Intangir's Vanilla Train Network
I updated my vanilla train network to work with factorio 2.0+ (and space age)The implementation is totally re-rebuilt (and relatively untested so this is alpha) using the new 2.0 features for parameterized BPs, and train interrupts. VERY powerful tools.
You use it essentially the same as before but it has several improvements:
Video explanation: https://youtu.be/CuYDZV47ht8
2.0.2a fixes a flex train overprovisioning issue that occurs with multiple supply stations
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
IVTN 2.0.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
Narrow track layout for grid block bases 2.0 (with red/green circuits):