Another Data, Another Dollar

In its current state, data can sometimes seem like another four-letter word to transit providers. The seismic shift of an industry toward its digital future might not hold the same glitz when you’re busy combing reports or pounding the pavement for the right in-house data talent. 

But tomorrow, give or take five years? The work of a handful of tech providers and payment processors alone spell massive shifts in visualization, open systems, machine learning, autonomous driving and the way we look at Singapore. Along with many more changes, there’s the chance that at least some transit providers will find gems for systems and riders among all that data.

“Pulling in these larger ecosystems of data gives agencies a better way to plan,” said Eric Reese, director of global strategy, Scheidt and Bachmann. “In some ways, they’re trying to understand the day in the life of the customer.”

In terms of capitalizing on data for smoother travel and happier riders, there are two civic “labs” under testing in Singapore and Chicago, according to Ian Slater, S.V.P., enterprise partnerships at Mastercard, a direct payment data provider for numerous transit agencies. Singapore, that unique southeast Asian city-state, is pouring funds into transit data projects like real-time rider tracking that could be the envy of the world. Desirable, though Slater said what is being tested in Chicago is closer to realistic for North American transit.

UI Labs, an agency that cooperates with the city and partners like Mastercard on distinct urban challenges, has spent months toying with data that surrounds a unified rider, transit and payment experience. Combining city, transit and third-party data, like payment processors or smartphone apps, UI Labs is assessing improvements to congestion that reward riders for beneficial behaviors. For instance, could a rider be encouraged to take an earlier CTA train with an inducement for their favorite coffee shop, thereby alleviating L lines at problematic times? (Slater said no rider is personally identifiable in these data profiles, and they would opt-in to participate.)

By fall 2017, there will have been enough data collected and quaffed for early results. From there, Slater said Chicago may have pioneering insight on what leads it and all other agencies to collect data in the first place.

“What do people consider to be useful and what do they consider to be noise? And what will help the city manage their transit system more effectively but also provide value for the rider?,” he said.

Seeing data is believing, from the perspective of Genfare. The makers of the first mechanical farebox 130 years ago, Genfare will introduce a new software-as-a-service visualization dashboard at the American Public Transportation Association's Expo in fall. Darren Dickson, president, said agencies can find major enhancements to data on hand when there are refined tools for modern reporting.

“As you look at visualization, you can get to the answer you’re looking for by seeing it in a different way,” Dickson said. “I’m not as concerned with data collection at this point as I am about using the information that has been collected.”

Further, Dickson said the mix of upgraded reporting and mobility will give transportation facilitators a picture of where and when riders end their trip, often a question-mark in their journeys.

On another front, more than one-quarter of transit agencies have an open data system. Chris Titze, senior associate and analyst at Cambridge Systematics, said he advocates for open data, as long as transit agencies don’t “sadly” view primarily as a cost-cutting measure. With the correct personnel on the IT and operations side, transit agencies can supersize data pools from an open approach. 

“It’s really something you buy into and a consortium approach: a group of individuals who agree on a methodology and software package and share the wealth,” Titze said.

Open systems tie into the aftermath of disruptions in travel from ride share providers like Uber and Lyft, said Anurag Komanduri, senior associate at Cambridge Systematics. Komanduri said slashes in prices from competition between ride share options is presently in a “a race to the bottom,” though he expects Uber and Lyft to pivot toward profitability. But the data structures, collection sources and analytic capabilities they create will be a wellspring for entities that can take advantage, not excluding public transit.

“The data and open systems now will better feed what’s next, for things like autonomous vehicles,” he said. “There is an ecosystem change and I don’t know what role transit will play.”

Scheidt and Bachmann, which makes solutions for parking, railway signaling and fare collection, is working on a future where the machines are talking to and teaching each other. Reese sees a compounding effect as agencies onramp to better collection of more quality data. From those operational efficiencies, transit agencies may be able to tap into machine learning functions based on advancing platforms and conduits to external, public data streams.

“You’re getting to customer prediction on high volume spikes in ridership, or where weather impacts [service],” he said, latter adding, “You’ll see more innovative things coming out of these agencies around saving money. … You’re talking about bus tracking systems that are able to know to re-route, give operators better decisions in real-time.”

Wherever the spectrum of technology takes transit, plus nearly every other industry, experts repeatedly touched on two themes to keep your sanity and career in the years ahead. On the tech provider side, it will “become incumbent … to continue to be innovative” in answering transportation and rider challenges, not just new bells and whistles, according to Reese.

From the perspective of transit providers, Komanduri said agencies with a clear goal can most nimbly weather whatever data challenges come down the road. It starts with operational statistics and a few key performance indicators. Agencies vexed by today’s data collection and analysis capabilities can then begin to set the bar a bit higher, with features like real-time snapshots of data, or the transit feed overlaid with a revenue feed, according to Mastercard’s Slater.

“Even doing that very basic thing is very valuable,” he said. It’s baby steps rather than trying to jump to the advanced analytics. The cities that struggle are the ones that try to do everything, all at once.”

Justin Kern is a freelance writer out of Milwaukee, Wisconsin.