def generate_trip_stats_aggregation(feed):
# get geometry by stop for distance measurement
geometry_by_stop = gtfstk.build_geometry_by_stop(feed, use_utm=True)
def trip_stats_aggregation(group):
d = OrderedDict()
keys = [
'route_id', 'route_short_name', 'route_long_name', 'route_mkt', 'route_direction',
'route_alternative', 'agency_id', 'agency_name', 'route_type', 'direction_id',
'shape_id', 'line_type', 'line_type_desc', 'cluster_id',
'cluster_name', 'cluster_sub_desc',
]
for key in keys:
d[key] = group[key].iat[0]
d['num_stops'] = group.shape[0]
d['start_time'] = group['departure_time'].iat[0]
d['end_time'] = group['departure_time'].iat[-1]
keys_for_start_and_end = [
'stop_id', 'stop_code', 'stop_name', 'stop_desc', 'stop_lat', 'stop_lon',
]
keys_for_all = [
'stop_code', 'stop_id', 'stop_desc_city', 'stop_name',
]
for key in keys_for_start_and_end:
d[f'start_{key}'] = group[key].iat[0]
d[f'end_{key}'] = group[key].iat[-1]
d['start_stop_city'] = group['stop_desc_city'].iat[0]
d['end_stop_city'] = group['stop_desc_city'].iat[-1]
d['start_zone'] = group['zone_name'].iat[0]
d['end_zone'] = group['zone_name'].iat[-1]
d['num_zones'] = group.zone_name.nunique()
d['num_zones_missing'] = group.zone_name.isnull().sum()
dist = geometry_by_stop[d['start_stop_id']].distance(
geometry_by_stop[d['end_stop_id']])
d['is_loop'] = int(dist < 400)
d['duration'] = (d['end_time'] - d['start_time']) / 3600
d['all_stop_latlon'] = ';'.join(str(x) + ',' + str(y) for x, y in
zip(group['stop_lat'].tolist(), group['stop_lon'].tolist()))
for key in keys_for_all:
d[f'all_{key}'] = ';'.join(group[key].tolist())
return pd.Series(d)
return trip_stats_aggregation
def compute_trip_stats_partridge(feed, zones):
"""
Parameters
----------
feed : partridge feed
zones: DataFrame with stop_code to zone_name mapping
Returns
-------
DataFrame with the following columns:
- ``'trip_id'``
- ``'route_id'``
- ``'route_short_name'``
- ``'route_short_name'``
- ``'agency_id'``
- ``'agency_name'``
- ``'route_long_name'``
- ``'route_type'``
- ``'direction_id'``
- ``'shape_id'``
- ``'num_stops'``: number of stops on trip
- ``'start_time'``: first departure time of the trip
- ``'end_time'``: last departure time of the trip
- ``'start_stop_id'``: stop ID of the first stop of the trip
- ``'end_stop_id'``: stop ID of the last stop of the trip
- ``'start_stop_name'``: stop name of the first stop of the trip
- ``'end_stop_name'``: stop name of the last stop of the trip
- ``'start_stop_code'``: stop code of the first stop of the trip
- ``'end_stop_code'``: stop code of the last stop of the trip
- ``'start_stop_lat'``: ``start_stop_lat`` of the first stop of the trip
- ``'start_stop_lon'``: ``start_stop_lon`` of the first stop of the trip
- ``'end_stop_lat'``: ``end_stop_lat`` of the last stop of the trip
- ``'end_stop_lon'``: ``end_stop_lon`` of the last stop of the trip
- ``'start_zone'``: zone name of the first stop of the trip
- ``'end_zone'``: zone name of the last stop of the trip
- ``'num_zones'``: ``num_zones`` of the first stop of the trip
- ``'num_zones_missing'``: ``num_zones_missing`` of the first stop of the trip
- ``'is_loop'``: 1 if the start and end stop are less than 400m apart and
0 otherwise
- ``'distance'``: distance of the trip in ``feed.dist_units``;
contains all ``np.nan`` entries if ``feed.shapes is None``
- ``'duration'``: duration of the trip in hours
- ``'speed'``: distance/duration
TODO: this is not true here, we're only using shape_dist_traveled
TODO: implement or drop from docs
If ``feed.stop_times`` has a ``shape_dist_traveled`` column with at
least one non-NaN value and ``compute_dist_from_shapes == False``,
then use that column to compute the distance column.
Else if ``feed.shapes is not None``, then compute the distance
column using the shapes and Shapely.
Otherwise, set the distances to NaN.
If route IDs are given, then restrict to trips on those routes.
Notes
-----
- Assume the following feed attributes are not ``None``:
* ``feed.trips``
* ``feed.routes``
* ``feed.stop_times``
* ``feed.shapes`` (optionally)
* Those used in :func:`.stops.build_geometry_by_stop`
- Calculating trip distances with ``compute_dist_from_shapes=True``
seems pretty accurate. For example, calculating trip distances on
`this Portland feed
<https://transitfeeds.com/p/trimet/43/1400947517>`_
using ``compute_dist_from_shapes=False`` and
``compute_dist_from_shapes=True``,
yields a difference of at most 0.83km from the original values.
"""
f = feed.trips
f = (f[['route_id', 'trip_id', 'direction_id', 'shape_id']]
.merge(feed.routes[['route_id', 'route_short_name', 'route_long_name',
'route_type', 'agency_id', 'route_desc']])
.merge(feed.agency[['agency_id', 'agency_name']], how='left', on='agency_id')
.merge(feed.stop_times)
.merge(feed.stops[['stop_id', 'stop_name', 'stop_lat', 'stop_lon', 'stop_code', 'stop_desc']])
.merge(zones, how='left')
.sort_values(['trip_id', 'stop_sequence'])
# .assign(departure_time=lambda x: x['departure_time'].map(
# hp.timestr_to_seconds)
# )
)
f[['route_mkt', 'route_direction', 'route_alternative']] = f['route_desc'].str.split('-', expand=True)
f = f.drop('route_desc', axis=1)
geometry_by_stop = gtfstk.build_geometry_by_stop(feed, use_utm=True)
g = f.groupby('trip_id')
def my_agg(group):
d = OrderedDict()
d['route_id'] = group['route_id'].iat[0]
d['route_short_name'] = group['route_short_name'].iat[0]
d['route_long_name'] = group['route_long_name'].iat[0]
d['route_mkt'] = group['route_mkt'].iat[0]
d['route_direction'] = group['route_direction'].iat[0]
d['route_alternative'] = group['route_alternative'].iat[0]
d['agency_id'] = group['agency_id'].iat[0]
d['agency_name'] = group['agency_name'].iat[0]
d['route_type'] = group['route_type'].iat[0]
d['direction_id'] = group['direction_id'].iat[0]
d['shape_id'] = group['shape_id'].iat[0]
d['num_stops'] = group.shape[0]
d['start_time'] = group['departure_time'].iat[0]
d['end_time'] = group['departure_time'].iat[-1]
d['start_stop_id'] = group['stop_id'].iat[0]
d['end_stop_id'] = group['stop_id'].iat[-1]
d['start_stop_code'] = group['stop_code'].iat[0]
d['end_stop_code'] = group['stop_code'].iat[-1]
d['start_stop_name'] = group['stop_name'].iat[0]
d['end_stop_name'] = group['stop_name'].iat[-1]
d['start_stop_desc'] = group['stop_desc'].iat[0]
d['end_stop_desc'] = group['stop_desc'].iat[-1]
d['start_stop_lat'] = group['stop_lat'].iat[0]
d['start_stop_lon'] = group['stop_lon'].iat[0]
d['end_stop_lat'] = group['stop_lat'].iat[-1]
d['end_stop_lon'] = group['stop_lon'].iat[-1]
d['start_zone'] = group['zone_name'].iat[0]
d['end_zone'] = group['zone_name'].iat[-1]
d['num_zones'] = group.zone_name.nunique()
d['num_zones_missing'] = group.zone_name.isnull().sum()
dist = geometry_by_stop[d['start_stop_id']].distance(
geometry_by_stop[d['end_stop_id']])
d['is_loop'] = int(dist < 400)
d['duration'] = (d['end_time'] - d['start_time']) / 3600
return pd.Series(d)
h = g.apply(my_agg)
h['distance'] = g.shape_dist_traveled.max()
# Reset index and compute final stats
h = h.reset_index()
h['speed'] = h['distance'] / h['duration'] / 1000
h[['start_time', 'end_time']] = (
h[['start_time', 'end_time']].applymap(
lambda x: gtfstk.helpers.timestr_to_seconds(x, inverse=True))
)
return h
def compute_trip_stats_partridge(feed, zones):
"""
Parameters
----------
feed : partridge feed
zones: DataFrame with stop_code to zone_name mapping
Returns
-------
DataFrame with the following columns:
- ``'trip_id'``
- ``'route_id'``
- ``'route_short_name'``
- ``'route_short_name'``
- ``'agency_id'``
- ``'agency_name'``
- ``'route_long_name'``
- ``'route_type'``
- ``'direction_id'``
- ``'shape_id'``
- ``'num_stops'``: number of stops on trip
- ``'start_time'``: first departure time of the trip
- ``'end_time'``: last departure time of the trip
- ``'start_stop_id'``: stop ID of the first stop of the trip
- ``'end_stop_id'``: stop ID of the last stop of the trip
- ``'start_stop_name'``: stop name of the first stop of the trip
- ``'end_stop_name'``: stop name of the last stop of the trip
- ``'start_stop_code'``: stop code of the first stop of the trip
- ``'end_stop_code'``: stop code of the last stop of the trip
- ``'start_stop_lat'``: ``start_stop_lat`` of the first stop of the trip
- ``'start_stop_lon'``: ``start_stop_lon`` of the first stop of the trip
- ``'end_stop_lat'``: ``end_stop_lat`` of the last stop of the trip
- ``'end_stop_lon'``: ``end_stop_lon`` of the last stop of the trip
- ``'start_zone'``: zone name of the first stop of the trip
- ``'end_zone'``: zone name of the last stop of the trip
- ``'num_zones'``: ``num_zones`` of the first stop of the trip
- ``'num_zones_missing'``: ``num_zones_missing`` of the first stop of the trip
- ``'is_loop'``: 1 if the start and end stop are less than 400m apart and
0 otherwise
- ``'distance'``: distance of the trip in ``feed.dist_units``;
contains all ``np.nan`` entries if ``feed.shapes is None``
- ``'duration'``: duration of the trip in hours
- ``'speed'``: distance/duration
TODO: this is not true here, we're only using shape_dist_traveled
TODO: implement or drop from docs
If ``feed.stop_times`` has a ``shape_dist_traveled`` column with at
least one non-NaN value and ``compute_dist_from_shapes == False``,
then use that column to compute the distance column.
Else if ``feed.shapes is not None``, then compute the distance
column using the shapes and Shapely.
Otherwise, set the distances to NaN.
If route IDs are given, then restrict to trips on those routes.
Notes
-----
- Assume the following feed attributes are not ``None``:
* ``feed.trips``
* ``feed.routes``
* ``feed.stop_times``
* ``feed.shapes`` (optionally)
* Those used in :func:`.stops.build_geometry_by_stop`
- Calculating trip distances with ``compute_dist_from_shapes=True``
seems pretty accurate. For example, calculating trip distances on
`this Portland feed
<https://transitfeeds.com/p/trimet/43/1400947517>`_
using ``compute_dist_from_shapes=False`` and
``compute_dist_from_shapes=True``,
yields a difference of at most 0.83km from the original values.
"""
f = feed.trips
f = (f[['route_id', 'trip_id', 'direction_id', 'shape_id']]
.merge(feed.routes[['route_id', 'route_short_name', 'route_long_name',
'route_type', 'agency_id', 'route_desc']])
.merge(feed.agency[['agency_id', 'agency_name']], how='left', on='agency_id')
.merge(feed.stop_times)
.merge(feed.stops[['stop_id', 'stop_name', 'stop_lat', 'stop_lon', 'stop_code', 'stop_desc']])
.merge(zones, how='left')
.sort_values(['trip_id', 'stop_sequence'])
# .assign(departure_time=lambda x: x['departure_time'].map(
# hp.timestr_to_seconds)
# )
)
# parse route_desc
f[['route_mkt', 'route_direction', 'route_alternative']] = f['route_desc'].str.split('-', expand=True)
f = f.drop('route_desc', axis=1)
# parse stop_desc
stop_desc_fields = {'street': 'רחוב',
'city': 'עיר',
'platform': 'רציף',
'floor': 'קומה'}
stop_desc_prefix = 'stop_desc_'
STOP_DESC_RE = ''
for n, fld in stop_desc_fields.items():
STOP_DESC_RE += fld + f':(?P<{stop_desc_prefix + n}>.*)'
sd = f.stop_desc.str.extract(STOP_DESC_RE).apply(lambda x: x.str.strip())
f = pd.concat([f, sd], axis=1)
# get geometry by stop for distance measurement
geometry_by_stop = gtfstk.build_geometry_by_stop(feed, use_utm=True)
g = f.groupby('trip_id')
def my_agg(group):
d = OrderedDict()
d['route_id'] = group['route_id'].iat[0]
d['route_short_name'] = group['route_short_name'].iat[0]
d['route_long_name'] = group['route_long_name'].iat[0]
d['route_mkt'] = group['route_mkt'].iat[0]
d['route_direction'] = group['route_direction'].iat[0]
d['route_alternative'] = group['route_alternative'].iat[0]
d['agency_id'] = group['agency_id'].iat[0]
d['agency_name'] = group['agency_name'].iat[0]
d['route_type'] = group['route_type'].iat[0]
d['direction_id'] = group['direction_id'].iat[0]
d['shape_id'] = group['shape_id'].iat[0]
d['num_stops'] = group.shape[0]
d['start_time'] = group['departure_time'].iat[0]
d['end_time'] = group['departure_time'].iat[-1]
d['start_stop_id'] = group['stop_id'].iat[0]
d['end_stop_id'] = group['stop_id'].iat[-1]
d['start_stop_code'] = group['stop_code'].iat[0]
d['end_stop_code'] = group['stop_code'].iat[-1]
d['start_stop_name'] = group['stop_name'].iat[0]
d['end_stop_name'] = group['stop_name'].iat[-1]
d['start_stop_desc'] = group['stop_desc'].iat[0]
d['end_stop_desc'] = group['stop_desc'].iat[-1]
d['start_stop_lat'] = group['stop_lat'].iat[0]
d['start_stop_lon'] = group['stop_lon'].iat[0]
d['end_stop_lat'] = group['stop_lat'].iat[-1]
d['end_stop_lon'] = group['stop_lon'].iat[-1]
d['start_stop_city'] = group['stop_desc_city'].iat[0]
d['end_stop_city'] = group['stop_desc_city'].iat[-1]
d['start_zone'] = group['zone_name'].iat[0]
d['end_zone'] = group['zone_name'].iat[-1]
d['num_zones'] = group.zone_name.nunique()
d['num_zones_missing'] = group.zone_name.isnull().sum()
dist = geometry_by_stop[d['start_stop_id']].distance(
geometry_by_stop[d['end_stop_id']])
d['is_loop'] = int(dist < 400)
d['duration'] = (d['end_time'] - d['start_time']) / 3600
d['all_stop_latlon'] = ';'.join(str(x)+','+str(y) for x, y in
zip(group['stop_lat'].tolist(), group['stop_lon'].tolist()))
d['all_stop_code'] = ';'.join(group['stop_code'].tolist())
d['all_stop_id'] = ';'.join(group['stop_id'].tolist())
d['all_stop_desc_city'] = ';'.join(group['stop_desc_city'].tolist())
return pd.Series(d)
h = g.apply(my_agg)
h['distance'] = g.shape_dist_traveled.max()
# Reset index and compute final stats
h = h.reset_index()
h['speed'] = h['distance'] / h['duration'] / 1000
h[['start_time', 'end_time']] = (
h[['start_time', 'end_time']].applymap(
lambda x: gtfstk.helpers.timestr_to_seconds(x, inverse=True))
)
return h