Real-time app review: Bus Detective

bus-detectiveThe first proper real-time app using SORTA’s new data has finally been released! It’s name is Bus Detective, and it’s available for Android, iwidgets, and web browser. The app is being developed by the decidedly friendly people at Gaslight, a software development company downtown across from the main library. Daniel and I met with them a couple weeks ago to talk about collaborating on our recent grant to develop a real-time display for local businesses. I found them hospitable, helpful, and most importantly to me, meaningfully interested in the usefulness of the thing: about half of the office takes transit to and from work every day. They are their own beta testers!

( You may also recognize Gaslight as a sponsor of the Cincinnati Bike Map. Gaslight rocks and their office is full of bikes too. Give them highfives when you see them. )

As for the app itself, it’s pretty simple right now, as it probably should be. You can click through all of the features as quickly as I could describe them, so I’ll leave you to that. The highest praise I can give it is that it seems to work. I’ve used the app a half-dozen times, and played around with it quite a bit. The interface is nice and simple and intuitive and most surprisingly of all, the data behind it seems pretty accurate. The buses come when it says they will. Plus, that logo. Gosh.

For all the developers in the room, Bus Detective is open-source and available on GitHub. Anyone who finds a technical/useability problem with the app should report a bug there. My understanding is that more features are coming soon, so there’s that to look forward to also.

Overall rating: 4.7 stars out of 5. ( You’ve got to leave some room for improvement, right? )

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Posted in: Simplicity
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Predicting a distribution of bus tardiness

I’m about to start digging into various real-time data feeds for American (bus) transit systems. For the most part right now I’m interested in finding a simple,  average distribution of lateness/earliness across all stops, the idea being that this could help riders predict, without live real-time feeds, when the bus is most likely to show up, by looking only at a fixed schedule.

Are buses more likely to be late than early? What percentage of buses are early, anyway? If it’s already five minutes late, is it very likely it’s coming in the next minute? Or should you start walking? What’s the difference in tardiness distributions between frequent and infrequent services? Are there types of places in a city which have consistently different distributions?

In the name of science, I’d like to make a prediction, ie. state my hypothesis, before I’ve collected any actual data. So here it is:

Predicted Bus TardinessI think that overall the distribution will have a strong late skew, a very short early tail, and a wide second hump around the time a second bus might start bunching up on the one in question. I’ll guess that between 10% and 20% of buses running on fixed schedules will be at least a few seconds early and that the median will be about 2 minutes late.

Now…anyone want to suggest a city with a real-time feed? I have my eye set on Portland at the moment but only because I’m have trouble finding decent APIs.

Comments: 1
Posted in: Analysis | Data
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