cross-posted from: https://lemmit.online/post/5292633
This is an automated archive made by the Lemmit Bot.
The original was posted on /r/science by /u/calliope_kekule on 2025-03-01 05:53:17+00:00.
cross-posted from: https://lemmit.online/post/5292633
This is an automated archive made by the Lemmit Bot.
The original was posted on /r/science by /u/calliope_kekule on 2025-03-01 05:53:17+00:00.
It’s not an extremely large amount of data at all, you can get perfect efficiency by having lights act on completely local, real-time, sensor data, as in “how many cars are in which direction”. AI is useful to recognise who wants to use the light but that’s the end of it. You don’t need to predict traffic patters as you don’t need them to see what’s the state of the streets right now, worse, such predictions are a source of BS. Lots of patterns happen all the time that have no precedence as construction sites shift, sportsball games get cancelled or not, whatnot.
I’m extremely sceptical about local data being enough to properly guide traffic…
the problem is that intersections are connected.
one intersection influences others down the line, wether that is by keeping back too much traffic, thereby unnecessarily restricting flow, or by letting too much traffic flow, thus creating blockages.
you need a big picture approach, and you need historical data to estimate flow on any given day.
neither can be done with local data.
could you (slightly) improve traffic by using local traffic flow to determine signals? probably, sure.
but in large systems, on metropolitan scales, that will inevitably lead to unforseen consequences that will probably probe impossible to solve with local solutions or will need to be handles by hard coded rules (think something like “on friday this light needs to be green for 30 sec and red for 15 sec, from 8-17h, except on holidays”) which just introduces insane amounts of maintenance…
source: i used to do analysis on factory shop-floor-planning, which involves simulation of mathematically identical problems.
things like assembly of parts that are dependant on other parts, all of which have different assembly speeds and locations, thus travel times, throughout the process. it gets incredibly complex, incredibly quickly, but it’s a lot of fun to solve, despite being math heavy! one exercise we did at uni, was re-creating the master’s thesis of my professor, which was about finding the optimal locations for snow plow depots containing road salt for an entire province, so, yeah, traffic analysis is largely the same thing math-wise, with a bit of added complexity due to human behavior.
i can say, with certainty, that the data of just the local situation at any given node is not sufficient to optimize the entire system.
you are right about real-time data being important to account for things like construction. that is actually a problem, but has little to do with the local data approach you suggested and can’t be solved by that local data approach either… it’s actually (probably) easier to solve with the big data approach!
And gets data from them, in the form of how and when cars arrive, and that’s all you need, at that point it’s a simple problem: When an individual traffic light regulates local traffic optimally based on that local information, then it does not cause undue problems for other traffic lights. Evolution does decentralised factory shop-floor planning just fine with just local information (have a look of how the genome assembles itself into bodies), and traffic flow is vastly less complex. “Acting on local information” does not mean “blind to global concerns”, that local information includes what’s necessary to know about the global situation. You can have every traffic light talk to the one down/upstream ("I’m seeing this many cars from you, I send you this many cars) but that’s just another way to do the local sensors.
Traffic routing can make use of global information, but we were talking about deciding the length of light phases, not figuring out where to build a metro line, narrow a street, whatnot.
this completely ignores larger traffic patterns like arterial roads.
with your idea you are guaranteed to get massive gridlock all along the major roads.
and biochemical assembly of proteins has just about nothing to do with either shop-floor-planning or traffic regulation.
what you are suggesting IS better than simple timers!
but it is NOT better than central coordination.
you are seriously underestimating the complexity of the problem, and your “all you need to do…” bs only shows how little you understand of the underlying issues.
do you really think nobody else has thought of what you’re proposing?
of course people have thought of this approach. it doesn’t work.
How. Seriously. Show me an adaptive traffic light dumb enough to cause gridlock. Not to mention that gridlock and having arterials, road hierarchies in general, are kinda incompatible with each other and most of the world doesn’t use grids in the first place.
And it’s not like we don’t have central control over here – it’s that all the information necessary to make decisions for a single traffic light is available right there, at the traffic light, because it is impossible to have traffic (or the absence thereof) and that not carrying the necessary information. Roads are wires, so to speak. Central control could make those decisions, but as local information suffices, why would it, regarding traffic lights it’s generally only monitoring. Central control can override things, things like ambulances influence traffic lights in a non-local manner (which is a luxury problem because they are allowed to cross on red anyway), but for basic operation central control could vanish and you wouldn’t see a difference, when a light loses connection but not power it just keeps on operating. Things like information systems telling people where to park need non-local control because they need non-local information.
wow, no.
none of what you said is actually true.
seriously, if you make a claim contradicting both the very premise of the post, and common knowledge on the topic, then at least provide a source for that claim, lr explain WHY you think your claim is true.
“all the information is there” is not enough information to verify the claim; it’s a wild guess without evidence to back it up.
if shit where THAT simple, we’d have it figured out 50 years ago… it’s almost like this isn’t the simple problem you desperately want it to be…
I said something about road hierarchies, you ignored it.
These systems are in operation. You claimed they lead to gridlock. What I get from the Chinese experiment here is that they collected data, threw an optimisation algo on it, and then adjusted local parameters, “err towards giving more green time in this direction” type of deal. They’re still going to use the same type of adaptive, local-control system that’s becoming increasingly common in the last decade.
Vehicles travelling on roads constitute information travelling over roads. Are you trying to deliberately misunderstand what I’m saying. You do not need to look at the app of the parcel carrier to know that your parcel arrived, it’s right there on your doorstep. That’s information. Metaphorically, thus, package delivery trucks are wires.
50 years ago we neither had the sensors we have now, nor did we have the processing power to use it. Traffic light control was often still done electromechanically. “Adaptive” means a lot more than “pedestrians have a button and there’s an induction coil to detect a car”. Those systems actually solve the local problem optimally which, in the case of traffic management, means that the global problem is solved optimally because the problem has optimal substructure. Don’t ask me for a proof of optimal substructure I just sat on a municipal traffic committee, I don’t actually design those systems. Got annoyed at stupid NIMBY questions so I drowned them with smart ones. When you observe those kinds of lights in low traffic situations they’re green for everyone because they switch as soon as they see someone arriving and noone else needs to be let through. In higher traffic situations they prioritise throughput, but make sure to not let waiting time for others get exceedingly long, or allow large backups.