scheduling of corrective maintenance. Predictive maintenance analytics would provide the right information in the right time, helping determine which equipment needs maintenance, and better planning maintenance work, all but doing away with the concept of “unplanned maintenance.”


  1. Selling power back to the grid: Renewable energy is dammed efficient. From solar panels to wind turbines, renewable energy can produce more electricity than any entity could ever possibly use, even the proposed Hyperloop. In the U.S., the Public Utility Regulatory Policy Act (PURPA) dictates that electric utilities on the traditional power grid must purchase the excess electricity that renewable energy systems generate. It’s a way of encouraging renewable energy production without requiring utilities to invest in expensive renewable systems themselves.


Renewable energy producers make up for a lot of what they use by funneling excess energy back into the grid. Predictive analytics once again are key here, helping the Hyperloop receive compensation for the energy it returns to the grid. Most states only allow customers to generate enough energy to cover their own needs. However, through a process called net metering, businesses can produce energy during the day, feed it back into the grid, then get credited for the energy they produce.. Though customers can’t sell electricity directly to other customers—they’re constrained by having to send their electricity through the existing grid—they can turn a profit by selling renewable-energy credits, chits that clean-energy producers receive and sell to utilities, who, in turn, are able to meet their energy needs from renewable sources quotas.


  1. Using Big Data for freight: When you need freight transportation for long distances, hazardous materials, or extremely heavy items—including that new car you’ve been waiting for—shipping by train or boat generally saves both time and money over traditional truckload shipping. But here’s something you may not know. If the shipping containers on those trains and boats that are transporting that new car aren’t full then you might have wait longer than expected. Simply, half empty containers aren’t profitable and carriers won’t send them out. This is why businesses habitually pay for entire containers—even if they’re half empty. This wouldn’t be a problem for the Hyperloop. Freight ICT (information and communication technology), which offers virtually unlimited data collection, greatly enhanced predictive capabilities, and real-time, dynamic decision-making and implementation, could be used to improve the efficiency of the Hyperloop’s freight vehicle operations, including processes at entry and exit and making better use of the freight network.


On the horizon

Lost in all the talk about the Hyperloop is any substantive discussion about which cities will benefit from and have access to this high-speed technology. There are the obvious candidates, specifically large metropolitan areas with population, jobs, existing public transit, infrastructure, financial resources and freight needs to support such a technology. But shouldn’t smaller regional locales enjoy the same access and benefits? In my next blog, I’ll look at the ways small town USA could become a Hyperloop utopia.

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