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Errors while parsing GTFS on tdata branch. Difference between branches? #203
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@mr-tm there are still many branches because we had a lot of experiments, including some that would change all data structures to check performance (column stores, different types of memory management). I would say try tdata-cherokee. We have mostly generated timetable via a script that directly accesses a database (and already has timedemandtypes). I am not surprised that it eats so much memory on raw gtfs files, and to be fair: I don't think GTFS is the best input to achieve good routing, mainly some preprocessing on stops and their relations towards eachother is required (for examples the transfer times between stops within a station). With respect to mobile deployment, ten years ago we did it with JNI ;) If you have more questions feel free to ask them. |
@skinkie Also, is it possible to get this library working with multiple timetable.dat files? For example, I have timetable.dat for city transports and timetable.dat for intercity buses and I'd like to use both files to create itinerary. |
The algorithm (RAPTOR) allow you to retreive the fastest times given N amount of transfers. Hence the only way to receive mulitple itineraries in one go is when there is actually a faster option available given more transfers. I am currently working on rRAPTOR (range raptor) which allows at virtually no cost to get a range of itineraries in a certain time range. That will be available in this branch.
No, that will not - by design - work. It is easier to merge the input (for example GTFS, see "one bus away transformer"). Theoretically you can merge timetable.dat too, but it would need some serious management. |
Full OTP json example. |
I don't know if I am able to 'support' these kind of debugging requests, but I'll see what I can do. |
@skinkie No problem. I think the issue here is that NONE stops are not correctly handled in |
@mr-tm I am accepting pull requests if you have some fixes ;) |
Hi!
First of all thank you for creating this library, from the description it truly seems like it's one of a kind! Couldn't believe it's possible to compress all netherland transit info to just 17MB from 246MB GTFS zip file!
I'm having an error while parsing GTFS file(http://gtfs.ovapi.nl/gtfs-nl.zip) on tdata branches(
tdata4
,tdata-cherokee
):I also noticed the processing with
gtfs2rrrr.py
is much longer (until the error) than the master. Also gtfs2rrrr.py, before the error hit, took about 11GB of ram.With
master
it was parsing just fine. (gtfsdb.py -> transfers.py -> timetable.py
)Which leads to the question - why are there so many different branches and which one would be the best for mobile deployment?
Thanks!
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