Traffic Prediction with Advanced Graph Neural Networks
1–10 of 45 posts
Re: Traffic Prediction with Advanced Graph Neural Networks
#2I think in addition to predict traffic jam for individuals, this can be used to adaptively control traffic lights and open/close lanes to reduce traffic jam for the whole system.
Re: Traffic Prediction with Advanced Graph Neural Networks
#3related question, is there actual open source data available for traffic or other kinds of urban movement for people to toy around with?
Re: Traffic Prediction with Advanced Graph Neural Networks
#4related question, is there actual open source data available for traffic or other kinds of urban movement for people to toy around with?
Houston has some.
I’m on my iPhone so I can’t browse the data, but XML and JSON can be found here.
https://traffic.houstontranstar.org/datafeed/datafeed_info.a...
Re: Traffic Prediction with Advanced Graph Neural Networks
#5Google Map's traffic prediction has always led me to a very curious question:
Clearly Google Maps has the ability to turn into a feedback loop. Traffic exists -> people use Google Maps to find better routes -> traffic is modified due to people taking alternate routes -> new traffic emerges.
So my question is: what is Google Maps traffic optimizing for? The best traffic experience for User 3982274, or the best traffic experience for the conglomerate of all cars on the road?
Should Google Maps route several cars through a suboptimal route, if it results in traffic as a whole becoming better?
If Google Maps is "greedy" for every driver, can that make a traffic problem worse?
In reality, I guess this problem is more hypothetical than real, at least today. But imagine this: in 30 years, if all cars are self-driving and self-navigating via systems like Google Maps, what is the system optimizing for?
edit: there's also Braess's paradox. I'm not sure if it applies here, but perhaps it does -- could "sending some users down a new route during heavy traffic" be identical to "adding a road to a network", which can therefore result in the paradox (worse network conditions for everyone)?
Re: Traffic Prediction with Advanced Graph Neural Networks
#6Google Map's traffic prediction has always led me to a very curious question: Clearly Google Maps has the ability to turn into a feedback loop. Traffic exists -> people use Google Maps to find better routes -> traffic is modified due to people taking alternate routes -> new traffic emerges. So my question is: what is Google Maps traffic optimizing for? The best traffic experience for User 3982274, or the best traffic…
There's been a lot of anger in some neighborhoods when mapping apps start directing people through their small streets. This isn't really hypothetical.
In theory, if Google Maps starts directing some portion of traffic through an alternate route, it's because it's less congested. As it does so, the main route also becomes less congested. In theory, it should reach a point where roughly, cars are being assigned to both routes, as both routes have roughly equalized in performance. In theory.
Re: Traffic Prediction with Advanced Graph Neural Networks
#7Google Map's traffic prediction has always led me to a very curious question: Clearly Google Maps has the ability to turn into a feedback loop. Traffic exists -> people use Google Maps to find better routes -> traffic is modified due to people taking alternate routes -> new traffic emerges. So my question is: what is Google Maps traffic optimizing for? The best traffic experience for User 3982274, or the best traffic…
When User 3982274 is on a busy road using the app, Google optimizes for that user's experience. If every user on that road is using the app at the same time, these algorithms should theoretically result in the optimal condition you described above.
For example, if there are two roads leading up to the destination, one at 100% capacity and the other at 0%. The app will start routing people from road 1 to road 2. When the two balance out and the app will stop the suggestion. Even though it helped only some individual users, the end result is a 50/50 split, so good for everyone.
Re: Traffic Prediction with Advanced Graph Neural Networks
#8Google Map's traffic prediction has always led me to a very curious question: Clearly Google Maps has the ability to turn into a feedback loop. Traffic exists -> people use Google Maps to find better routes -> traffic is modified due to people taking alternate routes -> new traffic emerges. So my question is: what is Google Maps traffic optimizing for? The best traffic experience for User 3982274, or the best traffic…
If drivers discovered Google Maps was intentionally sending them down sub-optimal routes, they'd quickly switch to a different navigator. (Even if the Google way was better for the network overall).
Re: Traffic Prediction with Advanced Graph Neural Networks
#9Google Map's traffic prediction has always led me to a very curious question: Clearly Google Maps has the ability to turn into a feedback loop. Traffic exists -> people use Google Maps to find better routes -> traffic is modified due to people taking alternate routes -> new traffic emerges. So my question is: what is Google Maps traffic optimizing for? The best traffic experience for User 3982274, or the best traffic…
When User 3982274 is on a busy road using the app, Google optimizes for that user's experience. If every user on that road is using the app at the same time, these algorithms should theoretically result in the optimal condition you described above. For example, if there are two roads leading up to the destination, one at 100% capacity and the other at 0%. The app will start routing people from road 1 to road 2. When…
That's how it theoretically works as a free service where all users are equal. It's not difficult to image a tiered subscription model which finds a sub-optimal 70/30 split more profitable. It's also important to note it's optimizing for time, not fuel usage (a shorter path may require expensive elevation changes, for instance), traffic noise for neighborhoods, safety, services access, etc.
Re: Traffic Prediction with Advanced Graph Neural Networks
#10Google Map's traffic prediction has always led me to a very curious question: Clearly Google Maps has the ability to turn into a feedback loop. Traffic exists -> people use Google Maps to find better routes -> traffic is modified due to people taking alternate routes -> new traffic emerges. So my question is: what is Google Maps traffic optimizing for? The best traffic experience for User 3982274, or the best traffic…
It's a good question, even if it's likely not applicable practically yet. In a game my company created, we implemented cooperative realtime pathfinding using WHCA* -- an algorithm that David Silver published [0] (he's now working at DeepMind last I looked).
WHCA* turned out to be a bit too suboptimal for our use-case, people generally expected "perfectly optimal" routes to be used for aircraft, and they weren't even overly happy with most-optimal "for-all" paths either. We eventually implemented a relatively simple "AStar-3D", essentially just A* against a space-time graph, and it's greedy/FIFO -- meaning it's optimal for each aircraft at the time the aircraft runs it's path. That made people happy -- aircraft no longer did seemingly stupid things like "oscillate", or get "temp. stuck" for overly long periods, etc.
I had no idea cooperative path-planning was so damn difficult -- I remember estimating it as a 1-week mini-project initially. Wow, such naivety, and that's when you even have perfect information! Such a cool domain, tons of respect for the work that's being done here, even if there are some tricky/ethical aspects that are going to come into play eventually, inevitably. :)