nate-wessel / routing Goto Github PK
View Code? Open in Web Editor NEWanalysis of retrospective transit route choice alternatives
Home Page: https://osf.io/preprints/socarxiv/3r4p6/
analysis of retrospective transit route choice alternatives
Home Page: https://osf.io/preprints/socarxiv/3r4p6/
207 -> 86
retro | entropy:0.76 | trips:443
Pr:0.9, It:{w29,s2615,r43,s2464,w135,s14499,r2,s14525,w69}
...is not nearly as common as...
{w34,s1880,r43,s2280,w135,s14499,r2,s14525,w69}
I'm pulling weekend trips out of the DB, which is not good
this may produce unexpected behaviour. I'm not sure why this was removed, but times are not being bounded by a potential walk time.
Seek to minimize initial wait times.
Something like:
all_possible_trips = []
for itinerary in best_routes:
trips = get_from_db()
all_possible_trips.extend(trips)
I believe this has to do with the presence of many suboptimal trips
double check the algorithm and maybe clean up and document
Make sure all times are handled correctly
More can be moved into Itinerary from Path, I think.
I can export this to CSV and make a quick plot in R
show sets inside parentheses
Base itin choice on initial wait/walk time + mean time in case of departure time matches.
Also allow for walking option.
Ensure that walk times are treated properly and consistently throughout the code.
I need to understand why this is.
This was added to the CSVs to make GTFS files, but would need to be added to the DB directly if I want to use DB based queries.
See 12 -> 281
Traceback (most recent call last):
File "access.py", line 21, in <module>
access(12)
File "access.py", line 16, in access
print( len(od.access()),'trips' )
File "/home/nate/routing/OD.py", line 150, in access
trips = db.all_itinerary_trips(self.retro_itin(0))
File "/home/nate/routing/db.py", line 107, in all_itinerary_trips
'final_depth':len(itin.segments)
psycopg2.ProgrammingError: cannot subscript type unknown because it is not an array
This is of course impossible. Seen in the all_times.csv output.
> sum(t$any>t$hab,na.rm=T) / nrow(t)
[1] 0.06619754
> sum(t$any>t$real,na.rm=T) / nrow(t)
[1] 0.01286895
on tabbed lines
It's too big
Route variation little matters if the time differences are negligible. How can I determine if this is so?
12 -> 316
retro | entropy:3.84 | trips:339
...
Pr:0.21, It:{w134,s3177,r47,s3184,w57,s14477,r2,s14482,w828}
...
Pr:0.05, It:{w137,s3177,r47,s3184,w57,s14477,r2,s14482,w828}
...
start from the edge of the time window
But also allow it still to search all possible combinations of points
This will need to go something like:
access(o):
for d:
od = OD(o,d)
od.access(x,y,z)
yadda yadda
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