Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/bmander/busbuzzard

Inference of probabilistic schedules from empirical data about transit vehicles.
https://github.com/bmander/busbuzzard

Last synced: about 2 months ago
JSON representation

Inference of probabilistic schedules from empirical data about transit vehicles.

Lists

README 

        
Busbuzzard

==========



Inference of probabilistic schedules from empirical data about transit vehicles.



## Massage GPS data into a set of observed stop_time events



#### Step 0: Get the data



* A CSV of points logged from the NextBus API.

  * For example: https://dl.dropboxusercontent.com/u/1158424/route_5.csv.zip

  * Try to find someone with a larger data dump, to get a larger data dump.

* An unzipped GTFS that describes the service recorded in the NextBus CSV

  * Try http://www.gtfs-data-exchange.com/ for current and past GTFS feeds



#### Step 1: Slice a route out of the NextBus CSV dump



`$ python slice_route.py data/nextbus.csv data/route_27.csv 27`



#### Step 2: Chain NextBus vehicle fixes into strings representing trip instances



`$ python chain.py data/route_27.csv data/route_27_chained.csv`



#### Step 3: Assign NextBus fix chains to GTFS trips



`$ python python match.py data/route_27_chained.csv data/your_gtfs_dir 27 data/route_27_your_gtfs.matches`



#### Step 4: Cache GTFS patterns



`$ python find_gtfs_patterns.py data/your_gtfs_dir data/your_gtfs_patterns.json`



#### Step 5: Compute passbys



`$ python passby.py data/your_gtfs_dir data/your_gtfs_patterns.json data/route_27_chained.csv data/route_27_your_gtfs.matches data/route_27_your_gtfs_passbys.csv`



## Visualize scheduled and observed stop_time events



Run viz_stop.py without stop, pattern, or service_id qualifiers



`python viz_stop.py data/route_5_fallwinter_passbys.csv data/sfmta_fallwinter_2012`



Which complains that it needs a stop_id, but helpfully gives you some options, like:



>Pick a stop. Here are some options:


>stop:3923	 count:1386


>stop:3927	 count:18021


>stop:4228	 count:21447


>stop:4229	 count:23270


>stop:4224	 count:20978


>stop:4225	 count:23290



Pick one and run again with a stop



`python viz_stop.py data/route_5_fallwinter_passbys.csv data/sfmta_fallwinter_2012 4228`



Now it complains that you need a direction, but supplies some, like:



>Pick a direction. Here are some options:


>direction:1	 count:21447



Pick one and run again, then it compains that you need a service_id, on account of how a pattern can run on different service_ids



`python viz_stop.py data/route_5_fallwinter_passbys.csv data/sfmta_fallwinter_2012 4228 1`



>Pick a service_id. Here are some options:


>service_id:1	 count:15780


>service_id:3	 count:2896


>service_id:2	 count:2771



Finally select a service_id:



`python viz_stop.py data/route_5_fallwinter_passbys.csv data/sfmta_fallwinter_2012 4228 1 1`



Which brings up a pyplot window illustrating a timeline of every scheduled and observed event at stop 4228, on direction 1, with a service_id of 1.



![alt tag](https://raw.github.com/bmander/busbuzzard/master/static/observed_schedule.png)



## Visualize empirical schedule



`python viz_waits.py data/route_5_fallwinter_passbys.csv data/sfmta_fallwinter_2012/ 4228 1 1`



Creates a matplotlib window with contents like:



![alt tag](https://raw.github.com/bmander/busbuzzard/master/static/empiricalsched.png)



Each line plots the quantile of the time to arrival of the next vehicle. The X axis represents time, in minutes, since midnight. The Y axis is seconds wait. The blue line is the time under which a vehicle arrived 25% of the time. The green line represents the amount of time under which a vehicle arrived half the time. Red, 75%, and teal 95%.