Bus Intersection Major Transit Stops, Branching Pathway - HQ Transit Corridor & Major Bus Stop Explorer

This is an alternate pathway for finding bus intersection Major Transit Stops. Here, we find pairs of routes that:

Are individually frequent (each route meets the frequency threshold by itself)
Have a shared trunk segment of 8 or more stops
Provide service for at least 5 kilometers not served by the other route.

If these criteria are met, we consider all stops in the shared trunk segment to be Major Transit Stops. Riders have access to multiple frequent routes that serve unique destinations.

import branching_derived_intersections as bdi
import create_aggregate_stop_frequencies as casf
from update_vars import analysis_date, GCS_FILE_PATH, MS_TRANSIT_THRESHOLD, SHARED_STOP_THRESHOLD, PUBLIC_FILE_PATH
import datetime as dt

import pandas as pd
import numpy as np
from shared_utils import webmap_utils, gtfs_utils_v2, rt_dates, portfolio_utils, rt_utils
import branca

from tqdm import tqdm
tqdm.pandas()
import lookback_wrappers
from segment_speed_utils import helpers
from calitp_data_analysis.gcs_pandas import GCSPandas
from calitp_data_analysis.gcs_geopandas import GCSGeoPandas
from calitp_data_analysis.utils import geojson_gcs_export
import calitp_data_analysis.magics

gcspd = GCSPandas()
gcsgp = GCSGeoPandas()

published_operators_dict = lookback_wrappers.read_published_operators(analysis_date)
trips, lookback_trips_ix = bdi.get_trips_with_route_dir(analysis_date, published_operators_dict)

shapes = helpers.import_scheduled_shapes(analysis_date, columns=["shape_array_key", "geometry"])

shapes = bdi.get_shapes_with_lookback(analysis_date, published_operators_dict, lookback_trips_ix)

max_arrivals_by_stop_single = gcspd.read_parquet(f"{GCS_FILE_PATH}max_arrivals_by_stop_single_route.parquet")

%%capture

singles_explode = bdi.get_filter_singles(max_arrivals_by_stop_single, MS_TRANSIT_THRESHOLD)

share_counts = {}
(
    singles_explode.groupby(["schedule_gtfs_dataset_key", "stop_id"]).progress_apply(
        casf.accumulate_share_count, share_counts=share_counts
    )
)

qualify_dict = {key: share_counts[key] for key in share_counts.keys() if share_counts[key] >= SHARED_STOP_THRESHOLD}
feeds_to_filter = np.unique([key.split("__")[0] for key in qualify_dict.keys()])

hcd_branching_stops = []
unique_qualify_pairs = []
for gtfs_dataset_key in feeds_to_filter:
    this_feed_pairs = bdi.evaluate_overlaps(
        gtfs_dataset_key, show_map=False, shapes=shapes, qualify_dict=qualify_dict
    )
    this_feed_stops = bdi.find_stops_this_feed(gtfs_dataset_key, max_arrivals_by_stop_single, this_feed_pairs)
    hcd_branching_stops += [this_feed_stops]
    unique_qualify_pairs += [this_feed_pairs]
hcd_branching_stops = pd.concat(hcd_branching_stops)
unique_qualify_pairs = pd.concat(unique_qualify_pairs)

unique_qualify_pairs.route_direction_pair = unique_qualify_pairs.route_direction_pair.astype(str)

unique_qualify_pairs = portfolio_utils.standardize_operator_info_for_exports(unique_qualify_pairs, analysis_date).drop(columns=['base64_url', 'source_record_id'])

cmap = branca.colormap.step.Accent_07

analysis_name_cmap = webmap_utils.categorical_cmap(cmap, unique_qualify_pairs.analysis_name.unique())

8 categories exceed 7 colors, colors will be duplicated

to_map_qualify = unique_qualify_pairs.copy()[unique_qualify_pairs.branching_qualify]
to_map_qualify['color'] = to_map_qualify.analysis_name.map(analysis_name_cmap)

branching_qualify_state = webmap_utils.set_state_export(to_map_qualify, filename = 'branching_qualify_intersections', subfolder='high_quality_transit_areas/',
                                                    map_title='Branching Trunk Segments (Intersections)', overwrite=True,
                                                       manual_centroid=[37.336813156889704, -121.88911054161129])

stops = gcsgp.read_parquet(f"{GCS_FILE_PATH}stops_with_lookback.parquet")

gdf = stops.merge(hcd_branching_stops, on=['schedule_gtfs_dataset_key', 'stop_id'])
gdf['color'] = [(0, 0, 0)] * len(gdf)

branching_with_stops_state = webmap_utils.set_state_export(gdf, filename = 'branching_qualify_intersections_stops', subfolder='high_quality_transit_areas/',
                                                    existing_state = branching_qualify_state, map_title='Branching Trunk Segments (Intersections) w/ Stops', overwrite=True,
                                                       manual_centroid=[37.336813156889704, -121.88911054161129])

%%capture_parameters
human_date = dt.date.fromisoformat(analysis_date).strftime('%B %d %Y (%A)')
human_date

Current Analysis Date¶

February 11 2026 (Wednesday)

If we are missing data on that date for a particular operator, we will patch in data from the previous three months. Currently patching in:

lookback_wrappers.read_published_operators(analysis_date)

{'2025-12-17': ['Bay Area 511 South San Francisco Shuttle Schedule',
  'Baldwin Park Schedule'],
 '2026-01-14': ['Bay Area 511 Mountain View Community Shuttle Schedule',
  'Victor Valley Schedule']}

Branching Trunk Segments Maps¶

webmap_utils.render_spa_link(branching_qualify_state['spa_link'], text='Trunk Segment Map')

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webmap_utils.render_spa_link(branching_with_stops_state['spa_link'], text= 'Trunk Segment Stop Map')

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webmap_utils.display_spa_map(branching_with_stops_state['spa_link'], title= 'Trunk Segment Stop Map')

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Full List of Trunk Segments Evaluated¶

unique_qualify_pairs.head(10)

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path = PUBLIC_FILE_PATH
fname = 'unique_qualify_pairs.geojson'

geojson_gcs_export(unique_qualify_pairs, path, fname)

Download full geojson

Individual Evaluation Maps¶

for gtfs_dataset_key in feeds_to_filter:
    this_feed_pairs = bdi.evaluate_overlaps(
        gtfs_dataset_key, show_map=True, shapes=shapes, qualify_dict=qualify_dict
    )

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