Surveys

This page lists all the survey types which are currently supported by MobiSurvStd.

For each type, the expected format (tree structure and filenames) is provided. If you have a survey with a different format (which can happen if you did not get your survey from Progedo), then MobiSurvStd will not be able to guess the survey type and to read it (even if you specify the survey type). In this case, you can try renaming or moving files to match the expected format. MobiSurvStd will then be able to properly guess the survey type and read it (assuming the files' content is the same).

If you are not able to read your survey or if you want MobiSurvStd to support a new survey type / format, feel free to open an issue on GitHub.

All surveys can be processed without spatial / zonal data when running with the --skip-spatial flag.

Enquête mobilité des personnes 2019 (EMP)

Code: emp2019

Link: https://www.statistiques.developpement-durable.gouv.fr/resultats-detailles-de-lenquete-mobilite-des-personnes-de-2019

Tested version: November 2024

Expected format:

emp_2019/
├── *k_deploc_public_V4.csv  # The `*` character can be anything (there is a typo in the original filename)
├── k_individu_public_V3.csv
├── q_2rmot_public_V3.csv
├── q_menage_public_V3.csv
├── q_voitvul_public_V3.csv
├── tcm_ind_kish_public_V3.csv
├── tcm_ind_public_V3.csv
└── tcm_men_public_V3.csv

Notes:

Many variables from the original survey data are not read because they are not part of any other survey type.
The survey does not guarantee any representativeness at the regional level.
Only one person per household is surveyed for their trips and additional characteristics.
The car type is not specified. Cars can include passenger cars, utilitary cars and recreational cars.
The survey includes data on 58 “voiturettes”, 59 “quads”, and 1 “tricycle” as a special vehicle category, which is not read by MobiSurvStd.
Walking legs are not specified in the survey data for trips combining walking with other modes.

Enquêtes mobilité certifiés Cerema (EMC²)

Code: emc2

Link: https://www.cerema.fr/fr/activites/mobilites/connaissance-modelisation-evaluation-mobilite/enquetes-mobilite-emc2

Download link (for researchers): https://data.progedo.fr/series/adisp/enquetes-menages-deplacements-emd-enquetes-mobilite-certifiee-cerema-emc

Expected format:

my_emc2_survey/
├── Csv
│   └── Fichiers_Standard
│       ├── *_std_depl.csv
│       ├── *_std_men.csv
│       ├── *_std_pers.csv
│       └── *_std_traj.csv
└── Doc
    └── SIG
        ├── *_ZF(_*)?.(TAB|shp)    # Optional "Zones fines" file
        ├── *_GT(_*)?.(TAB|shp)    # Optional "Générateurs de trafic" file
        └── *_DTIR(_*)?.(TAB|shp)  # Optional "Zones de tirage" file

Tested surveys:

Alençon 2018
Angers 2022
Besançon 2018
Bordeaux 2021
Bouzonville 2019
Brest 2018
Chambéry 2022
Évreux 2018
Gap 2018
Grenoble 2020
Lannion 2022
Le Havre 2018
Marseille 2020
Pointe-à-Pitre 2021
Poitiers 2018
Reims 2021
Rennes 2018
Sables d’Olonne 2021
Saint-Étienne 2021
Tours 2019
Valenciennes 2019
Vendée 2020

Notes:

The survey detailed zones correspond to the “zones fines” (“ZF”), which are usually the size of IRIS zones or smaller.
For most surveys, there are special locations corresponding to “Générateurs de trafic” (“GT”), which represent e.g., train stations or hospitals.
Draw zones correspond to the survey’s “zones de tirage”.
For each trips, the non-walking legs are recorded. MobiSurvStd reconstruct the walking legs using the access / egress walking time variables. This means that the leg travel time is known only for the walking legs (as it is not a recorded variable for the other legs).

Enquête Globale Transport 2018-2020

Code: egt2020

Link: https://data.progedo.fr/studies/doi/10.13144/lil-1581

Expected format:

egt_2020/
└── Csv
    ├── a_menage_egt1820.csv
    ├── b_individu_egt1820.csv
    ├── c_deplacement_egt1820.csv
    ├── d_trajet_egt1820.csv
    ├── e_voiture_egt1820.csv
    ├── f_drm_egt1820.csv
    └── g_velo_egt1820.csv

Notes:

There is no draw zones because the survey was interrupted before the end due to Covid-19.
There is no detailed zone but the coordinates of the origins / destinations are known (rounded to 100 meters).
The car types are not specified but utilitary cars and recreational cars ar excluded.
Motorcycles’ parking data is not read because the survey’s documentation is incorrect.
Variable main_mode_group can differ from the original MODP_H7 variable because MobiSurvStd uses a different definition of mode groups and a different methodology to identify the main mode.
The start_insee and end_insee values are derived from the leg’s coordinates, using INSEE data from 2025.
The work_insee and study_insee values are derived from the given coordinates, using INSEE data from 2025. There can be some errors because of the 100m rounding.

Enquêtes Déplacements Grands Territoires (EDGT)

Code: edgt

Link: https://www.cerema.fr/fr/activites/mobilites/connaissance-modelisation-evaluation-mobilite/enquetes-mobilite-emc2

Download link (for researchers): https://data.progedo.fr/studies?q=edgt

Expected format:

my_edgt_survey/
├── Csv
│   ├── Fichiers_Standard_Face_a_face
│   │   ├── *_std_faf_depl.csv
│   │   ├── *_std_faf_men.csv
│   │   ├── *_std_faf_pers.csv
│   │   └── *_std_faf_traj.csv
│   └── Fichiers_Standard_Telephone
│       ├── *_std_tel_depl.csv
│       ├── *_std_tel_men.csv
│       ├── *_std_tel_pers.csv
│       └── *_std_tel_traj.csv
└── Doc
    └── SIG
        ├── *(_ZF|zones_fines).(TAB|shp|MIF)        # Optional "Zones fines" file (the actual regex is more complex)
        ├── *(_GT|générateur*).(TAB|shp|MIF)        # Optional "Générateurs de trafic" file (the actual regex is more complex)
        └── *(_DTIR|_secteurstirage).(TAB|shp|MIF)  # Optional "Zones de tirage" file

Tested surveys:

Amiens 2010
Angers 2012
Annecy 2017
Annemasse 2016
Bayonne 2010
Clermont-Ferrand 2012
Dijon 2016
Dunkerque 2015
Lyon 2015
Marseille 2009
Metz 2017
Montpellier 2014
Nancy 2013
Nantes 2015 (Also available in an anonymized open-data format)
Nice 2009
Saint-Denis-de-la-Réunion 2016
Saint-Quentin-en-Yvelines 2010
Valence 2014

Notes:

The survey detailed zones correspond to the “zones fines” (“ZF”), which are usually the size of IRIS zones or smaller.
For most surveys, there are special locations corresponding to “Générateurs de trafic” (“GT”), which represent e.g., train stations or hospitals.
Draw zones correspond to the survey’s “zones de tirage”.
For each trips, the non-walking legs are recorded. MobiSurvStd reconstruct the walking legs using the access / egress walking time variables. This means that the leg travel time is known only for the walking legs (as it is not a recorded variable for the other legs).

Enquêtes Déplacements Villes Moyennes (EDVM)

Code: edvm

Link: https://www.cerema.fr/fr/activites/mobilites/connaissance-modelisation-evaluation-mobilite/enquetes-mobilite-emc2

Download link (for researchers): https://data.progedo.fr/studies?q=edvm

Expected format:

my_edvm_survey/
├── Csv
│   └── Fichiers_Standard
│       ├── *_std_depl.csv
│       ├── *_std_men.csv
│       ├── *_std_pers.csv
│       └── *_std_traj.csv
└── Doc
    └── SIG
        ├── *(_ZF|zones_fines).(TAB|shp|MIF)  # Optional "Zones fines" file (the actual regex is more complex)
        ├── *(_GT|générateur*).(TAB|shp|MIF)  # Optional "Générateurs de trafic" file (the actual regex is more complex)
        └── *_DTIR.(TAB|shp|MIF)              # Optional "Zones de tirage" file

Tested surveys:

Ajaccio 2017
Albi 2011
Angoulême 2012
Arras 2014
Beauvais 2010
Béziers 2014
Bourg-en-Bresse 2017
Carcassonne 2015
Châlon-sur-Saône 2014
Cherbourg 2016
Créil 2017
Dinan 2010
La Rochelle 2011
La-Roche-sur-Yon 2013
Laval 2011
Le Creusot 2012
Les Sables d’Olonne 2011
Longwy 2014
Niort 2016
Puisaye-Forterre et Aillantais 2012
Quimper 2013
Roanne 2012
Saint-Brieuc 2012
Saintes 2016
Saint-Louis 2011
Thionville 2012
Var 2012

Notes:

The survey detailed zones correspond to the “zones fines” (“ZF”), which are usually the size of IRIS zones or smaller.
For most surveys, there are special locations corresponding to “Générateurs de trafic” (“GT”), which represent e.g., train stations or hospitals.
Draw zones correspond to the survey’s “zones de tirage”.
For each trips, the non-walking legs are recorded. MobiSurvStd reconstruct the walking legs using the access / egress walking time variables. This means that the leg travel time is known only for the walking legs (as it is not a recorded variable for the other legs).

Enquêtes Ménages Déplacements (EMD)

Code: emd

Link: https://www.cerema.fr/fr/activites/mobilites/connaissance-modelisation-evaluation-mobilite/enquetes-mobilite-emc2

Download link (for researchers): https://data.progedo.fr/studies?q=emd

Expected format:

my_emd_survey/
├── Csv
│   └── Fichiers_Standard
│       ├── *_std_depl.csv
│       ├── *_std_men.csv
│       ├── *_std_pers.csv
│       └── *_std_traj.csv
└── Doc
    └── SIG
        ├── *(_ZF|zones_fines).(TAB|shp|MIF)  # Optional "Zones fines" file (the actual regex is more complex)
        ├── *(_GT|générateur*).(TAB|shp|MIF)  # Optional "Générateurs de trafic" file (the actual regex is more complex)
        └── *(_DTIR|secteur_*).(TAB|shp|MIF)  # Optional "Zones de tirage" file

Tested surveys:

Douai 2012
Fort-de-France 2014
Grenoble 2010
Lille 2016
Nîmes 2015
Rouen 2017
Saint-Étienne 2010
Strasbourg 2009
Toulouse 2013
Valenciennes 2011

Notes:

The survey detailed zones correspond to the “zones fines” (“ZF”), which are usually the size of IRIS zones or smaller.
For most surveys, there are special locations corresponding to “Générateurs de trafic” (“GT”), which represent e.g., train stations or hospitals.
Draw zones correspond to the survey’s “zones de tirage”.
For each trips, the non-walking legs are recorded. MobiSurvStd reconstruct the walking legs using the access / egress walking time variables. This means that the leg travel time is known only for the walking legs (as it is not a recorded variable for the other legs).

Enquête Globale Transport 2010

Code: egt2010

Link: https://data.progedo.fr/studies/doi/10.13144/lil-0883

Expected format:

egt_2010/
├── Csv  # Or Format_csv
│   ├── deplacements_semaine.csv  # Filenames are case-insensitive (the first letter can be capitalized).
│   ├── menages_semaine.csv
│   ├── personnes_semaine.csv
│   └── trajets_semaine.csv
├── Doc
│   └── Carreaux_shape_mifmid
│       └── carr100m.shp       # Option 1: Carreaux are in a shapefile within a directory.
└── Carreaux_shape_mifmid.zip  # Option 2: Carreaux are in a zipfile within the parent directory.

Notes:

The detailed zones correspond to the 100m by 100m cells.
For now, only the weekday trips are standardized.

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