Comments (6)
Some thoughts about this task: זהירות חפירה
I am not sure all these details should be implemented in the first version. But these are things that we discussed so I thought they should be documented.
Initial metrics that we could use are:
- If you arrive by train, what's the average time to the first bus that arrives.
- If you arrive by train, what's the average time over all bus lines.
For example, say there are two bus lines serving a station, and the schedule is:
Train 1 9:00
Line 1 9:04
Line 2 9:08
Train 2 9:20
Line 2 9:25
Line 2 9:30
Line 1 9:35
With metric 1, the result will be 4.5m (average of 4 minutes for train 1, and 5 minutes for train 2).
With metric 2, the result will be 8m (average of 6 minutes for train 1, and 10 minutes for train 2)
Both metrics aren't perfect, but maybe they are a good starting point to see how the data looks like.
Dividing the day into windows
We should divide the day into windows (e.g. 1 hour) and calculate the metrics separately for each hour. That would be more informative than having a single daily average.
*Transfer time: *
If a train arrives at 8:00 and the bus departs at 8:01, it's very probable that people would miss the bus, because it takes time to get out of the station. So we need a transfer constant TC. If a train arrives at time t1 and the bus leaves at time t2, the actual wait time is t2 - t1 - TC.
We can start with TC=5m.
Maximum wait time
Say there is a specific bus line that operates only in the afternoons. If a train arrives at 08:00am, no one is going to wait 8 hours for a bus. So we need a constant MAX_WAIT. If the wait > MAX_WAIT, we can ignore this bus.
Separate first stop from rest of stops
We know that the regulator (משרד התחבורה) only plans bus departure times. The data in the GTFS for the second, third etc. stop is not precise. So we should calculate the metrics twice: once for all buses, and once only for buses that start at the station.
From bus to train
I described metrics for transfers from train to bus. Similar metrics could be calculated for transfer from bus to train (arriving to the station by bus and then boarding a train).
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And BTW, @MYank0 and Uriya P are currently looking at this task.
I am going to try to load the data into the DB and make it available through re:dash.
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Hi,
I was thinking about using a simplified frequency metric with peaks for hub
stations, although the average per hour method would work pretty well. It
would be helpful to look at affected areas or maybe to pull from waze data
also.
Chag Sameach
Nathan
On Oct 19, 2016 1:33 AM, "Daphna Shezaf" [email protected] wrote:
And BTW, @MYank0 https://github.com/MYank0 and Uriya P are currently
looking at this task.
I am going to try to load the data into the DB and make it available
through re:dash.
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I am happy to say GTFS data (loaded from the file of October 16th) is now available in the database.
This can be accessed through re:dash
I also added to the database the data about which bus stops are within walking distance from train stations (station_walking_distance table).
This means that the data in the files I originally posted can be retrieved from the database using SQL. This file has examples of the queries.
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@MYank0 - what's our status re bus2train? Can we close this issue?
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Final report is here I think we can close the issue and for now. Maybe when we have real time data we will have more meaningful insights.
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Related Issues (20)
- Can't access siri br file HOT 2
- Duplicates rows in the db HOT 1
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- stride api siri vehicle locations should allow to filter by lont/lat bounding box HOT 1
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- error in stride api gtfs_stop/get HOT 2
- השלמות stride
- stride additional tasks HOT 1
- all processing should be idempotent and make sure data is available for last X days HOT 2
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- StrideRequestFailedException: Failure response from Stride API (500): QueuePool limit of size 5 overflow 10 reached, connection timed out, timeout 30.00 HOT 1
- add date/timestamp in /gtfs_rides/list HOT 3
- stride-api feature request: add parameter to siri_ride to enable querying gtfs_ride_id HOT 1
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