How to send hyper-local transit alerts 🗺🎯👈

Email sent: Jul 17, 2019 3:28pm

Smack the sand out of your birkenstocks. Up the brightness on your iPhone to beat that beach glare. It’s summer at Transit! And you know what that means? Vacation??? For us?!? With the deluge of back-to-school riders, only months away? Ha-ha-ha-ha more like full steam ahead… 🚂💨🚴💨🏃💨


Real-time transit data is important. Tracking buses and trains, though? That’s just the start. Riders down the line want more than precise vehicle positions — they want precise ETAs for their stop. Yet so many things can mess up a predicted ETA. Traffic. Bad weather. Disruptions. Spontaneous bus conga line.

We’ve pursued several ways to improve real-time data. Working with agencies to fix buggy feeds faster. Updating vehicle positions more frequently, with GO crowdsourcing. Improving ETA predictions with fancy math from our friends at Swiftly. Yet in developer circles, there’s been whispers of a new way to predict extremely complicated phenomena: machine learning.

Basically, you build a ridiculously-smart robot, feed it a ridiculous amount of historical data, and it’ll spit out an equally-ridiculous Multivariable Equation™ that closely fits those data points. In our case, those data points were transit ETAs.

TL;DR: We built a machine learning algorithm for predicting transit ETAs. Then we implemented it in Montreal, for our partners at the STM. Before, ~25% of agency-supplied ETAs missed the mark. But when we turned machine learning on? We cut that ETA inaccuracy by half, overnight.


Wanna know how accurate your agency’s predictions are?
Just ask, samurai. 🤺💫✉️


What distinguishes Transit from other apps isn’t just our good looks 💁fast customer service 🐎 or reckless use of emojis 😅. It’s our obsession with data. Besides real-time ETA data, there’s disruption data: figuring out which lines are down, alerting the affected users, and guiding them to the best alternative trip.

With new stop-based alerts, we show exactly which stops have disrupted service, and route riders around them. So you won’t get caught unawares when Transit says your line has “good service” but the vehicle zooms by your (out of service) stop.*

* However: Not nearly enough agencies supply data at the “stop” level. So we’re going one step further. Email us! We’ll hook you into the Transit dashboard where your agency team can quickly and easily add stop-based alerts. Make sure the right riders are always informed. 💪


Increasingly perfect real-time data? Increasingly perfect disruption data? We both want it. But it takes two to DaTaNgo! So get in touch 👉🕺📨


When you give a mouse a cookie (of real-time data and reliable service alerts) they’re going to ask for a glass of milk (mmm 😋 refreshing mobile tickets). We’re streamlining the trip experience so riders don’t have to download an additional ticket app, and so they can pay for transit in the same app where they find all their mobility options, like bikeshare, carshare, etc.

So! Guess who’s bringing Transit mobile tickets to their city? Say o-hi-o to EZfare tickets from Masabi for TARTA (Toledo) 👋 PARTA (Portage County) 👋, and Laketran (Lake County) 👋 with more soon, including Akron, Cincinnati, Northern Kentucky, and... you?

Far from Ohio? Oh no problem. We can support all ticketing platforms. Just ask.

Wanna see how your riders can get a sanity-saving ticketing experience? Check out this 🤓👉💼 case study about our first ticketing city: St. Catharines, Ontario.

There’s so much more to say. But the tide is calling our name… time for a quick swim! Next up: new cities, new services, and a new multimodal announcement sweeter than strawberry-vanilla beach shack soft serve. 🍦


Back in a jiffy.

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