I would be interested in trying out a "no left turns" (or at least no unprotected left turns) route preference.
There's a reason you seldom see UPS and FedEx trucks trying to turn left in crowded urban settings. It's because they cost time and money. Why don't Google and Apple understand that, and give us an option to avoid or discourage left turns?
Happens to me all the time.
It also seems to assume crossing a bridge at rush hour is cheap. My only guess, and this truly is a guess, it is sees people in the bridge lane stopped and the thru lane moving and takes the average.
Maps really need a “not worth it” features for tiny optimizations. I’d often prefer a slightly slower route that’s less work to drive.
> I’d often prefer a slightly slower route that’s less work to drive.
Me too, and I suspect most people — like, when is it ever a good option to avoid a free highway to save 1 minute when the driving is twice or more fatiguing? Are we bodyless machines or animals with a concept for "tired" and "attention"? This is where I feel recent evolutions in DL have been, perhaps, more for the benefit of developers and researchers (efficiency), and maybe not for real human benefits on the user side.
I don't claim the problem is easy, but I know second-hand that what users want and what AI projects solve for may be two very different X.
Seriously. I regularly travel from Ohio to Michigan and back, and often times if I take an exit for gas, Google Maps will assume I want to take an obscure country backroad for the final 2 hours rather than get back on the highway.
I absolutely do not want to worry about deer, a lack of gas stations, food, and light.
She’s certainly not wrong that from a UX perspective, simple can be really nice, especially if you’re driving on crowded roads and want to focus on driving.
It feels like cheating to the other drivers who didn’t take the “short cut” and leads to delays all around due to the extra merging going on. (exhibit A: I-85N vs. I-285 in Atlanta, aka Spaghetti Junction)
My intuition (listening to AI and DL podcasts) is that they just train too-general US-based (or evaluated or reinforced more specifically) models. Anyhow it's a disappointing situation that e.g. Google maps makes way more mistakes today than it did 5 years ago (but it also does more overall, I guess it's a trade-off, e.g. now it's great for public transportation in large enough cities).