Open Data

This section collects links to all the data produced during the project liftime and deposited in open data repository. Apart from the link, a short summary is added to each of the data repositories.

Note that most of the data will be consecutively shared along with the publication of the results.

List of available datasets:

  1. Temporal resolution data: origin-destination matrices (census tracks to public service providers) of travel times by public transport in the city of Szczecin (Poland).

  2. Madrid accessibility: an example datatset of accessibility used for Policy support tool: accessibility to jobs in Madrid at the transport zones level (car and public transport, several scenarios) computed for Madrid case study.

Temporal resolution data

Link to the dataset: Temporal resolution dataset

DOI: 10.18150/repod.7727991

The dataset is compiled to share raw data used for the paper: Stepniak, M., Pritchard, J.P., Geurs K.T., Goliszek S., 2019, The impact of temporal resolution on public transport accessibility measurement: review and case study in Poland, Journal of Transport Geography, doi: accepted for publication on 11th January 2019 (submitted: 18th July 2018).


  • Marcin Stępniak (tGIS, Department of Geography, Complutense University of Madrid, Spain)
  • Sławomir Goliszek (Institute of Geography and Spatial Organization, Polish Academy of Sciences)
  • John P. Pritchard (Centre for Transport Studies, University of Twente)
  • Karst T. Geurs (Centre for Transport Studies, University of Twente)

Description of dataset

The case study area is the city of Szczecin (Poland). The dataset consists of origin-destination (OD) matrices calculated every 1-minute during the four 1-hour-long periods:

  • 1: 02:00 - 03:00
  • 2: 07:00 - 08:00
  • 3: 10:00 - 11:00
  • 4: 22:00 - 23:00

The OD are calculated using the schedule for 21st April 2015. Source of data: [access: 15.04.2015]

Travel times are calculated using the Network Analyst extension of ArcGIS. The network database is built using the Add GTFS to a Network Dataset tool.

Origins: census track centroids (1745 points); Source: [access: 10.11.2015]. These points are used also as destinations (code: OBWOD).

Destinations: geolocated providers of public services:

Dataset structure

The main dataset consists of 28 .csv files stored in two subfolders: f03_Aggregates (destinations: Adm, Teatr, SpecHC and Zlob) and f03_Aggregates_Ai (destinations: OBWOD, Edu_LO and HOS). One file contains OD travel time to one destination during one time window. File names: AAAn.csv where AAA is a code of destination and n is the code of time windom [1:4], e.g. HOS2.csv contains travel times from census track centroids to hospitals calculated every 1 minute between 07:00 and 08:00.

Column names:

  • Or - code of origin
  • Des - code of destination
  • 61 columns with travel times codes as TtHHMM where HH stands for an hour, and MM for minute of the evaluated departure time (e.g. Tt0205 for the departure time = 02:05).

The supplementary dataset is stored in t00_data subfolder and it consists of 3 .csv files which quantitatively describes attractiveness of selected destinations:

  • EduLO.csv: number of classess in secondary schools;
  • HOS.csv: number of beds in hospitals’ departments;
  • POP.csv: number of population in census tracks.


License for files: CC-BY-4.0

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Madrid accessibility

Temporal link to dataset: Link to the dataset: Madrid accessibility (the data will be deposited in open data repository after the publication of the results)


  • Marcin Stępniak (tGIS, Department of Geography, Complutense University of Madrid, Spain)
  • Borja Moya-Gómez (tGIS, Department of Geography, Complutense University of Madrid, Spain)
  • Amparo Moyano (Department of civil engineering, Universidad de Castilla-La Mancha, Spain)

Description of dataset

The dataset consists of accessibility values for all transport zones in Madrid in 2018.

Dataset structure

The dataset structure is presented in the table below.

Column name Description Comment
Or ID of transport zone
Car accessibility
FreeFlow Free flow speed Benchmark scenario
Car_Best Car best-case scenario Congestion
Car_Avg Average car Congestion
Car_Worst Car worst-case scenario Congestion
Public transport accessibility
FullFreq PT - no waiting time PT route network
PT_Best PT best-case scenario Frequency of PT
PT_Avg Average PT Frequency of PT
PT_Worst PT worst-case scenario Frequency of PT
PT_VarCoeff Coefficient of variation of PT accessibility variability of PT accessibility

The dataset was used to prepare an example to illustrate the implementation of the policy support tool.


License for dataset: CC-BY-4.0

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Marcin Stępniak
Marie Curie postdoc researcher

I am transport and urban geographer interested in accessibility analysis, GTFS data and R scripts.