CMP_Scripts

Data preparation scripts for the Congestion Management Program (CMP)

Definitions

• monitoring period: Tue/Wed/Thur in April and May (excluding holidays etc)
• peak periods (in use since (at least) CMP 2007)
  • AM: 7-9am
  • PM: 4:30-6:30pm
  • the earliest instance I could find for this definition was in the LOS Monitoring appendix to CMP 2007, where it also said that the 2004 and 2006 studies used 4-6pm as the PM peak (Carter Burgess 2007 CMP Spring 2007 Level of Service Monitoring, p2; p172 in 2007_CMP_ALL.pdf)

Map / GIS

• The CMP road network data (for the biennial report) is at Q:\GIS\Transportation\Roads\CMP\cmp_roadway_segments* (see README explaining the differet files in that directory).
• Note that CMP and CHAMP segments are different

Data directories

2001-2023 data are stored in Q:\CMP\LOS Monitoring 20yy. Starting with the 2023 CMP cycle, I'm trying to start storing metrics calculation outputs (and certain inputs) in Q:\CMP\metrics

Raw data stored elsewhere

• speed
  • floating car runs: Q:\Data\Observed\Streets\Speed\CMP-floating_car_run\
• transit
  • GTFS: Q:\Data\Networks\Transit\Muni\GTFS

Sections/Metrics

Transit

APC

Download

1. Reach out to SFMTA Transit Performance and Analytics (as of 2023: Simon.Hochberg@sfmta.com) for APC data for April & May of the CMP year.
2. Use TD&A's Box account (username: modeling@sfcta.org) for the transfer.
3. Save the data to Q:\Data\Observed\Transit\Muni\APC\. (maybe also request GTFS from SFMTA)

Parsing

• requires a GTFS stops GIS file, see GTFS section below.
• Run transit_apc_volume_and_speed.py with config files as in transit/apc/configs. However, it's unclear to Chun Ho (Sept 2023) how the postprocessing csv file is generated.
• use figures.ipynb to create the figures for the report

GTFS

Download

As of Aug 2023: the MTC feed (which is the official feed for SFMTA) at https://511.org/open-data/transit (Bulk Data Feed -> GTFS Feed Download) has a service_id for each day of service in calendar_dates.txt, and there is no calendar.txt. I.e., no regular service is defined, and all services are defined de novo for each day (i.e. MTC does not give GTFS data based on scheduled service). Also, as of Aug 2023, the historic data feed has to include all operators (opearator_id=RG for regional). i.e. http://api.511.org/transit/datafeeds?api_key=[your_key]&operator_id=RG&historic=[YYYY-MM] or http://api.511.org/transit/datafeeds?api_key=[your_key]&operator_id=SF.

The latest SFMTA GTFS (scheduled) is also available on Data SF: https://data.sfgov.org/Transportation/SFMTA-General-Transit-Feed-Specification-GTFS-Prod/dni7-qpv3

For more historical SFMTA GTFS data (scheduled rather than each day as a special case as on 511.org), reach out to SFMTA (as of 2023: Simon.Hochberg@sfmta.com)

Place the data at Q:\Data\Networks\Transit\Muni\GTFS\.

Analysis

• For transit coverage calculations: Open calendar.txt to find the weekday service_id (for the period of analysis).
• For transit volume and speed calculations: Use the GTFS Go plugin in QGIS to get the stops GIS file for input to transit_apc_volume_and_speed.py. Though it seems like transit/apc/legacy/qaqc/SF_CMP2021_Transit_APC_QAQC.ipynb has python code to do this extraction.

Coverage

Calculate transit coverage

1. Make the input toml file. These are saved in
2. Run e.g. `python transit/coverage/transit_coverage.py

Screenline volumes (Bay Bridge and San Mateo County Line)

From PeMS data, use PeMS/.....py

Speed: LOS and reliability

Floating car

0. Not 100% sure, but I think: Before the monitoring period, speed/inrix/sample_size_analysis.py is used to determine which CMP segments need to have data collected on floating car runs to supplement the INRIX data.
1. Run parse_raw_data.py.
2. Use compare_years.ipynb to compare floating car data across years.

INRIX (& merging floating car data in)

Counts

For intersection and midblock counts, see the READMEs in Counts/intersection and Counts/midblock.

House style

"AM"/"PM" peak (period), but e.g. "7 a.m.".