4-way intersections: Throughput and deadlocks [image heavy]

[vanatteveldt]

Divaya
There is just so many problems with the "buffered diverging diamond intersection" that I don't know where to start.
I threw it on the test bench to confirm what I thought. And the test confirmed my concerns. Both with the throughput and how the trains move. set 1 = 68 per min. For reference: 264 is max for 4 lanes and 132 is max for 2 lanes. With this throughput you are better off with a 2 lane system.
I can elaborate its problems if you want to.

Not the OP, but out of sheer curiosity as an onlooker: yes please! I would love to have a better understanding of the principles of efficient junction design!

[Divaya]

Divaya
There is just so many problems with the "buffered diverging diamond intersection" that I don't know where to start.
I threw it on the test bench to confirm what I thought. And the test confirmed my concerns. Both with the throughput and how the trains move. set 1 = 68 per min. For reference: 264 is max for 4 lanes and 132 is max for 2 lanes. With this throughput you are better off with a 2 lane system.
I can elaborate its problems if you want to.

I'm not really that interested in only 2 lane designs. I'm super aware there's a bunch of problems with the diverging diamond, because it's an interchange designed for cars, not trains.

I put it together just for fun, relax a little please. It's never going to be an optimal design without bridges as the actual interchange has.

[hansjoachim]

Vanatteveldt
Sure
2 different looking incoming areas. South and north are the same. East and west are the same.
The intersection is divided up in 4 smaller intersections. I marked all the 4 intersections, which should be divided into many more.

South/north
Doesn’t split of right, left and straight.
East/west
Only splits of right.
The problem is that when a train is waiting it is holding up the trains behind and basically cutting the throughput in half or 1/3.
Also, most of the time parallel tracks will only have one train to pass, because the intersection doesn't split of the trains in left, right, straight.
The blocks are too big, and the buffers doesn’t really divide up the intersection as intended. In those 4 big blocks there are so many different scenarios so that some trains could have to wait to drive every 4-5 time.
U turns are not included in the test bench, but if trains were to take a U turn the throughput would suffer even more. A good U Turn requires much more space and here it also blocks unnecessary many train paths. Please keep U turns out of 4 and 3-way intersections if you want more than very low throughput. Unless no trains use the U-turn.

Other issues.
South/north:
1. Left goes first to the right and then crosses straight.
2. Some trains are very far away from the intersection. Look at the train path length from south to west or inner lane south to east. The trains must drive almost three 6 car train length to clear the intersection.
3. Right and left drives together and cuts the potential throughput in half. And if a train goes south to west, the next train going to the east would have to wait for a long time to drive.
East/west:
1. Left and straight goes the same direction and crosses way too many unnecessary lanes.
2. The end of left should turn one intersection earlier.
3. Trains here must also wait for a long time for trains to pass. Like west to north and west to south blocks the intersection for three 6 car lengths.

[hansjoachim]

Divaya
It looks nice though=)
I have made an intersection where bridges wouldn't increase throughput. As merging is the bottleneck.

[Divaya]

Alright, maybe I've come around a little.

I tested it and got 69:82. Not awful.

The performance is okay, but the massive footprint and 1.9k rail cost doesn't feel worth it. Uses a tiny bit of circuit logic to discourage trains using the left-turn lane if they're going straight-thru. Can definitely still be improved through signaling and conditional circuitry on intersections, but I've already spent way too much time tweaking it already.

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

[hansjoachim]

Divaya
I really like some features of this intersection.
The throughput WesT->east, east->west is really good! North->south, south->north is almost as good.
I would imagine that west<->east direction would be the mainline with this intersection.
If you have to have U-turns in the intersection this implementation is quite good, as it blocks very few paths and is buffered.
All right turns are great, can't make them better.

The left turns though. This intersection hates left turns.
You got a much better score with set 2 than 1. As set 2 has fewer trains turning left.
They only ever have one lane. Also, 2 and 2 left turns are merged together. East->south and south->west. West north and north->east.
This makes it so that left turning trains switches between going every third time, if all lanes are saturated, and with only one lane.
Compared to the straight lanes which all can drive every other turn and have 2/3 lanes.

South->west and North->east is also crossing more lanes than they need to, as they should start off on the left side of the straight going lanes and not the right side.
I'll make a version that shows what you could change to increase throughput

[hansjoachim]

Here is a slightly altered version with 79 in set 1 and 85 in set 2.


https://pastebin.com/fddjXDg7

[Divaya]

It's amusing that the west <-> east direction is so good, because the real life interchange is meant for north <-> south traffic to be a highway :V

I like the modification. It leans even heavier into the 'diverging' idea of the real life interchange, and it removes the u-turn (I'm not a fan of u-turns personally). Left turns will always be a struggle for this design unless I squeeze in another left turn lane to clear crossovers faster, but ideally you're designing train networks with as few left turns as possible.

[Blacky007]

I improved an existing 4-lane kross a bit and now there can be up to 6 Trains in this cross at the same time.
https://pastebin.com/rpPkhNkh

Edit: I found out that it is a updated version of Nexarius blueprint.
viewtopic.php?p=292755#p292755

[Divaya]

Same Diverging Diamond with Han's tweaks, but for 5 car / 10 car mix that I personally use incase anyone wants it.

Code: Select all

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

Signaled for 10 car, add signals if you only use 5 cars, needs circuit network logic if you mix 5 and 10 car like me.

[hansjoachim]

Divaya

Nice=)

[hansjoachim]

@aaargha
I have made an intetsection that outperform the combinator based intersections with a smaller footprint. 136 is max for a 4 way 2 lane 6 car intersection with nuclear fuel. I have a version that does 130 trains/min in set 1,2 and 110 with left only. Basically there is not more to gain and there is no need for the combinator based intersections I made. So I just wanted to say that I think you dont need to add a seperate category for combinator based intersections.

[mrvn]

@aaargha
I have made an intetsection that outperform the combinator based intersections with a smaller footprint. 136 is max for a 4 way 2 lane 6 car intersection with nuclear fuel. I have a version that does 130 trains/min in set 1,2 and 110 with left only. Basically there is not more to gain and there is no need for the combinator based intersections I made. So I just wanted to say that I think you dont need to add a seperate category for combinator based intersections.

Blueprint or it didn't happen.

[hansjoachim]

Mrvn
Basically the same intersection I already put up with a longer waiting area for the combinator based merger. But sure I can do that:)

[hansjoachim]

Hey
Here is the intersection.
I had one with 130 random but only 90 with left only. and on with 120 random and 110 left only.
I combined those and here is an intersection that does :
Nuclear fuel:
Set 1 127
Set 4 (left only) 125

To get over 130 the waiting area for the merger needs to be longer and it is already very long.

With rocket fuel I get:
set 1 113
set 2 113
Set 4 (left only) 108 (Until it is over saturated)

https://pastebin.com/rmTDkPY4

[hansjoachim]

So since I claimed 130, here is a version that actually does 130 with nuclear fuel.
https://pastebin.com/LygYBWJa

Set 10(same as 1 with nuclear) 130 (132 if you let it run for 60 min)
Set9(same as 4 with nuclear) 126

Rocket fuel
Set 1 (random) 124
set 2 123
Set 4 (left only) 109

[mrvn]

The last design seems to be limited by the output buffers. Consider merging the lanes there pairwise in a balanced tree structure and adding circuit logic. You can time it so trains leave with just the braking distance between trains and no extra gaps.

[hansjoachim]

The last design seems to be limited by the output buffers. Consider merging the lanes there pairwise in a balanced tree structure and adding circuit logic. You can time it so trains leave with just the braking distance between trains and no extra gaps.

I would consider 132/136 as close to perfect as you get.
I don't know how to add circuits logic to get a better flow.
If the lanes are merged earlier the trains tend to not use all lanes. Also you would need an even longer area to merge the trains.
The "problem" I see is that left only is a bit lower. That can be solved by doubling the center buffer to two train lengths.

[mrvn]

The last design seems to be limited by the output buffers. Consider merging the lanes there pairwise in a balanced tree structure and adding circuit logic. You can time it so trains leave with just the braking distance between trains and no extra gaps.

I would consider 132/136 as close to perfect as you get.
I don't know how to add circuits logic to get a better flow.
If the lanes are merged earlier the trains tend to not use all lanes. Also you would need an even longer area to merge the trains.
The "problem" I see is that left only is a bit lower. That can be solved by doubling the center buffer to two train lengths.

You want to join the lanes like this:

Code: Select all

----------0------\
                  >-----------\
----------2------/              \
                                 >-----------------
----------1------\              /
                  >-----------/
----------3------/

The <N> are where you put signals. You create a cyclic clock that goes through 0, 1, 2, 3, switching every T ticks and activate the signals when the clock == <N>. T is the distance in ticks between trains and depends on your train size, fuel, breaking research and desired (optimal) speed. If 136 trains / minute is the maximum then T = 60 * 60 / 136.

The idea of the signals is that it makes trains leave in a preset pattern that lets each train slot into the hole between the other trains. At each junction you merge trains in a perfect zipper pattern.

[hansjoachim]

You want to join the lanes like this:

Code: Select all

----------0------\
                  >-----------\
----------2------/              \
                                 >-----------------
----------1------\              /
                  >-----------/
----------3------/

The <N> are where you put signals. You create a cyclic clock that goes through 0, 1, 2, 3, switching every T ticks and activate the signals when the clock == <N>. T is the distance in ticks between trains and depends on your train size, fuel, breaking research and desired (optimal) speed. If 136 trains / minute is the maximum then T = 60 * 60 / 136.

The idea of the signals is that it makes trains leave in a preset pattern that lets each train slot into the hole between the other trains. At each junction you merge trains in a perfect zipper pattern.

Sounds reasonable, It could also be a more compact solution. Would you make it?