full_config.yml

## Synthetic population pipeline for Île-de-France
## based on the synpp package

# This is the path to a directory where the pipeline can store temporary data
working_directory: cache

# This section defines which parts of the pipeline should be run
run:
  - synthesis.output # To create the output population in the output_path (see below)
  #- matsim.output # Uncomment, if you want to run the full simulation (you'll need Java for that)

# Here the configuraiton of the pipeline starts
config:

  ### Some general configuration
  
  ## Number of CPUs to use
  processes: 4

  ### Random seeds
  
  ## global random seed for the output population
  random_seed: 1234

  ## bpe specific random seed when impute missing coordinates for known IRIS
  # bpe_random_seed: 0
  
  ### Define sampling rate for the output population
  sampling_rate: 0.001

  ### household travel survey (HTS)

  ## Define whether to use ENTD or EGT as the HTS
  hts: entd # entd, egt, edgt_lyon, edgt_44

  ## Whether to filter people going outside of the area and other filters
  # filter_hts: true

  ## if selected, chose the source for edgt_lyon
  # edgt_lyon_source: unchosen # unchosen, adisp, cerema

  ### Zone selection

  ## select regions by region_id
  # regions: [11]
  
  ## select departments by department_id
  # departments: []
    
  ### Output paths
  
  ## output folder
  output_path: output

  ## output prefix, appended to file names
  # output_prefix: ile_de_france_

  ## file formats that should be exported
  # output_formats: ["csv", "gpkg"] # ["csv", "gpkg", "parquet", "geoparquet"]

  ### Algorithms configurations

  ## Use the bhepop2 package for attributing income
  # income_assignation_method: bhepop2 # uniform, bhepop2

  ## Activate if you want to run mode choice, will assign a mode to output trips
  mode_choice: true

  ## Statistical matching configuration
 
  ## Minimum number of observation to sample from
  # matching_minimum_observations: 20

  ## list of attributes to use for matching
  # matching_attributes: ["sex", "any_cars", "age_class", "socioprofessional_class", "departement_id"]
  
  ## Use INSEE's urban type in statistical matching
  # use_urban_type: true
  # urban_type_path: urban_type/UU2020_au_01-01-2023.zip
  # matching_attributes: ["urban_type", "*default*"]
  
  ## Exclude entreprise without any employee (trancheEffectifsEtablissement is NA, "NN" or "00")
  # exclude_no_employee: true

  ## source for the education locations
  # education_location_source: bpe # bpe, addresses

  ## max iterations for the secondary location selection algorithm
  # secloc_maximum_iterations: np.inf

  ## Buffer arround buildings to capture adresses in their vicinity
  # home_address_buffer: 5.0

  ## How sample homes, using weights or not
  # home_location_weight: housing # "uniform", "housing"
  
  # home_location_source: addresses # "addresses", "buildings", "tiles"

  ## When running matsim

  ## performing one run of the matsim simulation or not
  # run_matsim: true

  ## creating the far or not
  # write_jar: true

  ### Analysis configuration

  ## Whether to use previously generated files or not
  # analysis_from_file: false

  ## prefix of the files to compare to
  # comparison_file_prefix: other_

  ### Tools configuration

  ## Mostly interesting if you run the simulation, or you activate the `mode_choice` option,

  ## Binaries paths  
  # git_binary: git
  # osmosis_binary: osmosis
  # java_binary: java
  # maven_binary: mvn

  ## Binaries parameters
  # java_memory: 14G
  # maven_skip_tests: false
  
  ## eqasim-java parameters
  # eqasim_version: 1.5.0
  # eqasim_branch: develop
  # eqasim_commit: ece4932
  # eqasim_repository: https://github.com/eqasim-org/eqasim-java.git
  # eqasim_path: ""

  ## pt2matsim parameters
  # pt2matsim_version: 22.3
  # pt2matsim_branch: v22.3

  ## Strategy to use in pt2matsim gtfs processing
  # gtfs_date: dayWithMostServices
  ## Export the detailed geometry of the network before simplification in pt2matsim
  # export_detailed_network: true

  ### Input paths

  ## Absolute root path of all input data
  data_path: /path/to/my/data

  # census_path: rp_2019/RP2019_INDCVI_csv.zip
  # census_csv: FD_INDCVI_2019.csv

  # ban_path: ban_idf

  # bdtopo_path: bdtopo_idf

  # bpe_path: bpe_2021/bpe21_ensemble_xy_csv.zip
  # bpe_csv: bpe21_ensemble_xy.csv

  # gtfs_path: gtfs_idf

  # income_com_path: filosofi_2019/indic-struct-distrib-revenu-2019-COMMUNES.zip
  # income_com_xlsx: FILO2019_DISP_COM.xlsx
  # income_reg_path: filosofi_2019/indic-struct-distrib-revenu-2019-SUPRA.zip
  # income_reg_xlsx: FILO2019_DISP_REG.xlsx
  # income_year: 19
  
  # tiles_path: tiles_2019/Filosofi2019_carreaux_200m_gpkg.zip
  # tiles_file: carreaux_200m_met.gpkg

  # od_pro_path: rp_2019/RP2019_MOBPRO_csv.zip
  # od_sco_path: rp_2019/RP2019_MOBSCO_csv.zip
  # od_pro_csv: FD_MOBPRO_2019.csv
  # od_sco_csv: FD_MOBSCO_2019.csv
  
  # ## external education locations file
  # education_file: education/education_addresses.geojson

  # osm_path: osm_idf
  # osm_highways: "*"
  # osm_railways: "*"

  # siren_path: sirene/StockUniteLegale_utf8.zip
  # siret_path: sirene/StockEtablissement_utf8.zip
  # siret_geo_path: sirene/GeolocalisationEtablissement_Sirene_pour_etudes_statistiques_utf8.zip

  # iris_path: iris_2021

  # population_path: rp_2019/base-ic-evol-struct-pop-2019.zip
  # population_xlsx: base-ic-evol-struct-pop-2019.xlsx
  # population_year: 19

  # # population projections
  # projection_path: projection_2021
  # projection_scenario: 00_central
  # projection_year: 2030

  # vehicles_method: default # fleet_sample, default
  # vehicles_path: vehicles
  # vehicles_year: 2021