Careful -- this is only locally optimal! You can do better :). [1] is the proven globally optimal solution of a very similar problem from one of the foremost experts in the TSP, Bill Cook at U of Waterloo. It is solvable in less than a second on your iPhone [2] -- yes your iPhone can solve a TSP to "true" optimality.
Also the route is just wrong at some points like the Delaware stop being 1+ hour south from where it's supposed to be due to Google Maps spitting out a weird result for New Castle Historic District.
Very curious about the decision metric for "major landmark" here. The San Benito County mark, for example, leads you to a dirt road in the middle of nowhere, and the major point for Delaware is just Delaware. Neither of those seem to fit a reasonable metric for a major landmark.
Seconded; I mean: I can understand why somebody who was into nuclear tourism might want to swing past the Hanford Site in Washington, but I'd bet that the majority of Americans might rather steer clear.
If you're wondering why the route has some 'strange' segments (why get off I-10 to head down Orlando way if you're going to drive to Jacksonville later?):
The link goes to the "Major U.S. landmarks" road trip.
When it came out a few weeks back, I noticed that it went to the South Rim of the Grand Canyon, which is the popular one. Going to the North Rim would save over 100 miles of driving. To be sure, the South Rim has the better view.
It also can't be done in winter, as its route through Yellowstone isn't open then.
In general, "machine learning" means that an algorithm did it, and that the algorithm engages in some analysis of the problem space or the solution space. Usually that analysis involves an iterative or repetitive element: making several tries and modeling what makes a try good or bad, making a try and then changing the solution tiny bits to find a try that's slightly better, etc. And "good" or "bad" is determined according to a human-provided rubric (eg: +1000 points for every sight seen, -1 point for every mile driven, -10 points for every day taken, etc.)
Unless the problem is very constrained, there usually is no guarantee of optimality; here there is probably no guarantee of optimality. It might be "locally" optimal, in that there might be no better trip that differs from this one by only a tiny bit.
And, no, "machine learning" here doesn't mean the enterprise and establishment of "Machine Learning", just some algorithm that the author used.
This is presumably not the intended parsing of that sentence, but "machine learning for religious reasons" sounds like a great premise for a sci-fi novel.
Nebraska is a great place for driving really fast. One time while passing through on my way to Montana I hit 130 mph in my Integra. I don't really have anything else to say for Nebraska.
But it does go on US2 in Montana, which is one of my criteria for an "optimal" trip through US.
But insofar as Nebraska is concerned, I have relatives there, and during one family reunion, we all went to the 4th of July parade in Hoskins. The large painted sign said "Parade at 8 am, pancake breakfast at 8:30". Not a large town.
But then I grew up 6 miles outside of a town of 3000, so I am not all that in favor of large towns.
agreed, but you're too early. The current buzzword your looking for to put in your marketing material is 'Machine Learning'. Revisit 'Combinatorial Optimization', possibly combined with 'Cloud' again in a few years.
[1] http://www.math.uwaterloo.ca/tsp/usa50/road.html
[2] https://twitter.com/wjcook/status/575762813345480705