[boskid] Not closest train arrives at the station

[ResidentDeath]

Why if i turn on train station, the train with the smallest ID arrives, not the one closest to the station.
Shedule is same.
video: https://www.youtube.com/watch?v=cHHwZahV3HM (eng sub)
make sure that the train id on top must be higher than train id on bottom

Code: Select all

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

[Kalanndok]

Don't see a problem here.
If you don't specify priorities then some random train will be called.

Solution:
Just set the priority of the train stop that shall release the train earlier to a higher value.

[mmmPI]

Because what takes a lot of time to compute is the path for one train, if the game was to try and search which train is the closest, it would need to calculate a path for EVERY trains first, to determine which is the closet everytime one train moves. It would be horrible for perfomance.

[ResidentDeath]

Because what takes a lot of time to compute is the path for one train, if the game was to try and search which train is the closest, it would need to calculate a path for EVERY trains first, to determine which is the closet everytime one train moves. It would be horrible for perfomance.

Thx for answering.
Can you explain how trains are chosen?
It is the random train, train with lowest id or something else? Can i read about this. I found only about how trains find path to closest station.

[mmmPI]

train with lowest id

This is correct, i don't know where you could read about this, you can find from testing

[boskid]

I am going to claim this is Not a bug.

Based on how this train setup is structured, when the train stop opens there are no trains waiting in a "destination full" state so the train stop is unable to pick one of the trains that is closest to it. Because of this, trains are following standard update and the first train that happened to update will be dispatched regardless where it is on the surface. Lowest ID train is usually first to update so you get the behavior that is observed.

Logic that selects closest train to arrive is only working in case where there are multiple trains all in the "destination full" state as in that case all the other conditions to depart were already fulfilled and they are known to be ready to arrive. Trains that still did not depart because their schedule has circuit conditions or condition for other stop to not be full are technically still stopped at other train stop and so they are not a candidate for the "closest train first" logic to run. I am not seeing too many options to make "closest train first" logic to apply in other cases because that would mean each tick between each train update i would have to first partially update trains to know if their wait conditions are fulfilled and then decide which one became available to the closest train stop from its point of view. Current behavior is actually better in that sense because by having trains update sequentially the "station X not full" condition works reasonably well because by the time this condition is checked this train is the only that can start going and make the station X not full causing a subsequent train to not spuriously depart to station X even if it was immediately taken by other train that was closer.

If you want the closest train to arrive, make your trains to stay in the "destination full" state so they are ready to depart as soon the target station becomes available.