3 and 4 way intersections

[kazaakas]

I tried running some smaller 3-way intersections on the test bench with several lengths of trains, but noticed that for certain lanes/directions, trains end up waiting a very long time before they could pass, sometimes until at the end of the test when all the traffic cleared up. Clearly, when train length is adjusted, it has an effect on the prioritization of who is allowed to pass.

Funnily enough this results in a huge score, but it's not very fair as the throughput is very unbalanced. I wonder if there is a factor for this that's worth measuring for determining whether a result is actually useful.

[Avona]

I wonder if there is a factor for this that's worth measuring for determining whether a result is actually useful.

You need to change the tpm. It's tuned for 2-4 trains so that there is a small gap that allows other trains to go. In the picture, the left circled CC is for manually changing the options. The central display is for your information. On the right tooltip, I have indicated that the train signal is the appropriate signal to change. Modify the signal until you get the desired train behavior.

[Hovedgade]

Here is yet another roundabout. I know it is symmetrical but i just couldn't resist naming it this way.
Link: Totally symmetrical roundabout

Picture

Metadata: RHT, 38x38, No buffers, Allows U-turns, 4 tile gap.

Cheers.

[RunItsATrap]

I wonder if there is a factor for this that's worth measuring for determining whether a result is actually useful.

You need to change the tpm. It's tuned for 2-4 trains so that there is a small gap that allows other trains to go. In the picture, the left circled CC is for manually changing the options. The central display is for your information. On the right tooltip, I have indicated that the train signal is the appropriate signal to change. Modify the signal until you get the desired train behavior.

Thanks for that hint, very useful.

Speaking of which. I am currently working on a buffered intersection for 5+16 trains. It only supports these directions: W->N, W->E, W->S and N->W, E->W, S->W. The number of trains per unit of time is such that in the real factory, #(W->N) = #(N->W) and so on and forth, so in the testbench, there should be as many trains coming from the west as from the other three directions together. Can that be configured/hacked somehow by me, or would that require deep shenanigans with the circuits?

[Avona]

I believe that you would need to modify the circuits to reduce the tpm on the X->W trains, although they would even out anyhow as the outbound west line would only be able to take maximum trains. I don't believe that there's a built in way to do this with the test bench and I don't have time to mess around with factorio atm. Perhaps Hans can give a better response if he sees this.

[hansjoachim]

I wonder if there is a factor for this that's worth measuring for determining whether a result is actually useful.

You need to change the tpm. It's tuned for 2-4 trains so that there is a small gap that allows other trains to go. In the picture, the left circled CC is for manually changing the options. The central display is for your information. On the right tooltip, I have indicated that the train signal is the appropriate signal to change. Modify the signal until you get the desired train behavior.

Thanks for that hint, very useful.

Speaking of which. I am currently working on a buffered intersection for 5+16 trains. It only supports these directions: W->N, W->E, W->S and N->W, E->W, S->W. The number of trains per unit of time is such that in the real factory, #(W->N) = #(N->W) and so on and forth, so in the testbench, there should be as many trains coming from the west as from the other three directions together. Can that be configured/hacked somehow by me, or would that require deep shenanigans with the circuits?

Been thinking about adding that functionality, but thought nobody would use it.
You asking for it is perfect.
It's possible now with just seting one value. On The lane, you want to limit find the combinator that reads the oil signal. Replace the oil signal with the tpm value you want times 600.

[hansjoachim]

I wonder if there is a factor for this that's worth measuring for determining whether a result is actually useful.

You need to change the tpm. It's tuned for 2-4 trains so that there is a small gap that allows other trains to go. In the picture, the left circled CC is for manually changing the options. The central display is for your information. On the right tooltip, I have indicated that the train signal is the appropriate signal to change. Modify the signal until you get the desired train behavior.

Thanks for that hint, very useful.

Speaking of which. I am currently working on a buffered intersection for 5+16 trains. It only supports these directions: W->N, W->E, W->S and N->W, E->W, S->W. The number of trains per unit of time is such that in the real factory, #(W->N) = #(N->W) and so on and forth, so in the testbench, there should be as many trains coming from the west as from the other three directions together. Can that be configured/hacked somehow by me, or would that require deep shenanigans with the circuits?

Now you can set tpm per lane/per direction. Set auto off and adjust the value on the constant combinators

[P¡ІІow]

Made some wobbly intersections \0/
Useless yet mesmerizing.

Image.png (548.48 KiB) Viewed 4716 times

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

Copy blueprint
There are (in bp book):
- IL (Inclined Left) RHT version
- IL LHT version
- IR (Inclined Right) RHT version
- IR LHT version
- Smaller IR RHT version (less gap between lines), couldn't make other versions of it, signals just didn't fit.
Tested only IL RHT version, it seems only ~4% worse then standard Symmetrical Cross, didn't had the patience to test others.

If there are question, ping me in discord (Pillow#8247), doubt will see reply in forum.

[hansjoachim]

Made some wobbly intersections \0/
Useless yet mesmerizing.
Image.png

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

Copy blueprint
There are (in bp book):
- IL (Inclined Left) RHT version
- IL LHT version
- IR (Inclined Right) RHT version
- IR LHT version
- Smaller IR RHT version (less gap between lines), couldn't make other versions of it, signals just didn't fit.
Tested only IL RHT version, it seems only ~4% worse then standard Symmetrical Cross, didn't had the patience to test others.

If there are question, ping me in discord (Pillow#8247), doubt will see reply in forum.

That is a nice intersection, will add it:)

[mrvn]

Made some wobbly intersections \0/
Useless yet mesmerizing.
Image.png

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

Copy blueprint
There are (in bp book):
- IL (Inclined Left) RHT version
- IL LHT version
- IR (Inclined Right) RHT version
- IR LHT version
- Smaller IR RHT version (less gap between lines), couldn't make other versions of it, signals just didn't fit.
Tested only IL RHT version, it seems only ~4% worse then standard Symmetrical Cross, didn't had the patience to test others.

If there are question, ping me in discord (Pillow#8247), doubt will see reply in forum.

That indeed looks mesmerizing. Now I want to build use it.

[Tallinu]

So. Today's FFF (#377) on rail changes. (Insert George Takei "Oh my!" here. I can neither confirm nor deny the reports of uncontrolled giggling.)

Seriously though, holy crap that's going to make things interesting for intersection design.

[flashgo1]

could some one tell me if this is a decent intersection. i built it loosely on one i saw that had a decent flow but some of the areas were just not working very well. it was designed with 2-8 trains in mind. i tested it without the tester cause i did not know of it and i dont have the time to figure out how to get that all setup now. at the time i had 12 2-8 vanilla trains running through it and very rarely did i ever had a train have to full stop.

Code: Select all

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

if you run a certin bp book, not mine, it fits perfectly.

[Avona]

So. Today's FFF (#377) on rail changes. (Insert George Takei "Oh my!" here. I can neither confirm nor deny the reports of uncontrolled giggling.)

Seriously though, holy crap that's going to make things interesting for intersection design.

I'm quite excited for the new curves. They both look beautiful and allow the long desired s-bends, which should make most intersections much smaller and allow some new designs.

[Avona]

could some one tell me if this is a decent intersection

I would suggest posting your BP to the Factorio discord server. There is a channel named "train-help" were you could get exhaustive help from many people.

The closest intersection to yours appears to be the Buffered Compact Attempt. It has a very low throughput for a buffered intersection. The main issue is that safe exit blocks must be provided after the last crossings. Those final crossings negate a great deal of the throughput benefits of buffering the lanes.

I would also suggest against lane changes in intersections. Intersections are generally the biggest bottlenecks of a rail system; put the lane changes at the entrances and exits of stations instead. Trains will often lane change to minimize their path distance, which blocks and slows other trains.

Also, the vast majority of megabases only need 2 lanes. If you're considering more lanes, instead make more alternate paths for traffic and design your base so that less paths overlap. You provide two right turns and only one straight path and left turn from each entrance. Right turns have the fewest traffic conflicts and barely benefit from parallelization. I would instead suggest two left turns and/or straight paths from each entrance.

When there are two chain signals with no junctions between them (crossings, splits, merges), the second chain does ABSOLUTELY NOTHING. When you have a chain leading to a rail signal with no junction between them, delete the rail signal and replace the chain with a rail signal. The chain in that combination does nothing. Sometimes you have a rail signal two tiles before a chain signal. The rail signal in this case provides insignificant benefit.

The green and red wires do not extend throughout the power poles, having missed the center.

I sincerely suggest that you visit the Factorio discord! They are very nice and helpful in the train-help channel. They can provide much better and timely advise as you build your intersections.

[aaargha]

Good to see you guys still keeping up with the testing!

I have been scavenging some old disks and found the material I used for the old test thread, including all the BP-strings that are no longer available in the thread.

Attached is an archive here with the 120 blueprints that I had collected for the old thread. The files are organized the same way as the old post and the base text files I used for each section included are also (includes sources and image links etc.). Do note that the naming is the old convention so RHD=RHT and LHD=LHT.

[aaargha]

Also, as we need some more cursed intersections, here's a roundabout that includes two-way tracks to make the long turns short:

In testbench.png (882.74 KiB) Viewed 3424 times

Cursedabout LHT

0eNqlW8tu20gQ/BeeRWN63qPrXvaQ496CYCFLRELAlg09jDUM/Xuo6GGJbi6rpFNgRSz1dFf19PQ0P6rHp23zumqXm2r6US2a9XzVvm7al2U1rb79/U81qdr5y3JdTb9/VOv253L2tP/e5v216b7Qbprn7hvL2fP+r9Wsfap23QPLRfNfNZXdRHnkrV1ttt0n56cO36i/XTxpdz8mVbPctJu2Ofzynz/e/11unx+bVQf9+fSm+9Wfvzb1nx+fVK8v6/Zg/UfVIdVhUr13/7iy21vTg7FnmPl29dYshkD8ESR2Ji7aVTM//KdXIB0GaXXEoCB6dK3u/5YaaI8lDSayxqgoiTYmaDD5inn1/NesXdZHwn2FMg8nMP8Qrt0uCni5Bh+ElWFYp8CKYT2oLl14BTgVx4LLdOdl2v4yNRmIg+2TI65coyYN1dOoblxgEu5FVQMd7/WAxkpJDOflKmgaXGbZqLOogIk0HlBsHo+KhYVSH22zZZxBVqic8cl6QXKGtazN/ZhrTLKwlgaIpLrX30YkQZKcDaTF/dCpzo0YybzOMS1LWXojsgngWKZRC+DTQkq1D6qxwLG7UX/9WqCckNmgVwRFDRNXVjqihvFIOUejpvFIOXqX6qNaDTVw9I+AofAWNcB+lVKJBQUSq8MlVY6oDgh/oVGB5OcNtcXYh7Mf+qlVE4KHC78BHahHDMuCuvGYefAoVMuJsmZcBd6joFYHVS0lCsCTYoFq1UceFigsfcJcsD9pHD2gocCKGhCUyiOwDhwKjubFYFhDgTI64McnOe58AlSXwdKwFigAA14ADtBTC1bwLKoAVVrAtWSDDqvpPoDFX22P1JIM+BUv/6wjbMU3KysELL5bScFhI37OklPAgCI4Cg8LVMERVtiQbjUpREejAnVwxAWWiHiBdaA1uhC0oiLCOxXFLFhejAwirC5Gs7FQRZvkh7MWkKotGV67QFmcCJElHVbTbrKcM9KwM1SrHW81cOxKVEejnG2Oemss4T2MgbSoZZoUqbZLGPas6oJE24zEK9N5DCEZ2GDfx4eRWjZspWDH00MWsE1uz7cMAt0yZE5qF/BfTouak7O7O0FoLM7+7nSm0TcH3lrgIJojX+zr902Jrph1nEzLFOhm5HKvTLVQF3NvStEiXYQsYuz4+gslpYsdHLq3Kw5MV3lYoap/PXrP+An85QZOS1kl3LqJWySzlMjX90AHoSQeFjjvl0y2jcSM3x4UsmY8X6KIg65QjeEaPX2b1btOI/RRB7mYNfy5DLlDNe7Gcski2hPDXYJFToFibuiFCIIb+b4FhJv4xgWEm/lyAcItZIe5rxCVc2L4cgFot4nwPUeg3yZiuZ5r3wdRBaU7jkB3VITvOAoSsEAXPgi7hG+KQLB8VwSC5dsiEGyh7zCAfpsQwx0nWKDhJlbIuwKgkSn4UEdRHaByFp/pqHVTdbd67ubdI8sPrKUBiRQ3zOEBquLDHEm1U199JkETEnx2XEqducJnN2p9wSqFnLCowAlcHHU2Ozc5PFRmOcdNRiD2enKIATifiwskKDJOis9w1LqpOgvowShkdpGY4hDVVj387GAUNGhKj+kCscJnN2rdVDVWnh06RELlyZlDyKeenApEij8fbhroFKTHIZ6d4YXilUhQJLf4fB+oToJy3/LVgAVzy+CpQOf5wE7F69PMgXsvJAKuDO4mmmKz9sEzL51Efc0B7G9eWKbikG+IDESA3X8GjOHeEDmtzWNkQ98QMUOwqpIjufPoK4+sEpIOYzljBlAcaUzRYcg3oTqUH5PDK2rTi7fcJtXT7LF56j77a7taN4vZ48t2/+lbs1ofqozc5ehiU4peks273W+/APLx

Copy blueprint
Cursedabout RHT

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

Copy blueprint

[Avona]

Also, as we need some more cursed intersections...

We absolutely need more cursed intersections lol. I love it!

Unfortunately, when I went to test it with the new mod, I got variable results (set 3 incorrectly testing). So I tried to test with the 5.3.3 bench as well and got good scores with RHT but LHT tested with the RHT sets...

So for now, I'll wait until there's a stable version of the test bench before editing it in to the post. I just wanted to make sure that you knew there were some technical difficulties but that it'll be updated soon.

[SaiMoen]

So. Today's FFF (#377) on rail changes. (Insert George Takei "Oh my!" here. I can neither confirm nor deny the reports of uncontrolled giggling.)

Seriously though, holy crap that's going to make things interesting for intersection design.

Strongly agree, and now with 378 it seems like it will get even more crazy than that, which I wouldn't have believed after 377. Although 378 is technically a mod so any intersections using elevated rails will be separated from the normal ones in here I guess.

[Tallinu]

Oh yes. Too bad it's only straight horizontal and vertical for the ramps, but still!

It's a "mod" as much as the rest of the expansion content will be a mod but that's just so you can enable and disable it really. The bits that require the expansion like ramps will need to be clearly marked as such for sure, but considering it's official content from the devs, I wouldn't really want stuff made using it to get swept off into a corner (aka different thread)...

[PavelH]

Hey, train noob here,

Thanks for all the interesting intersections! After my last attempt at a train-fed factory bottlenecked itself to oblivion (due to me only using simple T junctions), I have been looking into how to do trains better next time.

I have a bunch of clarifying questions:

1. Is there some intro writeup to intersection design? I see a bunch of patterns repeated in multiple of the intersections here, some of which make sense in retrospect but some of them are a mystery to me.

1a. A particular pattern that I don't understand is parallel lines on the non-intersecting turns - since both the input and output are single lane, I don't understand how this helps throughput. Is it somehow related to how the game selects which train has priority to enter the final block?

1b. Another pattern I don't understand are long non-signaled section that are not immediately after a crossing, or are much later than needed for the crossing. What role do these play? I understand having too dense signals causes throughout to drop due to trains not being able to speed up, but this seems excessive.
A specific example of this would be "parallel multicross turbo", where the blocks preceding the exit are at least 12 carriages long, with some other blocks also being seemingly unnecessarily long

2. Is there a reason all intersections are using signals in one line? Personally, I have an offset of 1 tile between the "in" and "out" signals, because that enables the straight section to be a rotationally symmetric blueprint, and (according to the indicator when placing the signals) the train stops in the same place. Does this matter at all?

3. Does it make sense to try designing an intersection with 4 lanes on one side and 2 lanes on the rest? Or am I unlikely to get much better than joining 4 lanes into 2 before the intersection?
The use case I imagine for this would be a 4 lane "spine" network, with several 2 lane "local" networks connected to the spine.
Since all of the large designs parallelize each path, I tried adapting some of them (namely "parallel multicross" and "parallel multicross turbo") to 4 lanes on one side, but the benchmark didn't really show any difference in throughput. I'm not sure whether this is because my design is bad or because the train direction mix used in the benchmark doesn't really match where such an intersection would be used. Any thoughts?