That's the optimal path being highlighted

Bro 😂

@@bulkvanderhuge9006 i think they know

@@bulkvanderhuge9006definitely seems inspired by a lightning strike still. The flash before especially

@@bulkvanderhuge9006nooooooooo really?

In general A* looks a lot like Lichtenberg figures¹. ¹ do not attempt to do this, they’re very dangerous with the slightest mistakes

im seeing slime mold

Lightning does indeed take the path of least resistance to the ground

Thank you, that was the imagery I was going for.

Actually it reminds me Principle of minimum effort in the energetic domain. I will not include graph theory because it will be messy. Something like sex on dirt.

This algo (and similar ones) use heuristic to calculate "cost" of reaching each node. I think it could be adjusted to include speed limit which cold result in a better path travel time-wise.

Yes it could, you just multiple the length of each node by the speed limit before the algorithm stores their value

You definitely could do something similar that takes into account the speed limit, steepness grade, road quality, construction hazards, and any other factors you can think of. Welcome to the field of Geographic Information Science!

was exactly my thought for creator to dynamically simulate rushhour like traffic and see how the pathing changes.

Very nice visualization, but driving that route through and inside Rome can only be dared to be thought by a naïve computer mind who happens to know very little about driving in a kind of traffic where you whish you had grenades' stocked in your dashboard ...

+1 for the comparison, that would be epic

I agree! That would be super cool!

For A Level Computer Science students in the UK, they frequently have to compare A* to Dijkstra's algorithm - a comparison to this would be super useful!

I'm curious about node weights here. Are they just using position, or are they weighted so that larger, faster roads have a much lower traversal cost?

I agree. There's a ton of comparison videos for sorting algorithms. I could imagine the same for path finding.

this video is a work of art

Why is the reaction to something cool often "You need to be hired by a boss who will constrain you and enslave you and never make you design something this cool ever again." People do cool shit when they they are free from constraints.

@@broli123 yea most people dont realize how much good ideas and beautiful thoughts go to waste because they were deemed to not make money and not worth the time by idiot bosses and managers, nearly all companies have their capabilities bottlenecked by these managers and bosses who only exist to greed

@@broli123 I think the idea(l) was that "You need to be paid to make these so that you can keep making these with the safety net of being paid" but, as you say, not everything have to be for money.

Yup, you generally don't get paid for your best work, but for the bare minimum that satisfies the requirements, while taking the least amount of time possible, so you can quickly move on to the next thing and do it again, but faster pls! Time is money!

Except that time is a factor in mapping, you should pull from the value denoting speed of the road. Shortest distance while useful, in reality shortest distance in Time is the preferred solution. The shortest distance on the Rome map would be to take the ring road and exit.

@@mdyas1711Make the road weights the speed of the road, problem solved

​@@marcosalazar4682Real mapping applications probably use the time it takes to traverse the road as weight, which already takes speed and distance into account.

@@mdyas1711 The dataset itself doesn't seem to have the weights for road speed, so this is the closest they can get.

Also traffic lights, left turns without traffic lights can be slower slower than right tuns, and probably a bunch of other considerations.

The other stunt would be adjusting the loading percentage bar based on prior loads. For example the game Satisfactory might load environment, natural features, natural resources, player-made features, player-made resources, player inventory, and player appearance. When the game first loads the natural features and resources would take the longest to load as there is so much present and the player-made items are a single drop pod, a few Miners, a power plant, and an Assembler. So 70% of the load time would be the natural features and ~20% would be player-made items. As time goes by, the player is clear-cutting the local forest and building a giant factory. If the game does not adjust then the loading bar would advance quickly to the 70% mark and the final 20% mark would take far longer. But if the game adjusts the loading bar as the game goes on, then the natural items would be reduced to maybe 15% of the loading bar while the player's items would be ~80% of the loading bar.

Because A* has the heuristic, bidirectional isn't as important. It's still doable, though, just not the 4x faster you get from bidirectional for things without a heuristic.

The usual improvement to A* is to make it hierarchical -- there's usually no point in looking at little alleyways in the middle when you have a long way to go.

I really want to know this too!

The heuristic just says to check the paths that are closer to the objective first since they're more likely to be on the right path. That's why you see it reaching towards the goal instead of fanning out in each direction equally like Djkstra would.

Thank You! Maybe at some point yes. I have seen few videos like that and they do look interesting.

Don't worry, Google maps is a decade plus worth of incredible computer science path finding algorithms optimised to incredible levels. The A* algorithm is just one of the classical path finding algorithms in computer science along with dijkstra's algorithm. What's used in Google maps is based on the path finding algorithms but with years worth of crazy work done on it

All that doesn't change anything about the algorithm. All it needs is the "distance" between any two connected nodes. What goes into calculating that "distance" doesn't matter---all of that is just input data modelling.

@@HenryLoenwind that is assuming they are using A* and not something of their own

A good heuristic, like exists for euclidean distance, makes bidirectional less helpful. It'd be 4x faster for Dijkstra, but A* doesn't benefit so much from it.

I think this map is weighed on traffic to some degree, you can see the algorithm makes much more progress on highways then on back roads

Yeah, the algorithm is pretty easy to do. But finding the best heuristics is the challenge. Based on Dijkstra's Algorithm

Do you still play diablo 2?

Simply not true @Leeki85. A* is optimally efficient, meaning that it guarantees to search fewer nodes to find the optimal path than any other search algorithm (so long as the heuristic for the cost-to-go is admissible).

The most efficient way is actually to use a more advanced geometry. Instead of a weighted graph, you can encode pre calculated connections of hubs into the map with very little memory cost (only a few bit per node). You end up with quasi linear lookup times for optimal routes. It gers more difficult if weights change dynamically, eg due to road and weather conditions. Also, to stick with A*, I think you start expanding from both ends, and the graphs meet somewhere in the middle.

Isn't A* just Dijkstra with a bit of stuff added into it? Or am I confusing that with a different algorithm?

@@jan-lukas That's an interesting point! Both Breadth-First Search, Dijkstra's and A* are in the same family of algorithms. They explore the space by expanding nodes at the frontier. What changes is how they pick the nodes from the frontier. Dijstraks' uses the "cost so far" to prioritise nodes, while A* also relies on a heuristic to estimate the final distance to the target. Running A* is like pouring water on an inclined plane towards the goal.

@@jan-lukas Almost, A* is actually Uniform-cost search with a heuristic added to the priority (Not the cost). A* with a heuristic of 0 boils down to UCS