Traveling Transaction Modeling

Over the past few years, traveling has become a major part of my life. I travel a lot for business, so I’m on the road a good chunk of the time. One of the most interesting (and daunting) things about getting from point A to point B is planning the route. This is most difficult when I have a meeting in New York City, because there are four possible (and reasonable) ways to get there. I could fly, go by rail, drive or take a bus.

Right away, the bus is a non-starter. It takes too long, the station is far from my home, and to be honest, it’s just not comfortable. Even though it’s the cheapest option, the quality of the seats multiplies by the length of the ride comes out to… well, you know how it is.

Flying should be the best bet. The flight takes only about 30 minutes. But with all the security at the terminal these days, the time it takes to get to the airport and the cost of the NYC taxi to wherever I’m going, it’s too much.

The train is fine, with good scheduling most of the time, and it does get you downtown at no extra cost, but if you want the best cabin, you have to pay for it. It’s expensive and depending on which train you ride, it can take nearly five hours to get to where you’re going.

Driving at least gives you some independence (and great music!), but it’s tiring and with gas prices where they are, it isn’t exactly a bargain. Tack on the cost (both financial and mental) of parking in New York City, and driving suddenly looks like a pretty bad plan.

All of these options have one problem in common – the scheduling is never accurate. You can’t be sure your flight will leave on time. Your bus could break down, you could hit traffic, or your train could be delayed for some reason. You always take the risk that the meeting will start without you, or not start at all.

What if we could track the data on that? Aggregate it based on EVERY ride, 24/7, all year long – how many minutes did we lose because of a flat tire, a mix-up on the runway, or a traffic jam? How buses perform in January, etc. If we had that information, we could filter it down and make the best decision on how to get there. Not just from an average, not just in general, but from real data. Can we have a database that tracks every individual ride, on every option, every day, and then aggregates it into a clear picture?

How often is the train delayed on Sunday? Can I get a comparison between today and last year? How many times has this bus line had to stop for a flat tire in March? Does it happen more in winter or in summer?

Now apply this to an application transaction. One little click of a mouse generates thousands of options, rather than just four. There are network devices, hardware (web tier, DD or – God forbid – a mainframe), you name it. Luckily, we do have a way to monitor them all. And the information we get is based on real data, not on an average.

Now who says that IT shows less progress than the travel industry? For now I’ll take my traveling decision based on 2 dimensions only – Price and Time.