[{"authors":["admin"],"categories":null,"content":"I am a transport and urban geographer. Currently I am a Marie Curie Postdoc Researcher working at the tGIS research group of the Complutense University of Madrid and researcher at the Institute of Geography and Spatial Organization, Polish Academy of Sciences. My research mostly focuses on accessibility in the urban realm extensively using GIS and R-scripting. Recently, I have been spending more and more time using GTFS feeds to visualize and analyse public transport networks.\nOn this webpage you can find a selection of my publications and research projects.\n","date":-62135596800,"expirydate":-62135596800,"kind":"taxonomy","lang":"en","lastmod":-62135596800,"objectID":"2525497d367e79493fd32b198b28f040","permalink":"https://marcinstepniak.eu/authors/admin/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/admin/","section":"authors","summary":"I am a transport and urban geographer. Currently I am a Marie Curie Postdoc Researcher working at the tGIS research group of the Complutense University of Madrid and researcher at the Institute of Geography and Spatial Organization, Polish Academy of Sciences. My research mostly focuses on accessibility in the urban realm extensively using GIS and R-scripting. Recently, I have been spending more and more time using GTFS feeds to visualize and analyse public transport networks.","tags":null,"title":"Marcin Stępniak","type":"authors"},{"authors":null,"categories":null,"content":" Causes And Consequences of Low Urban Accessibility.\nDefining proper policy responses \nAn efficient and just transport system is a key element of sustainable development and an indispensable component of coherent modern societies. Improving accessibility and reducing its disparities are the means that bridge the reduction of negative transportation outcomes with a positive response to the increased mobility needs of contemporary societies. This places accessibility at the core of the main challenges of EU’s urban areas. A proper identification of causes of low accessibility combined with a profound knowledge of the outcomes of a particular pattern facilitate the formulation of the most efficient policy response that leads towards social inclusion, an increase of well-being, and a fully-fledged citizenship of contemporary societies.\nThe CALCULUS project aims to deepen understanding of the causes and consequences of unfavourable accessibility patterns in order to develop a decision-support model which facilitates to overcome existing barriers in urban accessibility. Its main idea is to take an advantage of temporal sensitive transport network data in order to identify main restrictions of accessibility level and to quantify and compare an impact of particular restrictions, including congestion, intermodal imbalance (i.e. difference between public transport and private car accessibility), a spatial pattern of route network of public transport, its frequency and temporal variability. In order to achieve this, the in-depth analysis of the impact of temporal resolution on precision of travel time and accessibility measurement in public transport analysis.\nAdditionally, the CALCULUS project focus on the potential of so-called GTFS feeds (General Transit Feed Specification data – a schedule-based public transport data) in order to strengthen analysis of the supply side of public transport network in a given area, and to facilitate inter-urban comparisons (link).\n\nThe CALCULUS project is founded under the European Commission’s Horizon 2020 Marie Skłodowska-Curie Actions Individual Fellowship (H2020 MSCA IF). The project is implemented under the supervision of prof. Javier Gutiérrez Puebla at the tGIS Research Group (Transport, Infrastructure and Territory) at the Department of Geography of University Complutense of Madrid.\nThis project has received funding from the European Union\u0026rsquo;s Horizon 2020 research and innovation Programme under the Marie Sklodowska-Curie Grant Agreement no. 749761.\nThe views and opinions expressed herein do not necessarily reflect those of the European Commission.\n","date":1536451200,"expirydate":-62135596800,"kind":"section","lang":"en","lastmod":1536451200,"objectID":"23a674c823b17df3d272b6f8ea26ee59","permalink":"https://marcinstepniak.eu/projects/calculus/","publishdate":"2018-09-09T00:00:00Z","relpermalink":"/projects/calculus/","section":"projects","summary":"Learn how to use Academic's docs layout for publishing online courses, software documentation, and tutorials.","tags":null,"title":"CALCULUS project overview","type":"docs"},{"authors":null,"categories":null,"content":" 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.\nNote that most of the data will be consecutively shared along with the publication of the results.\nList of available datasets:\n Temporal resolution data: origin-destination matrices (census tracks to public service providers) of travel times by public transport in the city of Szczecin (Poland).\n 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.\n  Temporal resolution data Link to the dataset: Temporal resolution dataset\nDOI: 10.18150/repod.7727991\nThe 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: https://doi.org/10.1016/j.jtrangeo.2019.01.007. accepted for publication on 11th January 2019 (submitted: 18th July 2018).\nAuthors:\n 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:\n 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: http://www.zditm.szczecin.pl/rozklady/GTFS/ [access: 15.04.2015]\nTravel times are calculated using the Network Analyst extension of ArcGIS. The network database is built using the Add GTFS to a Network Dataset tool.\nOrigins: census track centroids (1745 points); Source: https://geo.stat.gov.pl/ [access: 10.11.2015]. These points are used also as destinations (code: OBWOD).\nDestinations: geolocated providers of public services:\n Adm: City council; source: http://www.szczecin.pl/chapter_59000.asp [access: 10.11.2015] Zlob: Nurseries; source: http://empatia.mpips.gov.pl/web/piu/dla-swiadczeniobiorcow/rodzina/d3/rejestr-zlobkow-i-klubow# [access: 10.11.2015] Teatr: Theatres; Source: http://www.e-teatr.pl/pl/instytucje/lista.html [access: 05.09.2015] SpecHC: Specialized health care; Source: http://www.e-teatr.pl/pl/instytucje/lista.html [access: 05.09.2015] HOS: Hospitals; Source: http://nfz.gov.pl/ [access: 10.11.2015] Edu_LO: Secondary schools; Source: https://sio.men.gov.pl/ [access 10.11.2015]  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.\nColumn names:\n 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:\n EduLO.csv: number of classess in secondary schools; HOS.csv: number of beds in hospitals\u0026rsquo; departments; POP.csv: number of population in census tracks.  Licence License for files: CC-BY-4.0\nBack to top\n\nMadrid 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)\nAuthors:\n 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.\nDataset structure The dataset structure is presented in the table below.\n   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.\nLicence License for dataset: CC-BY-4.0\nBack to top\n\n","date":1557010800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1557010800,"objectID":"92fdb590bbe269228e2f24c166862c28","permalink":"https://marcinstepniak.eu/projects/calculus/repository/open_data/","publishdate":"2019-05-05T00:00:00+01:00","relpermalink":"/projects/calculus/repository/open_data/","section":"projects","summary":"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.\nNote that most of the data will be consecutively shared along with the publication of the results.\nList of available datasets:\n Temporal resolution data: origin-destination matrices (census tracks to public service providers) of travel times by public transport in the city of Szczecin (Poland).","tags":null,"title":"Open Data","type":"docs"},{"authors":null,"categories":null,"content":" The study of an impact of temporal resolution on precision of travel time and accessibility measurement in public transport analysis.\nPublication Stępniak, M., Pritchard, J. P., Geurs, K. T., \u0026amp; Goliszek, S. (2019). The impact of temporal resolution on public transport accessibility measurement: Review and case study in Poland. Journal of Transport Geography, 75, 8–24. https://doi.org/10.1016/j.jtrangeo.2019.01.007 (Open access)\nAbstract In recent years there has been a significant increase of temporally variable analyses of accessibility by public transport as a result of the increased availability of open and standardized time table information in the form of GTFS (General Transit Feed Specification) data. To date, very little attention has been paid to systematically analyze the impact of temporal resolutions on the results. Different authors have applied different standards, often in an ad-hoc manner. In this study, we address the loss of precision associated with a stepwise reduction of the temporal resolution of travel time estimations based on GTFS data for the city of Szczecin in Poland. The paper aims to provide guidance to researchers and practitioners on the selection of appropriate temporal resolutions in accessibility studies. We test four sampling methods in order to analyze four different public transport frequency scenarios, three types of accessibility measures (travel time to the nearest provider, cumulative opportunities measure and potential accessibility) and seven types of destinations ranging from high to low centrality. Additionally, the impact on spatial disparities is explored using the Gini coefficient.\nWe find that a reduction of temporal resolution is associated with a decrease in precision of public transport accessibility measurement. However, with up to 5-min resolutions this reduction is negligible, while computational time is reduced fivefold, compared to a 1-min resolution benchmark. Lower temporal resolutions still provide relatively precise estimations of travel times and accessibility measures. However, further resolution reductions are associated with decreasing reductions of computational time. As a result, we argue that 15-min temporal resolution provides a good balance between precision and computational time while providing very precise estimations of Gini coefficients (errors ≤0.001).\nA non-linear relationship is found between the public transport frequency and the loss of precision, with lower frequencies leading to a greater loss in precision. More attention should be paid to highly centralized services, in particular when analyzed using proximity and cumulative opportunities measures. Finally, the cumulative opportunities measure is found to be highly sensitive to changes in the temporal resolution and not suited for time-sensitive accessibility analysis.\nRepository All the data used for the study, including its detailed description, can be downloaded from the open data repository ( link). The data is shared under the CC-BY-4.0 licence.\nReference: Stepniak, M.; Goliszek, S.; Pritchard, J.; Geurs, K. (2019) The impact of temporal resolution on public transport accessibility measurement. RepOD. http://dx.doi.org/10.18150/repod.7727991\nAll the R code used for the analysis is available from the github repository: https://github.com/stmarcin/Temporal-paper.\nAdditionally, a separate code which can be used to generate departure times for a given day (e.g. to conduct accessibility analysis or calculate origin-destination matrices), using user-defined sampling method and temporal resolution is stored in separated github repo: https://github.com/stmarcin/Sampling_Departure_Time.\n\n","date":1557010800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1557010800,"objectID":"b841f26632092e9179479ca90f46ee69","permalink":"https://marcinstepniak.eu/projects/calculus/main_results/temporal_resolution/","publishdate":"2019-05-05T00:00:00+01:00","relpermalink":"/projects/calculus/main_results/temporal_resolution/","section":"projects","summary":"The study of an impact of temporal resolution on precision of travel time and accessibility measurement in public transport analysis.\nPublication Stępniak, M., Pritchard, J. P., Geurs, K. T., \u0026amp; Goliszek, S. (2019). The impact of temporal resolution on public transport accessibility measurement: Review and case study in Poland. Journal of Transport Geography, 75, 8–24. https://doi.org/10.1016/j.jtrangeo.2019.01.007 (Open access)\nAbstract In recent years there has been a significant increase of temporally variable analyses of accessibility by public transport as a result of the increased availability of open and standardized time table information in the form of GTFS (General Transit Feed Specification) data.","tags":["GTFS","accessibility"],"title":"Temporal resolution","type":"docs"},{"authors":null,"categories":null,"content":"\nThis section contains main project results. They are divided into three main parts:\n Temporal resolution: The study aims to to provide guidance to researchers and practitioners on the selection of appropriate temporal resolutions in accessibility studies.\n GTFS study: This research focus on an evaluation of a supply side of public transport using GTFS and other data available in open access.\n Policy support tool: This section contains a brief summary of methodological guidelines for an evaluation of an impact of particular transport related restrictions of accessibility in urban area. The method extensively uses a new, time-sensitive network data (speed profiles and GTFS feeds) and it is based on comparison of accessibility levels and its spatial patterns in order to identify a particular, transport-related accessibility restrictions.\n  ","date":1536451200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1536451200,"objectID":"2ff372ef04a1354ae19b2705240b9e13","permalink":"https://marcinstepniak.eu/projects/calculus/main_results/","publishdate":"2018-09-09T00:00:00Z","relpermalink":"/projects/calculus/main_results/","section":"projects","summary":"This section contains a brief summary of the main project results with links to more detailed information","tags":["CALCULUS"],"title":"CALCULUS project main results","type":"docs"},{"authors":null,"categories":null,"content":"\nCALCULUS project repository facilitates to get all project deliverables. Note that part of the content will be succesfully made available at the time of publication of a given part of the results. Thus, this part of the web will be successively updated.\nThe repository is organized in 4 sub-sections:\n Open data: links to the data produced during the project lifetime. The data are stored in the external, open data repository (e.g. RepOD).\n Open code: links to code (r-scripts) prepared for project. All the code is stored at my github.\n Interactive map: An interactive map which shows accessibility values in the city of Madrid.\n Publications: links to published papers.\n Slides and talks: links to slides for my presentations related to the project.\n  \n","date":1536451200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1536451200,"objectID":"cf1d8c1c39927cc4834f3bf6d9e217fc","permalink":"https://marcinstepniak.eu/projects/calculus/repository/","publishdate":"2018-09-09T00:00:00Z","relpermalink":"/projects/calculus/repository/","section":"projects","summary":"This section contains all links to project outputs, including data, code, publications etc.","tags":null,"title":"CALCULUS project repository","type":"docs"},{"authors":null,"categories":null,"content":" Evaluation of a supply side of public transport using GTFS data.\nIntroduction The aim of the study is to evaluate a supply side of public transport. The assumption that lies behind this study is that it should minimize the amount of data required and to guarantee its fully replicability. The result is a set of indicators which enable to evaluate public transport network in a given area and easily compare the results between different cities, functional urban areas (FUA) or metropolises, including international comparisons.\nThe presented example uses an example of Madrid.\nData Three different types of data are used in the study:\n GTFS (General Transit Feed Specification) which composes of a series of text files with data which contain all information about public transport schedules and associated geographic information.\nData source for an example: Open data from the Consorcio Regional de Transportes de Madrid.    GTFS feed (part)    Route network derived from OpenStreetMaps Data on population distribution: population grids from Eurostat GEOSTAT initiative  Back to top\nEvaluation The evaluation procedure is divided into three main parts, preceded by an overview of available (or colleced) GTFS data:\n stops location and their accessibility frequency of service accessibility  Preview Before the evaluation, there is a need to revise a basic spatial and temporal information about all available feeds for a given case study. A map shows a spatial range of all available GTFS feeds comparing to the city and FUA limits.\nThe table provides information about the names and temporal coverages of all feeds in order to confirm that they cover the same period of time. It is also used to select a \u0026ldquo;typical day\u0026rdquo; for further analyses (different feeds contain a calendar data coded in a different way, thus a selection of one day is necessary in order to avoid a double-counting of particular trips and/or departures). In this example, based on the data shown in the table, we focus on a \u0026ldquo;typical working day\u0026rdquo;: 2018/09/11.\nBack to top\nStops location First part of the evaluation focus on a description of public transport network, its spatial pattern and accessibility. The table summarize basic indicators of the network using all available GTFS feeds, providing insights about existing transport modes, number of stops and total number of the departures during the selected day.\nNote: due to different systems of coding of calendar, departure times and frequencies. The table provides unified information, regardless:\n existence of frequencies.txt file - if this file exists, not every departure time is listed in stop_times.txt, what needs to be recalculated; this is usually the case of high frequency transport modes, like metro;\n overlapping calendar data - some datasets code departure time as 25:30:00 (HH:MM:SS, i.e. 01:30:00 + 1 day) which refers to the departure realized the next day in relation to the analysed. If this is the case, there is a need to include part of the departures which are assigned to the previous day than selected for the analysis.\n  Finally, apart from the division by transport modes, the table summarize differences between the city within its limits, FUA and the whole GTFS dataset(s).\nThen, maps visualize distribution of public transport stops in order to analyse their spatial pattern and, e.g. compare a supply of public transport during the day (peak hours) and night (low frequency, limited service).\n   city FUA     stops in service during peak hours        stops in service during a night         The last part focuses on accessibility to public transport: what is a walking distance to the nearest stop in service? What is a difference between peak hours and night time? What is a difference between the city and its FUA? The next set of maps and graph address these questions.\n   city FUA     walking distance to stops in service during peak hours        walking distance to stops during a night         Back to top\nFrequency The frequency of public transport differs in a course of a day, in line with peaks and valleys of density of human mobility: more departures take place during peak hours, less in out-of-peak period and much less during the night. Each city or each country has its own curve of daily changes of frequency. Nevertheless, it is important to properly identify periods different frequency, especially, when doing international comparisons. These differences are visualized by the graph presented below. Note, that y-axis shows a total number of departures and not frequency - the latter would be difficult to visualize as there are changes in e.g. number of lines. Moreover, some differences in frequency patterns may occur between a city and its FUA, so both patterns are presented simultaneously. Additionally, it enables to check what share of departures in the all FUA are realized within city limits and how it changes during a day.\nBack to top\nAccessibility by public transport Regardless the number of stops, lines, departures etc., the most important function of public transport is to enable people to reach their destinations. Thus, the most important indicator which evaluates public transport network is a level of accessibility which offers this network. In this example, a potential accessibility is used, with the number of population as a proxy of destination\u0026rsquo;s attractiveness. This indicator includes relations between all pairs of origin–destination nodes in a given area and it assumes the greater importance of larger centres than smaller ones and the diminishing attractiveness of more distantly located destinations and it is expressed by the following formula.\n$$A_ {i} = \\sum_{j}g(M_j)*f{(t_{ij} )} $$\nwhere $A_i$ is a potential accessibility of a zone $i$, $g(M_j)$ is the function of destination attractiveness of a zone $j$ (e.g. number of population1), and $f{(t_{ij} )}$ is a distance decay function. In a given example, a negative exponential is used as distance decay function, with $\\beta = 0.0223$ (i.e. a destination loses half-value of its attractiveness at 31 minutes travel time).\n1 we use number of population due to the fact that population data are the most broadly available in high resolution datasets.\nThe results are presented as a set of maps which compare level of accessibility during the day (peak hours) and night and they are presented in a high resolution level of 1km2 grids. Map for FUA are accompanied by zoom-in maps limited to the city area.\n   city FUA     accessibility by public transport during peak hours        accessibility by public transport during a night         The last graph compares a share of population which has a particular level of accessibility (as a relation to the highest possible value measured in during a day).\nBack to top\nContributors This study was prepared in collaboration with:\n Chris Jacobs-Crisioni (European Commission, Joint Research Centre) David Sousa Vale (University of Lisbon)  Back to top\n\n","date":1557010800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1557010800,"objectID":"936a42c65dcf765f85a7c8f1ef3761ed","permalink":"https://marcinstepniak.eu/projects/calculus/main_results/gtfs_study/","publishdate":"2019-05-05T00:00:00+01:00","relpermalink":"/projects/calculus/main_results/gtfs_study/","section":"projects","summary":"Evaluation of a supply side of public transport using GTFS data.\nIntroduction The aim of the study is to evaluate a supply side of public transport. The assumption that lies behind this study is that it should minimize the amount of data required and to guarantee its fully replicability. The result is a set of indicators which enable to evaluate public transport network in a given area and easily compare the results between different cities, functional urban areas (FUA) or metropolises, including international comparisons.","tags":["GTFS","accessibility"],"title":"GTFS study","type":"docs"},{"authors":null,"categories":null,"content":" The section provides list of links to all #rstats code repositories developed during the project lifetime.\nNote that repositories will be consecutively shared along with the publication of the results.\nList of currently available code repositories:\n Sampling departure times: code to generate .dbf file with a sample of departure times applying selected sampling method and temporal resolution.\n Temporal resolution paper: code used in paper Stępniak, 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 75, 8–24, (link).\n GTFS Full Frequency: replaces original frequency of public transport service by a generic one, which assumes ´full frequency´ (no waiting times at public transport stops)\n GTFS moveVis: code used to prepare visualization of public transport flows.\n  \nSampling departure times Link to the repo: Sampling departure times\nThe code provides a function DepartureTime() which generates departure times using one of four sampling methods (i.e. Systematic, Simple Random, Hybrid, Constrained Random Walk) for a selected day and time window, applying a user-defined temporal resolution.\nThe code was inspired by Owen \u0026amp; Murphy (2018) paper and was prepared for the study on impact of temporal resolution on on public transport accessibility measurement (Stępniak et al., 2019).\nThe output of the function is a .dbf file which contains generated departure times (to be used e.g. in ArcGIS Network to calculate origin-destination matrices with time-dependent transport data, e.g. GTFS). The structure of the output file:\n   ID Date     rowID (integer), starts with 0 Departure date \u0026amp; hour   Example    0 05/14/2017 00:00   1 05/14/2017 00:09   2 05/14/2017 00:10    Details about the function syntax an example of its application can be found in repo description.\nBack to top\nTemporal resolution paper Link to the repo: Temporal paper\nThe code repository contains all r code developed for the paper Stępniak, 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 75, 8–24, (link).\nDetailed description of all scripts, workflow and description of inputs and outputs of particular scripts can be found in repo´s README file. All the data used for the study can be downloaded from Open Data Repository RepOD. Direct link and reference of the dataset:\nStepniak, M., Goliszek, S., Pritchard, J., Geurs, K., 2019. The Impact of Temporal Resolution on Public Transport Accessibility Measurement. [Dataset] RepOD. https://doi.org/10.18150/repod.7727991\nBack to top\nGTFS Full Frequency Link to the repo: GTFS Full Frequency\nThis repository contains a function which replaces original frequency of public transport service (as coded in original GTFS feed) by a generic one (user-defined maximum waiting times at public transport stops), keeping the original travel times between stops. It is prepared in order to investigate impact of public transport´s route network on accessibility level (regardless applied resources, i.e. real frequency).\nThis code is used for the study policy support tool\nBack to top\nGTFS moveVis Link to the repo: GTFS moveVis\nThe code was prepared for the Researchers Night 2018 in Madrid and it was used to illustrate public transport flows in Madrid (metro, trams and suburban trains) during the morning peak hours. Data source: Consorcio de Transportes de Madrid).\nThe repo consists of two parts: first coverts data derived from a GTFS feed in order to meet the requirements of the moveVis package. The second part creates a video showing a movement of public transport vehicles using `moveVis´ package.\nNote that since new version of moveVis package is available, with completely rewritten code and introduced a new logic and new functions, the code is likely depriciated. For the details consult the moveVis official webpage\nThe output is presented on the video attached below. \nBack to top\n","date":1557010800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1557010800,"objectID":"70d75e397c54398154edba3dc88f384f","permalink":"https://marcinstepniak.eu/projects/calculus/repository/open_code/","publishdate":"2019-05-05T00:00:00+01:00","relpermalink":"/projects/calculus/repository/open_code/","section":"projects","summary":"The section provides list of links to all #rstats code repositories developed during the project lifetime.\nNote that repositories will be consecutively shared along with the publication of the results.\nList of currently available code repositories:\n Sampling departure times: code to generate .dbf file with a sample of departure times applying selected sampling method and temporal resolution.\n Temporal resolution paper: code used in paper Stępniak, M., Pritchard, J.","tags":null,"title":"Open code","type":"docs"},{"authors":null,"categories":null,"content":" Policy support tool for efficient transport policy: using temporal sensitive transport data to identify main accessibility restrictions.\nIntroduction The main aim of the study is to prepare a methodological guidelines and describe data needs related to the evaluation of an impact of particular transport related restrictions of accessibility at the level of the whole city or metropolitan area (global restrictions) and spatial pattern of their distribution (local restrictions). The results of the analyses enable to identify main causes of the limited accessibility in a given area which in turn can be used as a base of the strategic decisions leading towards more efficient transport system.\nThe study extensively uses a potential of new, temporally sensitive data sources and methods which incorporate a temporal variability into the accessibility analysis. New data facilitates to compare accessibility by different transport modes (e.g. public transport and private car), as well as to identify different factors affecting spatial patterns of accessibility, including geography, quality of transport network and congestion levels and organization of public transport, including its routing, frequencies and timing.\nThe bottom line of the study is to apply a comparative approach in order to detect main reasons that limit accessibility level in particular areas. We compute travel times by private car using free flow and congested speeds, and we calculate travel times by public transport with a fine temporal resolution, applying a schedule-based, public transport data (GTFS format). Additionally, we prepare a pseudo-GTFS dataset, transforming the original GTFS feeds in a way that we exclude any waiting time from the model, i.e. travel time includes only in-vehicle time (or times in case of transfers) and walking to, from (i.e. from origin to stop and from the final stop to the destination) and between public transport stops (in case of transfers).\nThe presented document uses Madrid as a case study and focuses on accessibility to jobs during the morning peak hours (i.e. 7-10am). However, depending on particular needs and availability of data, jobs might be replaced by any other type of destination.\nBack to top\nData The main characteristic of the tool are small data requirements. In fact it requires three types of data:\n road network data which permits to calculate travel time with free flow and congested speeds; GTFS feed in order to calculate travel time by public transport (for the details consult GTFS study); data for the origin-destinations zones  Ad. 1. In the presented example of the application of the tool the TomTom® Speed Profiles are used in order to prepare origin-destination matrices with free flow and congested speeds. However, Speed Profiles can be replaced by another (similar) data (e.g. OpenStreetMaps, HERE Traffic, TravelTime Platform or Inrix Traffic, among others).\nSpeed Profiles is a network dataset, where a particular edge of the network (i.e. road segment) contains a very precise information about an average travel speed, depending on: day of the week and hour with 5-minute temporal resolution. The graph below presents a relative change of speed during the course of the day (typical working day) with 100 equal to a free flow speed of a particular road segment. The graph presents a shape of curves of almost 300 different \u0026ldquo;speed profiles\u0026rdquo;.\n  Figure: Speed Profiles   Ad. 2. GTFS feeds used for this example are provided by Consorcio Regional de Transportes de Madrid.\n  Figure: Structure of GTFS feed (sample)   Apart from real GTFS feed a pseudo-GTFS is required in order to evaluate an impact of route network (routing scheme) on accessibility level. The pseudo-GTFS is a public transport network dataset with no waiting times included (i.e. door-to-door travel time is equal to in-vehicle and walking to/from/between stops).\nAd. 3. This example focuses on accessibility to jobs and it uses number of jobs as a proxy of destination attractiveness. The data are provided by Instituto de Estadistica de Comunidad de Madrid. Additionally the population number per zone is used in order to: (1) remove areas of low population density from the analysis, and (2) to calculate weighted averages of accessibility level at the city level.\nBack to top\nData preparation and preliminary analysis Preparation of Origin-Destination matrices The input data for the the analysis are several origin-destination matrices (OD matrices) for a particular transport mode. As travel time by public transport highly depends on the departure time (as it affects waiting times, transfer times etc.) in case of public transport there are several OD matrices, one for each of the departure times. Based on the previous study we use a hybrid sampling method and 5-minute temporal resolution, in order to reduce an impact of temporal resolution on travel time measurement. In result, we use 36 OD matrices which are then aggregated in order to obtain:\n average travel time (using a hybrid-based average, for the details see: Stępniak and Jacobs-Crisoni, 2017)\n minimum travel time in order to prepare a \u0026ldquo;best-case scenario\u0026rdquo; (the highest possible accessibility level of a given area during the morning peak hours).\n maximum travel time in order to prepare a \u0026ldquo;worst-case scenario\u0026rdquo;\nThe last two are used to o evaluate an impact of public transport schedule on accessibility level, i.e. to what extent the fact that one can (or cannot) freely select their departure time affects the level of accessibility they experience.  In case of private car accessibility, two matrices are required: one for flee flow speeds and one for congested speeds. In case of availability of more temporally detailed data (i.e. several OD matrices for different departure times, e.g. every 15 minutes), the minimum and maximum travel time may be prepared, as it is done in case of public transport. Nevertheless, the variability of travel time by car is more regular and smooth, thus this information is only a supplementary one.\nIn result the number of OD matrices is limited to eight: four for public transport and four for private car travel times.\nAccessibility calculation In the second step, the accessibility values for each of the scenarios are calculated. In this case a potential accessibility measure is used:\n`$$A_ {i} = \\sum_{j}g(M_j)*f{(t_{ij} )} $$\nwhere $ A_i $ is a potential accessibility of a zone $ i $, $ g(M_j) $ is the function of destination attractiveness of a zone $ j $ (e.g. number of job), and $ f{(t_{ij} )} $ is a distance decay function. In a given example, a negative exponential is used as distance decay function, with $ \\beta = 0.0223 $ (i.e. a destination loses half-value of its attractiveness at 31 minutes travel time).\nAdditionally, the coefficient of variation is calculated as it facilitates to evaluate a variability of travel time on accessibility level (only for public transport accessibility).\n   Scenario Abbreviation Description Comment     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 which contains all the data above can be found in the project open data repository.\nSpatial pattern of accessibility Once accessibility values are calculated, we can focus on preliminary analysis of spatial patterns of accessibility in particular scenarios. Four of them are particularly important from the policy perspective: accessibility by car with free flow speeds, accessibility by car with congested speeds, accessibility by public transport in no-waiting-times scenario and average accessibility by public transport. In order to facilitate comparison of maps, all of them follow the same color palette and breaks.\n   accessibility by car accessibility by public transport              The preliminary analysis of spatial patterns of accessibility enable to identify main regularities in spatial distribution of accessibility level. First, it seems that all of them follow the same central-periphery division. However, some irregularities as well as differences between scenarios can be found. In case of congestion, there is a difference between south and north (and south-west in particular), while in case of public transport we can find a star-shape pattern, along the rail links. Moreover, one can find a strong difference in accessibility level between car and public transport scenarios. The next section focuses on these general differences.\nBack to top\nAccessibility restrictions at city scale The aim of this section is to facilitate a general comparison accessibility values in different scenarios and detect the main factors which limits accessibility at the city (metropolitan) level. The box-plot shows a general distribution of accessibility level, while table focuses on relative differences, but it additionally uses population weighted averages in order to reflect the extend to which observed differences affect inhabitants.\n  Speed Profiles   First, we can see that accessibility in all car scenarios are higher than in public transport ones. The exception is made only for not realistic scenario with no-waiting times, which is slightly higher than the most congested scenario. Second, the impact of congestion is less significant (less than 10%) than impact of change of transport mode (~25%). Finally, selection of a particular departure time is more important in case of travel by public transport than in case of car trips (difference between best and worst case scenarios: 90.7% vs 78.1%). This shows that those who travel by public transport have to larger extent adapt their daily schedule to public transport schedule than car traveler to the traffic conditions (congestion).\nTable 1: Population weighted relative differences between all accessibility scenarios    Car accessibility Public transport accessibility   scenario  Free flow  Best  Average  Worst  no waiting times  Best  Average      Car Best case  95.4          Car Average  90.3  94.6         Car Worst case  86.5  90.7  95.8        PT no waiting times  87.1  91.3  96.6  100.9       PT Best case  76.5  80.2  84.8  88.5  87.7      PT Average  68.0  71.2  75.3  78.6  77.8  88.7     PT Worst case  59.9  62.7  66.3  69.2  68.5  78.1  87.9     Back to top\nAccessibility restrictions at local scale This section focuses on spatial pattern of impact of particular accessibility restrictions. The title of a particular map indicates which of the two scenarios are compared against each other. The in-depth analysis enable to evaluate the extent to which a particular transport-related accessibility restriction affects inhabitants of a given zone.\n   Spatial pattern of accessibility restrictions               Back to top\nBivariate maps The next set of maps focuses on an evaluation of impacts of pairs of factors. As the maps presented above show a spatial pattern of impact of a particular restriction, bivariate maps puts these results in the broader context. Apart of the evaluation of a given factor, they inform what these restrictions means for inhabitants, e.g. congestion affects inhabitants in the areas with low level of accessibility by public transport to the larger extent, as people have limited alternatives other than travel in traffic jams). Using this kind of illustration facilitates to identify areas of intervention which should be addressed in the first order.\nFirst map confronts impact of congestion and the level of accessibility by public transport. If high level of congestion affects areas with relatively efficient public transport, it may stimulate inhabitants to use public transport instead of private car, as it becomes to be more competitive. On the contrary, high level of congestion and low accessibility by public transport means that inhabitants are stuck in traffic jams because they don´t have any reasonable alternative. In this case policy makers (or transport planners) should focus on this area first.\nSecond map facilitates to identify how public transport can be improved: to what extent accessibility in a given area is limited due to low (insufficient) frequency or in order to improve accessibility, there is a need to reorganize a route network of public transport.\nThe third map analyse to what extent inhabitants of particular zones are double affected by limited accessibility by public transport, i.e. not only by low average level of accessibility by public transport, but also by the high variation in accessibility level (so to larger extent their daily schedule need to focus a public transport schedule).\nBack to top\nConcluding remarks The presented decision support tool facilitates an identification of the most severe restrictions of accessibility level, so transport planners and policy makers will know what they should focus on in order to improve quality of life of inhabitants of a city. It provides a general information about the most important accessibility restriction at the city level and provides a set of very detailed information about the spatial pattern of impacts of particular restrictions.\nIn the future, a technical note will be shared through a github repository and relevant information will be shared through project repository. At the moment, an example dataset of accessibility values used for the presented study is available throught the open data repository.\nBack to top\nContributors This study was prepared in collaboration with:\n Borja Moya-Gómez (tGIS Research Group, Complutense University of Madrid) Juan Carlos Garcia Palomares (tGIS Research Group, Complutense University of Madrid) Amparo Moyano (Department of Civil Engineering, Universidad de Castilla-La Mancha)  Back to top\n\n","date":1557010800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1557010800,"objectID":"d42d3cab16325d89b283adc901860d06","permalink":"https://marcinstepniak.eu/projects/calculus/main_results/policy_support_tool/","publishdate":"2019-05-05T00:00:00+01:00","relpermalink":"/projects/calculus/main_results/policy_support_tool/","section":"projects","summary":"Policy support tool for efficient transport policy: using temporal sensitive transport data to identify main accessibility restrictions.\nIntroduction The main aim of the study is to prepare a methodological guidelines and describe data needs related to the evaluation of an impact of particular transport related restrictions of accessibility at the level of the whole city or metropolitan area (global restrictions) and spatial pattern of their distribution (local restrictions). The results of the analyses enable to identify main causes of the limited accessibility in a given area which in turn can be used as a base of the strategic decisions leading towards more efficient transport system.","tags":["accessibility","GTFS","speed profiles"],"title":"Policy support tool","type":"docs"},{"authors":null,"categories":null,"content":"The interactive map below facilitates an exploration of the results of accessibility analysis in Madrid. The map indicates how many jobs are available from a given area (transport zone) assuming a typical commuting habits observed in the city. You can switch between four different scenarios of accessibility measurement:\n Car: free flow speeds Car: congested speeds Public transport: no waiting times Public transport: average travel time  By clicking any of transport zones, you can see a popup which shows the values of all four scenarios, an ID code of the zone and zone´s population.\n(Note that it takes a while the map to be loaded)\n \n","date":1557010800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1557010800,"objectID":"6acbfe034e4916c1d04d5b6c43aa30cc","permalink":"https://marcinstepniak.eu/projects/calculus/repository/interactive_map_madrid/","publishdate":"2019-05-05T00:00:00+01:00","relpermalink":"/projects/calculus/repository/interactive_map_madrid/","section":"projects","summary":"The interactive map below facilitates an exploration of the results of accessibility analysis in Madrid. The map indicates how many jobs are available from a given area (transport zone) assuming a typical commuting habits observed in the city. You can switch between four different scenarios of accessibility measurement:\n Car: free flow speeds Car: congested speeds Public transport: no waiting times Public transport: average travel time  By clicking any of transport zones, you can see a popup which shows the values of all four scenarios, an ID code of the zone and zone´s population.","tags":null,"title":"Interactive map","type":"docs"},{"authors":null,"categories":null,"content":"At the moment, links to publications from the project are available here.\n\n","date":1557010800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1557010800,"objectID":"c16dd3f7295efe41396f5205e7b929e6","permalink":"https://marcinstepniak.eu/projects/calculus/repository/publications/","publishdate":"2019-05-05T00:00:00+01:00","relpermalink":"/projects/calculus/repository/publications/","section":"projects","summary":"At the moment, links to publications from the project are available here.","tags":null,"title":"Publications","type":"docs"},{"authors":null,"categories":null,"content":" This section collects links to all presentations related to the project CALCULUS.\nInvited talks  Measuring transport related accessibility restrictions: invited talk prapared for the meeting URBAN 2030 - Monitoring and reporting of the urban and territorial dimensions of Social Development Goals (SDGs) – Expert Group Meeting on Transport indicators. Meeting was organized by DG Regio in collaboration with EU Joint Research Centre, and supported by UN-Habitat.\nBrussels, June 26th, 2019.  Conferences and workshops NECTAR workshops \u0026amp; conferences Presentation prepared for NECTAR (Network on European Communications and Transport Activities Research) meetings:\n Temporal dimension of accessibility. Application for detection of causes of low accessibility: Talk presented at the NECTAR (Network on European Communications and Transport Activities Research) Cluster 6 (Accessibility) the Tenth Anniversary meeting.\nLas Palmas de Gran Canaria, December 14th-15th, 2018\n The impact of temporal resolution on the precision of accessibility measurement: Slides presented at the event Accessibility in urban modelling: from measurement to policy instruction co-organized by NECTAR Cluster 6 and Urban Europe Research Alliance (UERA).\nLyon, June 18th-20th, 2018\n Detecting causes of low urban accessibility: a comparative approach: Presentation delivered at the conference Smart cities and value of time co-organized by Instituto Tecnológico de Santo Domingo (Intec) and the University of Twente.\nSanto Domingo, January 18th-19th, 2018\n  Other conferences:  RSA (Regional Studies Association): New data sources, temporal variability and identification of causes of low urban accessibility: Slides presented at the 2019 RSA Annual Conference Pushing Regions beyond their Borders in the session Transport Infrastructure Planning.\nSantiago de Compostela, June 5th-7th, 2019\n CIT (XIII Congreso de Ingeniería del Transporte CIT 2018): Causes of low urban accessibility: a comparative approach\nPresentation delivered at the XIII Congreso de Ingeniería del Transporte (CIT 2018) Nuevos retos en el transporte: menos es Más.\nGijon, June 6th-8th, 2018\n  Seminars  IGSO PAS: Identyfikacja przyczyn niskiej dostępności przestrzennej: nowe źródła danych, zmienność w czasie i konkurencja międzymodalna (in Polish): Presentation at the seminar organized by Department of Urban and Population Studies of Institute of Geography and Spatial Organization, Polish Academy of Sciences.\nWarsaw, April 1st, 2019  \n","date":1557010800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1557010800,"objectID":"93d42c70f2ef15ea98af39cb70a5000a","permalink":"https://marcinstepniak.eu/projects/calculus/repository/slides_talks/","publishdate":"2019-05-05T00:00:00+01:00","relpermalink":"/projects/calculus/repository/slides_talks/","section":"projects","summary":"This section collects links to all presentations related to the project CALCULUS.\nInvited talks  Measuring transport related accessibility restrictions: invited talk prapared for the meeting URBAN 2030 - Monitoring and reporting of the urban and territorial dimensions of Social Development Goals (SDGs) – Expert Group Meeting on Transport indicators. Meeting was organized by DG Regio in collaboration with EU Joint Research Centre, and supported by UN-Habitat.\nBrussels, June 26th, 2019.","tags":null,"title":"Slides and talks","type":"docs"},{"authors":[],"categories":[],"content":" I have created my first (ever!) #rstats package. It is called {DepartureTime} and its purpose is to prepare a dataset with several departure times for temporally sensitive accessibility analysis.\nWhat does {DepartureTime} do? The package consists of one function which permits you to generate a series of departure times applying user-defined temporal resolution and one of four different sampling procedures:\n Systematic sampling method: departure times are selected using a regular interval defined by the frequency Simple Random sampling method: a specified number of departure times (defined by the frequency) is randomly selected from the time window Hybrid sampling method: departure times are randomly selected from given time intervals (resulted from applied temporal resolution) Constrained Random Walk Sampling sampling method: a first departure time is randomly selected from the subset of the length defined by the frequency and beginning of the time window; then, the next departure time is randomly selected from the subset limited by \\(Tn+f/2\\) and \\(Tn+f+f/2\\).  An example of the result of sampling procedures for 20-minute temporal resolution and 1-hour-long time window (07:00-08:00) illustrates the following table:\n   Sampling method Departure times Comments     Systematic 07:00; 07:20, 7:40, 08:00 regular interval of 20 minutes1   Simple Random 07:18; 07:51; 07:55 3 randomly selected departure times from the time window2   Hybrid 07:02; 07:23; 07:50 One randomly selected departure time from each time interval period3   Random Walk 07:15; 07:36; 07:49 on average there should be 20-minute interval between departure times4    1 as 20-minute interval fits to 60 minute time window it provides 4 departure times.\n2 i.e. one per each 20 min. in 60-minute time window.\n3 i.e. one from 07:00-07:19, one from 07:20-07:39 and one from 07:40-07:59.\n4 due to the nature of the sampling procedure, the number of departure times might differ.\nFor details on sampling procedures, please consult Owen \u0026amp; Murphy (2018).\nWhy may you need {DepartureTime}? Briefly: because, if you include temporal dimension to your accessibility analysis, the result depends on the applied departure time. In particular, in case of public transport, accessibility level in a given area might be highly affected by the fact whether you are \u0026ldquo;lucky\u0026rdquo; or not in terms of waiting times (for the first connection and/or in case of transfers) and, in result, these values might differ substantially just because you decided on a particular departure time. In order to limit this negative impact on analysis, you need to consider different departure times and than aggregate results in order to better reflect how does one experience level of accessibility.\nConsider this graph presented by Owen \u0026amp; Levinson (2015):\n{width=400px}\nIt shows how accessibility change over time for a selected census block. Depending on the selected departure time, you can get completely different accessibility level, even though the transport system nor the distribution of activities does not change.\nFurther, even if the level of availability does not change so drastically, you may still want to simulate different situation e.g. applying free flow, peak or out-of-peak speeds. The graph below, prepared for the Dutch case study, compares daily variation of job accessibility by car and by public transport (walk-and-ride and bike-and-ride models):\nThus, regardless transport mode, you may need to repeat analysis without changing anything but departure time (even though, a required temporal resolution would change): in case of car accessibility you may need to generate origin-destination matrices couple of times during the day, while in case of public transport you should compute travel times even couple of times per hour (I you need more information on the consequences of applied temporal resolution on precision of accessibility analysis I can shamelessly suggest you the following paper: Stepniak, Pritchard, Geurs \u0026amp; Goliszek (2019)).\nHow can you take advantage of {DepartureTime}? As described in the paper, you can select different approach how to tackle the issue of temporal resolution. Regardless which approach you select, you would need a table which contains all departure times in order to automatize calculations. I used it in ArcGIS Network Analyst (with Add GTFS to a Network Dataset tool), but as far as I know, {DepartureTime} may be also useful when working with OpenTripPlanner.\nIn ArcGIS you can easily prepare an arcpy code or just a ModelBuilder, which permits you to iterate by subsequent departure time. The simple ModelBuilder looks like this:\nThe .dbf file with the output from {DepartureTime} you need to locate in Departure Times (market with the red circle) and iterate it by Field Values (by Date field, setting Data type as Date)\nIn result of the above model, you obtain a set of origin-destination matrices, one for each of a departure time, exported to separate files (e.g. .dbf files). Then, they can be aggregated in order to obtain more realistic average travel time (or average accessibility), e.g. during the morning peak-hours or during the day (night etc.).\nHow does {DepartureTime} work? The {DepartureTime} package can be installed in R directly from GitHub (if you don\u0026rsquo;t have {devtools} installed, you need to install it first):\n# install.packages(\u0026quot;devtools\u0026quot;) devtools::install_github(\u0026quot;stmarcin/DepartureTime\u0026quot;)  The function has the following syntax and default values:\nDepartureTime \u0026lt;- function(method = \u0026quot;H\u0026quot;, dy = format(Sys.Date(), \u0026quot;%Y\u0026quot;), dm = format(Sys.Date(), \u0026quot;%m\u0026quot;), dd = format(Sys.Date(), \u0026quot;%d\u0026quot;), tmin = 0, tmax = 24, res = 5, MMDD = TRUE, ptw = FALSE)  Function variables:\n method - sampling method; Options:  R OR Random: Simple random sampling; S OR Systematic: Systematic sampling; H OR Hybrid: Hybrid sampling; W OR Walk: Constrained random walk sampling;  dy, dm and dd - date of the analysis (formats: YYYY, MM, DD); default: system date; tmin and tmax - limits of the time window (format: HH); default: full day (00:00 - 24:00); res - temporal resolution; default: 5 minutes MMDD - date format of the output (TRUE / FALSE) default: TRUE  TRUE: MM/DD/YYYY; FALSE: DD/MM/YYYY;  ptw - print limits of subsetted time-windows; default: FALSE;  The DepartureTime() function creates a data frame with generated departure times, already formatted for ArcGIS:\n  ColumnName  Description      ID  rowID (integer), starts with 0    Date  Departure date \u0026amp; hour     Example with selected user-defined parameters:\nDepartureTime(method = \u0026quot;S\u0026quot;, # systematic sampling method dm = 5, dd = 15, # user-defined date: 15th May, 2020 (current year) tmin = 7, tmax = 9, # user-defined time window (07:00 - 09:00) res = 20) # user-defined temporal resolution (20 minutes)    ID  Date      0  05/15/2020 07:00    1  05/15/2020 07:20    2  05/15/2020 07:40    3  05/15/2020 08:00    4  05/15/2020 08:20    5  05/15/2020 08:40    6  05/15/2020 09:00     You can easily save {DepartureTime} output as .dbf file (you need to have {foreign} package installed):\nlibrary(DepartureTime) library(foreign) library(dplyr) # generate departure times for 8-10am time window # with 30-minute temporal resolution applying hybrid sampling model: DepartureTime(tmin = 8, tmax = 10, res = 30) %\u0026gt;% #save output in OD_analysis subfolder as My_Departure_Times.dbf write.dbf(\u0026quot;OD_analysis/My_Departure_Times.dbf\u0026quot;)  Questions? If you have any questions, feel free to contact me or fill an issue on github.\nFurther reading Murphy, B., Owen, A., 2019. Temporal sampling and service frequency harmonics in transit accessibility evaluation Journal of Transport and Land Use 12, 893–913. https://doi.org/10.5198/jtlu.2019.1379\nOwen, A., Levinson, D.M., 2015. Modeling the commute mode share of transit using continuous accessibility to jobs Transportation Research Part A: Policy and Practice 74, 110–122. https://doi.org/10.1016/j.tra.2015.02.002\nOwen, A., Murphy, B., 2018. Temporal Sampling and Service Frequency Harmonics in Transit Accessibility Evaluation, in: Transportation Research Board 97th Annual Meeting. p. 10.\nPritchard, J.P., Stępniak, M., Geurs, K.T., 2019. Equity analysis of dynamic bike-and-ride accessibility in the Netherlands, in: Lucas, K., Martens, K., Ciommo, F. Di, Dupont-Kieffer, A. (Eds.), Measuring Transport Equity. Elsevier, pp. 73–83. https://doi.org/10.1016/B978-0-12-814818-1.00005-6\nStępniak, 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 75, 8–24. https://doi.org/10.1016/j.jtrangeo.2019.01.007\n","date":1589760000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1589786132,"objectID":"09417ac6236a757c485593861eb1b165","permalink":"https://marcinstepniak.eu/post/departuretime-r-package/","publishdate":"2020-05-18T00:00:00Z","relpermalink":"/post/departuretime-r-package/","section":"post","summary":"I have created my first (ever!) #rstats package. It is called {DepartureTime} and its purpose is to prepare a dataset with several departure times for temporally sensitive accessibility analysis.\nWhat does {DepartureTime} do? The package consists of one function which permits you to generate a series of departure times applying user-defined temporal resolution and one of four different sampling procedures:\n Systematic sampling method: departure times are selected using a regular interval defined by the frequency Simple Random sampling method: a specified number of departure times (defined by the frequency) is randomly selected from the time window Hybrid sampling method: departure times are randomly selected from given time intervals (resulted from applied temporal resolution) Constrained Random Walk Sampling sampling method: a first departure time is randomly selected from the subset of the length defined by the frequency and beginning of the time window; then, the next departure time is randomly selected from the subset limited by \\(Tn+f/2\\) and \\(Tn+f+f/2\\).","tags":["rstats","GTFS","CALCULUS"],"title":"{DepartureTime} R package","type":"post"},{"authors":[],"categories":[],"content":"Below you can find info about some transport geography events which come into my radar. I am aware that there is a lot more happing in the following days. If you know about a conference, seminar or workshop that fits to the profile of those listed below - please, let me know and I will update this list.\n NECTAR (Network on European Communications and Transport Activities Research) Cluster 6 Workshop on The Role of Accessibility in times of Technological Innovation, (Re-)Urbanization and Climate Change\nWhere and when: Munich, December 12 to 13, 2019\nDeadline for submission of abstracts (500-1000 words): October 11, 2019\nDetails\n NECTAR (Network on European Communications and Transport Activities Research) Special Session at the World Symposium on Transport and Land Use Research (WSTLUR) on Accessibility and quality of life\nWhere and when: Portland (Oregon), July 13 to 16, 2020\nDeadline for submission of full papers: November 15, 2019\nSelected papers will be published as part of a special issue in the Journal of Transport and Land Use (JTLU).\nDetails\n WSTLUR World Symposium on Transport and Land Use Research 2020\nWhere and when: Portland (Oregon), July 13 to 16, 2020\nDeadline for submission of full papers: November 15, 2019\nSelected papers will be published as part of a special issue in the Journal of Transport and Land Use (JTLU).\nDetails\n IGU (International Geographical Union) several Special Sessionns organized by the IGU Transport \u0026amp; Geography Commission at the International Geographical Congress 2020\nWhere and when: Munich, Istanbul, August 17-21, 2020\nDeadline for submission of abstracts (up to 300 words): January 13, 2020\nDetails\nList of Special Sessions:\n Transport Geography and Climate Change (Tim Ryley \u0026amp; Tim Schwanen)\n Urban Transport Geography (Andrew Goetz, Markus Hesse \u0026amp; Becky Loo)\n Bridging the Future of Port Geography (Claude Comtois)\n Transport and Social Equity: Pathways to Fair, Just, Inclusive and Accessible Mobilities (Pengjun Zhao, Alexandros Nikitas)\n Mobility-as-a-Service: shaping the future of cities (Ana Condeco-Melhorado, Juan Carlos García-Palomares \u0026amp; Alexandros Nikitas)\n \u0026lsquo;Be a hub or die\u0026rsquo; - The polarizing transport connectivity and its impacts in megacity regions (Chia-Lin Chen \u0026amp; James Wang)\n Beyond the traditional regulation of transport services: The penetration of third countries\u0026rsquo; markets (Frédéric Dobruszkes \u0026amp; Tay T. R. Koo)   Additionally, for those who are based in (or intent to visit) Spain:\n CIT (Congreso de Ingeniería del Transporte) 14th Conference on Transport Engineering on R-Evolution of Transport\nWhere and when: Munich, Burgos, June 24-26, 2020\nDeadline for submission of abstracts (up to 300 words): October 31, 2019\nSelected papers will be published in a special issue of the journal Transportation Research Procedia.\nDetails\n  \n","date":1570492800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1570492800,"objectID":"8175da6501c78d00fd8e3f1d97f5a8ac","permalink":"https://marcinstepniak.eu/post/upcoming-transport-geography-events/","publishdate":"2019-10-08T00:00:00Z","relpermalink":"/post/upcoming-transport-geography-events/","section":"post","summary":"Below you can find info about some transport geography events which come into my radar. I am aware that there is a lot more happing in the following days. If you know about a conference, seminar or workshop that fits to the profile of those listed below - please, let me know and I will update this list.\n NECTAR (Network on European Communications and Transport Activities Research) Cluster 6 Workshop on The Role of Accessibility in times of Technological Innovation, (Re-)Urbanization and Climate Change","tags":["Newsletter"],"title":"Upcoming transport geography events","type":"post"},{"authors":[],"categories":[],"content":" In this post, I would like to share how to prepare an interactive map using #rstats and {tmap} package. The first part shows how to make a simple (one thematic layer), interactive choropleth map and save an output as a .html file, which can be then inserted to a website or serve as a stand alone web. A {tmap} provides with a certain interactivity: zoom in/out and map moving, a popup display and a selection of visibility of basemaps and/or thematic layers.\n The map shows an total number of accessible jobs from a given location (transport zone), applying a typical commuting distance of inhabitants of Metropolitan area of Madrid (more details about used potential accessibility measure, can be found here).\nData used for this example can be downloaded from the github. The accessibility values were calculated for the MSCA CALCULUS project. The shapefiles of transport zones of Madrid can be downloaded from the open data portal of the Comunidad de Madrid (Delimitaciones Territoriales / Zonas de Transporte). In this map I use the zones dated for year 2013 limited to those located within the Municipality of Madrid (584 units).\nStep 1. Load packages and get the data library(readr) library(dplyr) library(tmap) library(rgdal)  For the map we need a shapefile with transport zones of Madrid (Madrid_TAZ.shp) and csv file with accessibility values, both stored in the [Data] subfolder. The code below loads a shapefile (as SpatialPolygonsDataFrame) and merges it with the .csv. Additionally, the code recalculates the total number of accessible jobs (an original Ai_tmap.csv files contains results in thousands) which will be used in the popup.\nFree.Flow \u0026lt;- merge( # read shapefile readOGR(\u0026quot;Data/Madrid_TAZ.shp\u0026quot;, layer = \u0026quot;Madrid_TAZ\u0026quot;, GDAL1_integer64_policy = TRUE), # read csv file add a new column to be displayed in popup (read_csv(\u0026quot;Data/Ai_tmap.csv\u0026quot;) %\u0026gt;% mutate(K.FreeFlow = FreeFlow*1000) ), # matching columns by.x = \u0026quot;TAZ_Madr_1\u0026quot;, by.y = \u0026quot;Or\u0026quot;)  Step 2. Make a (t)map! First, let\u0026rsquo;s set the tmap \u0026ldquo;view\u0026rdquo; mode:\ntmap_mode(\u0026quot;view\u0026quot;)  The advantage of using tmap is that it shares a logic with ggplot. Thus, first we need to create a tmap object (tm_shape()) followed by a thematic layer (tm_polygons()), and then additional parameters (e.g. tm_layout()) should be specified. The subsequent elements we add using a +.\nLet\u0026rsquo;s start with the first map using all default settings. We need to define the source of thematic layer (Free.Flow - a SpatialPolygonsDataFrame object) and a variable which defines a thematic layer (FreeFlow):\ntm_shape(Free.Flow) + tm_polygons(\u0026quot;FreeFlow\u0026quot;)  Here it is! A first interactive map. In the top-left corner, we can turn off the visibility of our thematic layer and select one of the three default basemaps.\nThe popup shows us a value taken from the column used to create a thematic layer (FreeFlow) and it uses a first column to define area ID. We can change it now, using a popup.vars parameter of tm_polygons() function. The content of the popup is defined as: some.text = variable.name and a variable.name should be matched to the name of column of a thematic layer (as defined by tm_shape()). We use two columns: K.FreeFlow which contains a total number of available jobs, and POP2017 indicating a total number of population:\ntm_shape(Free.Flow) + tm_polygons(\u0026quot;FreeFlow\u0026quot;, # popup definition popup.vars=c( \u0026quot;Accessible_jobs: \u0026quot;=\u0026quot;K.FreeFlow\u0026quot;, \u0026quot;Population: \u0026quot; = \u0026quot;POP2017\u0026quot;) )  One remark: by default the popup uses a first column of our SpatialPolygonsDataFrame to display an area ID. We can use variables from another column by defining an id parameter:\ntm_shape(Free.Flow) + tm_polygons(\u0026quot;FreeFlow\u0026quot;, # popup definition popup.vars=c( \u0026quot;Accessible_jobs: \u0026quot;=\u0026quot;K.FreeFlow\u0026quot;, \u0026quot;Population: \u0026quot; = \u0026quot;POP2017\u0026quot;), id = \u0026quot;TAZ_Madrid\u0026quot; )  Now, let\u0026rsquo;s change the color palette and transparency of the thematic layer. I want to use the inferno palette - a viridis-based palette matched by {colorspace} team. I want to divide my data into as much as 16 classes. Additionally, I want to set the transparency of the thematic layer. In result, I need to define three parameters: palette, n (number of classes) and alpha (transparency):\nalpha = 0.6, n = 16, palette = hcl.colors(16, palette = \u0026quot;Inferno\u0026quot;)  However, the darkest colors of the inferno palette are a bit too dark for me, so I want to exclude them. In order to do this, first I set more colors from the palette, and then filter out first two of them. Now, our code looks like this:\ntm_shape(Free.Flow) + tm_polygons(\u0026quot;FreeFlow\u0026quot;, # popup definition popup.vars=c( \u0026quot;Accessible_jobs: \u0026quot;=\u0026quot;K.FreeFlow\u0026quot;, \u0026quot;Population: \u0026quot; = \u0026quot;POP2017\u0026quot;), id = \u0026quot;TAZ_Madrid\u0026quot;, # transparency, number of classes and palette alpha = 0.6, n = 16, palette = hcl.colors(18, palette = \u0026quot;Inferno\u0026quot;)[3:18] )  The zones\u0026rsquo; borders are too visible so we need to increase their transparency and change their color. I\u0026rsquo;ve tested several solutions taking an advantage of this on-line application and selected the one that fits the best:\ntm_shape(Free.Flow) + tm_polygons(\u0026quot;FreeFlow\u0026quot;, # popup definition popup.vars=c( \u0026quot;Accessible_jobs: \u0026quot;=\u0026quot;K.FreeFlow\u0026quot;, \u0026quot;Population: \u0026quot; = \u0026quot;POP2017\u0026quot;), id = \u0026quot;TAZ_Madrid\u0026quot;, # transparency, number of classes and palette alpha = 0.6, n = 16, palette = hcl.colors(18, palette = \u0026quot;Inferno\u0026quot;)[3:18], # border definition: color and transparency border.col = \u0026quot;#990099\u0026quot;, border.alpha = 0.1 )  The map is almost ready. The last amendments: title of the legend and the map title. The previous is defined by the title parameter of tm_polygons() function, while the latter by a parameter of a separate function tm_layout():\ntm_shape(Free.Flow) + tm_polygons(\u0026quot;FreeFlow\u0026quot;, # popup definition popup.vars=c( \u0026quot;Accessible_jobs: \u0026quot;=\u0026quot;K.FreeFlow\u0026quot;, \u0026quot;Population: \u0026quot; = \u0026quot;POP2017\u0026quot;), id = \u0026quot;TAZ_Madrid\u0026quot;, # transparency, number of classes and palette alpha = 0.6, n = 16, palette = hcl.colors(18, palette = \u0026quot;Inferno\u0026quot;)[3:18], # border definition: color and transparency border.col = \u0026quot;#990099\u0026quot;, border.alpha = 0.1, # title of the legend title = \u0026quot;Accessible jobs\u0026lt;br\u0026gt;(thous.)\u0026quot; ) + # map title tm_layout(title = \u0026quot;Accessibility to jobs\u0026lt;br\u0026gt;Model: Car free flow speeds\u0026quot;)  Note, that in both cases, a typical html tags (like \u0026lt;br\u0026gt;) can be used.\nStep 3. Save and re-use the output The map is ready! Now, we only need to save it as a html widget. We can use a native tmap_save() function:\ntmap_last() %\u0026gt;% tmap_save(\u0026quot;Madrid_accessibility_map.html\u0026quot;)  It can be then inserted to a web page:\n\u0026lt;iframe src=\u0026quot;/img/Madrid_accessibility_map.html\u0026quot; frameborder=0, height=400, width=\u0026quot;100%\u0026quot;, scrolling=\u0026quot;no\u0026quot;\u0026gt;\u0026lt;/iframe\u0026gt;  All the code and the data can be downloaded from my github.\n\nSession info:\n R version 3.6.0 (2019-04-26)\nPlatform: x86_64-apple-darwin15.6.0 (64-bit)\nRunning under: macOS Mojave 10.14.5\n \nSome useful links:\n tmap: get started!\n tmap in a nutshell The greate on-line course Using R for Data Journalism by Andrew Ba Tran The chapter in Geocomputation with R by Robin Lovelace, Jakub Nowosad and Jannes Muenchow  \n","date":1569369600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1569369600,"objectID":"99a6a86b0da0cfe8591dd36be904be45","permalink":"https://marcinstepniak.eu/post/interactive-choropleth-maps-with-r-and-tmap-part-i/","publishdate":"2019-09-25T00:00:00Z","relpermalink":"/post/interactive-choropleth-maps-with-r-and-tmap-part-i/","section":"post","summary":"In this post, I would like to share how to prepare an interactive map using #rstats and {tmap} package. The first part shows how to make a simple (one thematic layer), interactive choropleth map and save an output as a .html file, which can be then inserted to a website or serve as a stand alone web. A {tmap} provides with a certain interactivity: zoom in/out and map moving, a popup display and a selection of visibility of basemaps and/or thematic layers.","tags":["TransportViz","rstats","rspatial","tmap","CALCULUS"],"title":"Interactive choropleth maps with R and tmap (part I)","type":"post"},{"authors":[],"categories":[],"content":"Lastly, a friend of mine asked me about github so, apart from writing him back, I´ve decided to prepare a short blog post with a very basic introduction to github and RStudio. I hope someone finds it useful. It is not a manual \u0026ldquo;how to use git and RStudio\u0026rdquo; - there is a very good and comprehensive book on this topic by Jenny Bryan (which I highly recommend!). This post is as short as it can be, assuming that:\n you use RStudio, you have only a very general idea how git (and github) works, you want to start to work with git but you are afraid that something can go wrong, first of all, you want to share your code and having a version control is just an extra asset (at the moment).  The approach here is the following:\n create a repository on github clone it to your desktop add all the code you want to your repository (locally) commit (save) changes and push them to the github.  OK. Let's do it. First - you need to create a github account. Go to https://github.com/ and click Sign up. Fill in all required fields and create an account. Then, sign in and press your avatar´s icon (on the top-right of the page). Select Your repositories. Once you see the list of Your repositories (probably empty for the moment), you should click New in order to create a new repo:    Now, you need to name your repo (here: test-repo) and you are free to add a brief description. Still not sure about the result and prefer to keep it private until you confirm everything is correct? No problem, you can opt for a private repository. You will make it public, simply by changing the option in the settings of your repo. Would you like someone to check your code (and/or repo) before it goes public? No problem either - you can add collaborators, even if you are using a GitHub Free version (in that case, you can add up to 3 collaborators in private repos).\nThen, opt for Initialize this repository with a README (a README file is the one everyone sees when viewing your repo). I suggest also to add a .gitignore (select R from a drop-down menu). A .gitignore file contains a list of the files which will be ignored by the git, which means they will not be committed (saved) nor push to the remote repository, so they will not be stored on github. Finally, you can select a licence under which your code will be distributed.\nChoose Create a repository.\nYour repository is now alive and you can revise it.\n This is the name of your repository. You can see that the repository is private (so no one can see it).\n A brief description of your repository (the one you provided with in the previous step).\n A list of the files in your repository. At the moment there are only the ones created automatically by github.\n A content of the README.md file. This is a place where you will provide with a description of your code/repository.\n  Everything is set for cloning your repo to your computer. Before we move to RStudio, you need to copy an URL of your repo. Use a Clone or download button (5 on the print screen above) and click copy icon.    Now you need to create a new project in RStudio. In RStudio, go to File -\u0026gt; New Project and select Version Control. Then select Git.\n    Paste a repository URL and navigate to the folder you would like to locate your repo. RStudio will create a subfolder for you, using the name of your repo as a name of a new subfolder.   One of the Panes of RStudio is for Git. You can see there all uncommitted changes, i.e. the files which have been changed since the last commit. At the moment, there is a list of files that are different than in your remote repository (on your github account).    There is only one file - a .Rproj file. Before you place your code in the folder you have just created, I suggest you to add the .Rproj file to .gitignore. You need to open .gitignore and then type name-of-your-repo.Rproj at the end of the .gitignore file (in my case it would be test-repo.Rproj). Save a .gitignore file, and a .Rproj file disappears from your Git window, replaced by .gitignore.\nLet\u0026rsquo;s use the .gitignore file to test a commit - a function which save changes in your repo. Click Commit button. You will be moved to a new window. In the box at the bottom, you can check what changes have been made in your file (see below: 1). In my case, I have added three new lines:\n 37 (empty line)\n38 (comment # my files)\n39 (added test-repo.Rproj so it will be ignored by git)\n You need to mark (2) the file you want to commit (i.e. save changes in your repository - in the example it is .gitignore), and add a short Commit message (it will serve also as a name of your version; should be short but meaningful). Everything is ready for the Commit, so click the button (4)! You should get an info about your update saying:\n [master some-numbers-and-letters-here] gitignore update\n1 file changed, 3 insertions(+)\n The second line reports details of your update (1 changed file with 3 new lines).\nAll changes are made locally. What you need now, is to push your local changes to the remote repository (i.e. to github). Click Push button (5). If everything works, you get an output similar to the one below (the first line is an URL of your remote repository):\n To https://github.com/stmarcin/test-repo.git\nsome-numbers-and-letters-here..some-numbers-and-letters-here HEAD -\u0026gt; master\n Done! Go to your github account (or refresh your web-browser) and you should see your commit message (1) and info about your latest commit (2). If you click a commit message (here: gitignore update) you can see all changes you have made!\nThe last passage consists of the following steps:\n Add files to the RStudio project folder (I have added two of them: R_01_some_code.R and R_02_some_another_code.R). They appear in a Git pane. Click Commit button.   Select all files (stage them; 1) Add a Commit message (2) Commit changes (3) Push changes to the remote repository (4).  Now you can/should edit a README.md file, providing all necessary information about your code. Once it is done, you can make your repo public. Click Settings of your repo, scroll down to the Danger Zone and select Make public.\nCongratulations! Your code is now on GitHub. Instead of conclusions, I have an extra point: if you want your repository to get a DOI number (e.g. in order to make your code citable) you can find useful this manual: https://guides.github.com/activities/citable-code/.\n\n","date":1568332800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1568332800,"objectID":"a0992343bcb1e2441eaa7d44b3151148","permalink":"https://marcinstepniak.eu/post/intro-to-very-basic-github-rstudio/","publishdate":"2019-09-13T00:00:00Z","relpermalink":"/post/intro-to-very-basic-github-rstudio/","section":"post","summary":"Lastly, a friend of mine asked me about github so, apart from writing him back, I´ve decided to prepare a short blog post with a very basic introduction to github and RStudio. I hope someone finds it useful. It is not a manual \u0026ldquo;how to use git and RStudio\u0026rdquo; - there is a very good and comprehensive book on this topic by Jenny Bryan (which I highly recommend!). This post is as short as it can be, assuming that:","tags":[],"title":"A very basic intro to GitHub \u0026 RStudio","type":"post"},{"authors":[],"categories":[],"content":"Finally, I have made it. I have designed and deployed my first personal webpage. And here it is. It would not be possible without Hugo Academic and a lot of available on-line resources which facilitates me to understand how it is organized, how can I change / add anything and how to make it working. There is a bench of very good manuals how to prepare your webpage using RStudio, blogdown, Hugo Academics, github and netlify. I am not going to repeat their work, but I am going to prepare a blog post with all relevant links. Perhaps someone find it useful. Believe me - it is quite straightforward.\nThe most important motivation for this web is to promote and disseminate results of my MSCA CALCULUS project. However, I love the opportunity of having my own blog. Twitter is great, but sometimes it is not a perfect format to share some ideas, results or opinions. Having a blog, additionally written in .Rmarkdown format, allows much more and I am going to use this opportunity.\nAnd last but not least - I prepared this page as best as I could. Nevertheless, there are still many things that I would like to improve, add or simply change. It is not perfect, but it allows me to improve things later. On the other hand, an existing web is (usually) better than a planned one. So here it is. More to come soon. I hope you will enjoy. Stay tuned. And follow me on Twitter.\n","date":1565913600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1565913600,"objectID":"fc6b69c4d876098898f78a496de468f5","permalink":"https://marcinstepniak.eu/post/on-a-value-of-imperfection/","publishdate":"2019-08-16T00:00:00Z","relpermalink":"/post/on-a-value-of-imperfection/","section":"post","summary":"Finally, I have made it. I have designed and deployed my first personal webpage. And here it is. It would not be possible without Hugo Academic and a lot of available on-line resources which facilitates me to understand how it is organized, how can I change / add anything and how to make it working. There is a bench of very good manuals how to prepare your webpage using RStudio, blogdown, Hugo Academics, github and netlify.","tags":null,"title":"On a value of imperfection","type":"post"},{"authors":["Stępniak Marcin","Pritchard John P","Geurs Karst T","Goliszek Sławomir"],"categories":[],"content":"","date":1564350446,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1564350446,"objectID":"c7a3a68ed7a025fc396f850073b60a72","permalink":"https://marcinstepniak.eu/publication/stepniak_et_al_2019_jtrg/","publishdate":"2019-07-28T23:47:26+02:00","relpermalink":"/publication/stepniak_et_al_2019_jtrg/","section":"publication","summary":"In recent years there has been a significant increase of temporally variable analyses of accessibility by public transport as a result of the increased availability of open and standardized time table information in the form of GTFS (General Transit Feed Specification) data. To date, very little attention has been paid to systematically analyze the impact of temporal resolutions on the results. Different authors have applied different standards, often in an ad-hoc manner. In this study, we address the loss of precision associated with a stepwise reduction of the temporal resolution of travel time estimations based on GTFS data for the city of Szczecin in Poland. The paper aims to provide guidance to researchers and practitioners on the selection of appropriate temporal resolutions in accessibility studies. We test four sampling methods in order to analyze four different public transport frequency scenarios, three types of accessibility measures (travel time to the nearest provider, cumulative opportunities measure and potential accessibility) and seven types of destinations ranging from high to low centrality. Additionally, the impact on spatial disparities is explored using the Gini coefficient. We find that a reduction of temporal resolution is associated with a decrease in precision of public transport accessibility measurement. However, with up to 5-min resolutions this reduction is negligible, while computational time is reduced fivefold, compared to a 1-min resolution benchmark. Lower temporal resolutions still provide relatively precise estimations of travel times and accessibility measures. However, further resolution reductions are associated with decreasing reductions of computational time. As a result, we argue that 15-min temporal resolution provides a good balance between precision and computational time while providing very precise estimations of Gini coefficients (errors ≤0.001). A non-linear relationship is found between the public transport frequency and the loss of precision, with lower frequencies leading to a greater loss in precision. More attention should be paid to highly centralized services, in particular when analyzed using proximity and cumulative opportunities measures. Finally, the cumulative opportunities measure is found to be highly sensitive to changes in the temporal resolution and not suited for time-sensitive accessibility analysis.","tags":["GTFS"],"title":"The impact of temporal resolution on public transport accessibility measurement: Review and case study in Poland","type":"publication"},{"authors":[],"categories":null,"content":"Slides from the talk can be found here\n","date":1561539600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1561539600,"objectID":"970b55cc7137f59da938c9e854db52f5","permalink":"https://marcinstepniak.eu/talk/sdg_brussels_201906/","publishdate":"2019-08-01T00:00:00Z","relpermalink":"/talk/sdg_brussels_201906/","section":"talk","summary":"Invited talk at the Expert Group Meeting on Transport indicators.","tags":[],"title":"Measuring transport related accessibility restrictions","type":"talk"},{"authors":["Stępniak Marcin","Jacobs-Crisioni Chris"],"categories":[],"content":"","date":1514497646,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1514497646,"objectID":"bb5e56673b29b8ef8c12f4e52e73c315","permalink":"https://marcinstepniak.eu/publication/stepniak_jacobs-crisioni_2017_jtrg/","publishdate":"2017-12-28T23:47:26+02:00","relpermalink":"/publication/stepniak_jacobs-crisioni_2017_jtrg/","section":"publication","summary":"Analyses of spatial interaction are to some degree plagued by uncertainty regarding the impact of spatially dispersed interaction masses within zones on travel times. In this paper, interaction-weighted travel times are computed from a matrix between regularly distributed points at fine resolution, and used together with secondary data to improve estimates of interaction weighted travel time based on commonly applied methods. The paper proposes a method for computing intra-zonal, interaction weighted travel times that is considerably less sensitive to spatial aggregation than existing approaches, and demonstrates that population-weighted centroids are to be preferred over geographically-weighted centroids.","tags":[],"title":"Reducing the uncertainty induced by spatial aggregation in accessibility and spatial interaction applications","type":"publication"},{"authors":["Stępniak Marcin","Rosik Piotr"],"categories":[],"content":"","date":1509227606,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1509227606,"objectID":"bd23c23730a54658ca4a2f30d4ff6e92","permalink":"https://marcinstepniak.eu/publication/stepniak_rosik_2017_nets/","publishdate":"2017-10-28T23:53:26+02:00","relpermalink":"/publication/stepniak_rosik_2017_nets/","section":"publication","summary":"Investment in transport infrastructure is the main factor responsible for decreasing origin-destination travel times, which are then implemented into a potential accessibility measure. This measure uses population size as a proxy for a destination’s attractiveness. Thus, changes in population distribution as well as the development of the transport infrastructure are mutually responsible for changes in accessibility. The potential accessibility measure is applied to assess change in accessibility in Poland over a twenty year period of time (1995–2015). During this time Poland has experienced a significant change in population distribution. At the same time, accession to the European Union provided an opportunity to use the structural funds and has resulted in an unprecedented development of the transport infrastructure, in particular the road network. The coexistence of both phenomena provides perfect conditions to investigate the complex interrelationship of both components of accessibility, namely transport and land-use. This leads towards a change in the level of accessibility and its spatial pattern, resulting in a transformation of the level of territorial cohesion. However, the selection of the particular impedance parameters greatly influences the importance assigned to an element of infrastructure or a component of population accessibility. Thus, several impedance functions are applied in order to capture their influence on the balance between the infrastructure and population components of accessibility change.","tags":[],"title":"The Role of Transport and Population Components in Change in Accessibility: the Influence of the Distance Decay Parameter","type":"publication"},{"authors":null,"categories":null,"content":"","date":1504224000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1504224000,"objectID":"f2edb8a4db0cc134dbafc5d59abad7f2","permalink":"https://marcinstepniak.eu/project/calculus/","publishdate":"2017-09-01T00:00:00Z","relpermalink":"/project/calculus/","section":"project","summary":"MSCA IF Causes and consequences of low urban accessibility. Defining proper policy responses.","tags":["European","Led"],"title":"MSCA IF CALCULUS","type":"project"},{"authors":["Stępniak Marcin","Goliszek Sławomir"],"categories":[],"content":"","date":1501276662,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1501276662,"objectID":"dc764b9181fb9dc8516fb5d92eae4589","permalink":"https://marcinstepniak.eu/publication/stepniak2017_the-rise-of-big_spatial-data/","publishdate":"2017-07-28T23:17:42+02:00","relpermalink":"/publication/stepniak2017_the-rise-of-big_spatial-data/","section":"publication","summary":"The growth of large, open datasets coupled with an acceleration of technical developments, including GIS solutions, opens the door to new challenges in transport research. One of the emerging fields of research is the temporal dynamics of accessibility. The increase in availability of General Transit Feed Specification (GTFS) data permits the inclusion of very detailed, schedule-based travel time information. In the study presented we focus on the spatial and temporal variation in accessibility by public transport in the city of Szczecin (Poland). This paper advocates the necessity of incorporating a temporal component in accessi- bility analysis. We conducted a full day analysis for 1 day using averaged 15-min-long time periods at a very detailed spatial scale (enumeration districts). Based on the calculated origin-destination matrix in 96 time-profiles we calculated the potential accessibility indicator. Then we investigated spatial disparities and their variability during the day-long observation. Apart from the well-known spatial disparities in accessibility level, our findings underline the uncertainty of the accessibility pattern. Moreover, the results show that less accessible areas are also more affected by the daily variation in accessibility level. The findings provide a more realistic insight into accessibility patterns which will be useful for transport planners and policy makers.","tags":[],"title":"Spatio-Temporal Variation of Accessibility by Public Transport—The Equity Perspective","type":"publication"},{"authors":null,"categories":null,"content":"","date":1500422400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1500422400,"objectID":"8cbd207271fe98a112663b790fef0acd","permalink":"https://marcinstepniak.eu/project/wise_act/","publishdate":"2017-07-19T00:00:00Z","relpermalink":"/project/wise_act/","section":"project","summary":"The WISE-ACT COST Action is a European-wide network that explores the wider impacts of Autonomous and Connected Transport.","tags":["European"],"title":"WISE-ACT COST","type":"project"},{"authors":["Stępniak Marcin","Wiśniewski Rafał","Goliszek Sławomir","Marcińczak Szymon"],"categories":[],"content":"","date":1485640930,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1485640930,"objectID":"63c66ca1df62c39c527cdfce17c66a09","permalink":"https://marcinstepniak.eu/publication/stepniak_et_al_2017_dostepnosc_przestrzenna/","publishdate":"2017-01-29T00:02:10+02:00","relpermalink":"/publication/stepniak_et_al_2017_dostepnosc_przestrzenna/","section":"publication","summary":"Zapewnienie odpowiedniej dostępności do usług publicznych jest jednym z podstawowych zobowiązań państwa względem ich obywateli. Współcześnie, istotnym problemem społecznym jest nie tyle brak zasobów, co raczej brak dostępu do tych zasobów, przy czym usługi publiczne mogą być traktowane jako jeden z rodzajów zasobów. Dostępność do usług publicznych należy zatem traktować jako kluczowe zagadnienie warunkujące jakość życia, stanowiące istotny czynnik determinujący nierówności społeczno-przestrzenne. Dotychczasowe badania dostępności do usług koncentrowały się zazwyczaj na pojedynczym typie usług czy ograniczonym obszarze badań. Zaistniała więc potrzeba kompleksowego ujęcia zagadnienia dostępności do usług publicznych w skali całego kraju, obejmującego również aspekt popytowopodażowy. Po raz pierwszy na tak szeroką skalę zostało zastosowane wielowymiarowe podejście, zgodnie z którym dostępność do poszczególnych typów usług publicznych jest mierzona z wykorzystaniem różnych, specjalnie dopasowanych metod. Szczegółowe analizy dostępności do usług obejmowały pięć kategorii usług publicznych: administrację publiczną (urzędy gminne, powiatowe, wojewódzkie, marszałkowskie, urzędy skarbowe, izby skarbowe, placówki ZUS i KRUS), opiekę nad dziećmi (żłobki, kluby dziecięce), edukację (przedszkola, szkoły podstawowe, gimnazja, szkoły ponadgimnazjalne), opiekę zdrowotną (apteki, przychodnie POZ, szpitale i poradnie, zespoły ratownictwa medycznego, szpitalne oddziały ratunkowe) oraz usługi kulturalne (kina, teatry)","tags":[],"title":"Dostępność przestrzenna do usług publicznych w Polsce","type":"publication"},{"authors":null,"categories":null,"content":".twoC {width: 100%} .clearer {clear: both} .twoC .table {max-width: 50%; float: right} .twoC img {max-width: 50%; float: left}   .column-left{ float: left; width: 25%; text-align: left; } .column-center{ display: inline-block; width: 5%; text-align: center; } .column-right{ float: right; width: 70%; text-align: left; }  The main aim of the proposed project is to identify patterns of accessibility to different types of public services in Poland. In result, an Atlas of accessibility to public services in Poland will be prepared, supplemented by the detailed, geographical databases consisting of geocoded location of public services providers and the accessibility indicators calculated for particular administrative units: municipalities (gminas, LAU-2 units), powiats (LAU-1) and voivodeships (NUTS-3). All calculations will be enabled by the development of an original, GIS-based computer application. Further, we plan to contribute to the development of a methodology in the field of accessibility to public services, with the special attention focused on the potential of GIS techniques for improving the efficiency of accessibility analysis. The methodological review includes GIS-based measures of accessibility of public services, an influence of modifiable areal unit problem, edge effect, temporal organization of service delivery and usefulness of a raster-based approach for an analysis of accessibility to public services. Finally, the project results should provide an answer to the question whether the location of public service providers (such those related to the delivery of health care, education, child care, social care, public administration, culture and labour market services) is spatially correlated with the distribution of certain socio-economic groups provoking the potential inequalities in access to services of general interest.\n  \nGIService project was implemented at IGSO PAS in the years 2014-2017.\nThis research was funded by the National Science Centre allocated on the basis of the Decision No. DEC-2013/09/D/HS4/02679. \n","date":1392163200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1392163200,"objectID":"76970221115f432f84d5753fae0ee5bd","permalink":"https://marcinstepniak.eu/project/giservice/","publishdate":"2014-02-12T00:00:00Z","relpermalink":"/project/giservice/","section":"project","summary":"GIS approach for analysis of spatial accessibility to public services - concepts, methods, implications.","tags":["Led"],"title":"GIService","type":"project"},{"authors":["Stępniak Marcin","Rosik Piotr"],"categories":[],"content":"","date":1382997206,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1382997206,"objectID":"77285e539270a03c65084c5cb44aebdd","permalink":"https://marcinstepniak.eu/publication/stepniak_rosik_2013_jtrg/","publishdate":"2013-10-28T23:53:26+02:00","relpermalink":"/publication/stepniak_rosik_2013_jtrg/","section":"publication","summary":"The aim of this paper is to contribute to previous methodological studies of the approach to potential accessibility used in the evaluation of development of the road network. This is done by making a threefold analysis, which combines the overall improvement in the level of accessibility, territorial cohesion, and spatial spillovers. Moreover, we use different spatial dimensions (namely national and international) and different distance decay parameters to estimate both the short trips (e.g. commuting) and the long trips (e.g. business trips or tourism). The results are presented at a very detailed spatial scale (i.e. municipalities – LAU-2 units). The paper provides empirical evidence of improvement in accessibility, changes in the degree of territorial cohesion, and spatial spillovers resulting from the recent completion of two sections of motorway in Poland. The selected case studies differ according to their location (i.e. national and European, peripheral vs. central location), population density, and the settlement structure around the investment. The validity of the proposed multidimensional approach to the evaluation of road investments is verified as the combination of different accessibility dimensions and leads to results which differ respecting efficiency, equity and spillover effects. This paper provides arguments to strengthen the need for the tailor-made parameters of potential accessibility indicator and spatial dimension of analysis. They should be adjusted to the main aim of the particular evaluation.","tags":[],"title":"Accessibility improvement, territorial cohesion and spillovers: a multidimensional evaluation of two motorway sections in Poland","type":"publication"},{"authors":null,"categories":null,"content":"","date":1288569600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1288569600,"objectID":"6ccd8616936d07948d31ca1ff62d85d0","permalink":"https://marcinstepniak.eu/project/espon_segi/","publishdate":"2010-11-01T00:00:00Z","relpermalink":"/project/espon_segi/","section":"project","summary":"ESPON - Indicators and Perspectives for Services of General Interest in Territorial Cohesion and Development.","tags":["European"],"title":"ESPON SeGI","type":"project"},{"authors":null,"categories":null,"content":"","date":1283299200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1283299200,"objectID":"989c6c1a33600b73070b1acfd92d1a60","permalink":"https://marcinstepniak.eu/project/espon_best_metro/","publishdate":"2010-09-01T00:00:00Z","relpermalink":"/project/espon_best_metro/","section":"project","summary":"ESPON - Best Development Conditions in European Metropolises (Paris, Berlin and Warsaw).","tags":["European"],"title":"ESPON Best Metropolises","type":"project"},{"authors":null,"categories":null,"content":"","date":1264982400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1264982400,"objectID":"f0aeaf0ac213638db73633b2fd8f7194","permalink":"https://marcinstepniak.eu/project/espon_tracc/","publishdate":"2010-02-01T00:00:00Z","relpermalink":"/project/espon_tracc/","section":"project","summary":"ESPON Transport accessibility at regional/local scale and patterns in Europe.","tags":["European"],"title":"ESPON TRACC","type":"project"},{"authors":null,"categories":null,"content":"","date":1249084800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1249084800,"objectID":"e39bfa0a6afb6671b4501b5307fbc6ff","permalink":"https://marcinstepniak.eu/project/infraregtur/","publishdate":"2009-08-01T00:00:00Z","relpermalink":"/project/infraregtur/","section":"project","summary":"Infrastructural and organizational possibilities of spatial accessibility improvement as a factor for development of the Polish-Slovak tourist regions (INTERREG IIIC).","tags":["European"],"title":"INFRAREGTUR","type":"project"}]