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Comment by taeric | Hacker News Reader

Oddly, I feel this needs some basic machine learning thrown in. If every day you rated how good commute (and what type) was for you, it could start making the prediction based on your preferences. Possibly even with more information such as when you are looking. (e.g. Some people are ok with colder weather, or if you are running late, the option that is consistently faster will be the better choice.)

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The problem there is that you don't know how bad the other option would have been.

The tube experience is probably fairly consistent, just that it costs more (except in the cases of delays and closures) so rating the bicycle trip is really the most important thing here. Perhaps "flip a coin" with probabilities proportional to the needle's position when it's on the side of the tube and take the bicycle a bit more often to see how terrible it actually is.

This is based on my experience in SF (i.e. muni/bart), but the public transit experience is pretty variable. Crowds, homeless people, traffic conditions, timely connections. The tube is probably a little better/consistent, but rating the experience would be a good feature to add in SF.

I would expect some correlation with the weather: nicer weather means nicer tube trips because, with worse weather, more people take the tube, and they will wear thicker, wetter clothes. Also, if it is extremely hot, the tube can be relatively cool (depends heavily on the tube in question)

The tube also can be extremely busy (sometimes with very noisy passengers) due to sales in shopping centers or due to sporting matches, exhibitions, etc.

I suppose. I'd be fine with it just biasing towards things I enjoyed. Doesn't matter if I would have enjoyed something else more. (Clearly it could, but I'm thinking this is a case of the perfect being the enemy of the good.)

Usually you would throw in some radomization, ie, 5% of the time the barometer would tell you the opposite of what it really thinks you should do.

As much as I tend to dislike social, this is a case where a distributed barometer app would be useful. Read indicators, make recommendation, track time-to-work, evaluate results.

This also calls for some value-assignment to transport methods. E.g.: cycling is preferable to tube, but more than light rain benefits tube, but more than an X minute / X% delay swings benefit to cycling. The real variable here is how long a commute takes -- so, is/was there a tangle in the tube when that was the recommendation.

Closing the loop on predictions/recommendations is a common problem in any monitoring/alerting application. For all the ballyhoo on the online dating problem, I'd warrant that getting closure on the dating experience is where most sites fall flat.

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