Translation to Python: useful_macros and transit boarding summary

Database/Submit_Full_Regional_Model_SOLA.bat

@@ -454,6 +454,7 @@ if "%transitAsmt%" EQU "T" (
         if %ERRORLEVEL% GTR 0 (goto issue)
         @ECHO -- Completed Select Line Boarding Analysis >> model_run_timestamp.txt
     )
+    call python transit_asmt_macros\summarize_transit_boardings.py
     @ECHO End Transit Assignment: %date% %time% >> model_run_timestamp.txt
 )
 goto last

Database/transit_asmt_macros/summarize_transit_boardings.py

@@ -0,0 +1,177 @@
+'''
+SUMMARIZE_TRANSIT_BOARDINGS.PY
+    Craig Heither, 10-25-2022
+    Translated from Emme macro to Python by Tim O'Leary, 9-26-2025
+
+    Summarize transit boardings for congested transit assignment.
+    No inputs necessary-- reads batch_file.yaml to determine scen numbers.
+    Will not run unless transit assignment has been completed.
+'''
+
+#libraries
+import os
+import sys
+from pathlib import Path
+import yaml
+import pandas as pd
+
+#startup emme
+proj_dir = Path(__file__).resolve().parents[2]
+db = proj_dir.joinpath('Database')
+sys.path.append(str(proj_dir.joinpath('Scripts')))
+from tbmtools import project as tbm
+
+#connect to modeller
+modeller = tbm.connect(proj_dir)
+emmebank = modeller.emmebank
+
+#output location
+out_boarding_csv = os.path.join(
+    db, 'transit_asmt_macros/report/Boarding_summary.csv'
+)
+out_transit_punch = os.path.join(
+    db, 'transit_asmt_macros/report/transit_punch_segment.csv'
+)
+
+if os.path.exists(out_boarding_csv):
+    os.remove(out_boarding_csv)
+
+#get scenario info from batch_file.yaml for transit asmt scenarios
+db = proj_dir.joinpath('Database')
+with open(os.path.join(db, 'batch_file.yaml')) as f:
+    lines_without_backslashes = ''.join([line.replace('\\','/') for line in f])
+    config = yaml.safe_load(lines_without_backslashes)
+yr = str(config['scenario_code'])[0]  # e.g., '2' from '200'
+
+trnt_scens = {
+    f'Night (6pm-6am)': f'{yr}21',
+    f'AM (6am-9am)': f'{yr}23',
+    f'Midday (9am-4pm)': f'{yr}25',
+    f'PM (4pm-6pm)': f'{yr}27',
+}
+
+tr_dfs = []
+
+for period_name, scen_num in trnt_scens.items():
+
+    scen = emmebank.scenario(scen_num)
+    if scen is None:
+        raise ValueError(f'Scenario {scen_num} not found in Emmebank')
+
+    network = scen.get_network()
+    links = network.links()
+
+    trpunch_list = []
+    for link in network.links():
+        segments = link.segments() #empty if link does not have transit segments
+        inode = network.node(link.i_node)
+        jnode = network.node(link.j_node)
+        #for each segment (if it has any), add this info to table:
+        for segment in segments:
+            line = network.transit_line(str(segment.id).split('-')[0])
+            seg = [
+                link.id,
+                segment.id,
+                link.length,
+                line.headway,
+                segment['transit_boardings'],
+                segment['transit_volume'],
+                segment['data1'],
+                inode['@zone'],
+                jnode['@zone']
+            ]
+
+            trpunch_list.append(seg)
+
+    columns = ['id', 'segment_id', 'length', 'headway', 'transit_boardings','transit_volume','ltime','inode_zone','jnode_zone']
+    trpunch = pd.DataFrame(data=trpunch_list, columns=columns)
+
+    trpunch.eval('pmt = transit_volume * length', inplace=True)
+    trpunch.eval('pht = transit_volume * ltime / 60', inplace=True)
+
+    #use transit line info to determine mode
+    def line_mode(x):
+        '''
+        function for .map() method used directly below this function 
+        to determine mode name from segment_id
+
+        segment_id format: [alpha-code][5-digit number], e.g., 'b12535', 'cbl10011'
+
+        b -> CTA local bus
+        e -> CTA express
+        p -> Pace regular bus
+        l -> Pace feeder bus
+        q -> Pace express bus
+        c** -> CTA rail lines (3-letter code)
+        m** -> Metra lines (3-letter code)
+
+        returns: the english name representation of the mode code
+
+        '''
+
+        line = x.split('-')[0]
+        name = ''.join([char for char in line if char.isalpha()])
+        name_key = {
+            'cbl': 'CTA Blue Line',
+            'cbr': 'CTA Brown Line',
+            'cg': 'CTA Green Line',
+            'cor': 'CTA Orange Line',
+            'cpk': 'CTA Pink Line',
+            'cpr': 'CTA Purple Line',
+            'crd': 'CTA Red Line',
+            'cye': 'CTA Yellow Line',
+            'mbn': 'Metra BNSF',
+            'mhc': 'Metra Heritage Corridor',
+            'mme': 'Metra Electric',
+            'mmn': 'Metra Milwaukee North',
+            'mmw': 'Metra Milwaukee West',
+            'mnc': 'Metra North Central',
+            'mri': 'Metra Rock Island',
+            'msw': 'Metra SouthWest Service',
+            'mun': 'Metra UP North',
+            'mnw': 'Metra UP Northwest',
+            'muw': 'Metra UP West',
+            'mss': 'NICTD South Shore',
+            'b': 'CTA Regular Bus',
+            'e': 'CTA Express Bus',
+            'p': 'Pace Regular Bus',
+            'q': 'Pace Express Bus',
+            'l': 'Pace Feeder Bus',
+        }
+        nam = [code for code in name_key.keys() if code in name]
+        nam = max(nam, key=len) if len(nam) > 0 else None
+        return name_key[nam]
+
+    trpunch['mode'] = trpunch['segment_id'].map(line_mode)
+
+    if len(trpunch.loc[trpunch['mode'].isnull()]) > 0:
+        print(f'SOME TRANSIT SEGMENTS WERE NOT MAPPED TO A MODE!')
+        print(trpunch.loc[trpunch['mode'].isnull()])
+        raise ValueError('Some transit segments were not mapped to a mode (must be c, m, p, q, l, b, or e). Look at printed output of errors above.')
+
+    trpunch['tod'] = scen_num
+    trpunch['tod_name'] = period_name
+
+    tr_dfs.append(trpunch)
+
+tr_all = pd.concat(tr_dfs, ignore_index=True)
+
+#output all segment info to csv (similar to punch link)
+tr_all.to_csv(out_transit_punch, index=False)
+
+#summary by time-of-day
+summary_tod = tr_all.groupby(['tod_name', 'tod', 'mode']).agg({
+    'transit_boardings':'sum',
+    'pmt':'sum'
+}).reset_index()
+#total daily
+summary_daily = summary_tod.groupby('mode').agg({
+    'transit_boardings':'sum',
+    'pmt':'sum'
+}).reset_index()
+summary_daily['tod'] = 'All'
+summary_daily['tod_name'] = 'Daily'
+
+summary = pd.concat([summary_tod, summary_daily], ignore_index=True)
+summary.to_csv(out_boarding_csv, index=False)
+print(f'Summary transit boarding information written to: {out_boarding_csv}')