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dc.contributor.authorWiśniewski, Szymon
dc.date.accessioned2026-01-09T07:15:34Z
dc.date.available2026-01-09T07:15:34Z
dc.date.issued2024-07
dc.identifier.urihttp://hdl.handle.net/11089/57181
dc.descriptionThe repository contains processed, quality-controlled, and harmonized empirical data used to analyse changes in hourly (diurnal) profiles of car traffic and bus-transport demand in Rzeszów (Poland) across the full study period 2018–2024. The input data originate from two complementary monitoring systems: (1) the municipal ITS system, which provides hourly traffic counts from a network of monitoring locations (e.g., inductive loops and measurements on junctions and key corridors), and (2) Automatic Passenger Counting (APC) in public transport, which records boardings and alightings at stops. APC observations were originally available at a finer temporal resolution and were subsequently aggregated to hourly intervals to ensure full comparability with the ITS data and to support consistent construction of 24-hour profiles. All deposited tables were prepared consistently for the entire investigated timeframe and follow the study design that distinguishes three analytically relevant phases: the pre-pandemic period (2018–2019), the pandemic period (2020–2021), and the post-restriction period (2022–2024). Within each phase, the dataset enables comparisons between weekdays and weekends and supports complete 24-hour diurnal profiles. Data preparation included systematic quality-control and harmonisation procedures applied across the full study period, such as checks for the completeness of hourly observations, identification and removal of erroneous series, and filtering of atypical days (e.g., holidays) and other observations that could bias the comparative analyses. The resulting “analysis-ready” tables are designed to reproduce the key empirical steps reported in the paper, including the derivation of diurnal profiles, period means, spatial summaries for the monitoring network and the stop set included in the analysis, as well as change indicators used in comparative analyses (including inputs for statistical testing and spatial classification). In terms of metadata and data structure, the ITS component is provided as processed hourly traffic-count tables organised by monitoring location (junction/corridor identifier), directional attributes (e.g., approach/entry leg and movement relation where applicable), and an hourly timestamp, with traffic volume expressed as vehicles per hour. The APC component is provided as processed passenger-count tables derived from operational stop-level records, organised by public-transport service identifiers (e.g., line/trip/run descriptors), stop identifiers, and time information, with the key variables describing boardings and alightings aggregated to hourly intervals. Importantly, data of the same type, scope, and processing logic are available and were prepared for the full study period covered by the analyses (2018–2024) for the monitoring network and public-transport stop set included in the research design. Due to data-sharing conditions imposed by the system owners, the repository provides only processed and aggregated data limited to what is necessary to replicate the reported findings at the temporal and spatial-structure level, while the raw ITS and APC records remain with the respective source institutions. The deposited data were used in the publication: Borowska-Stefańska, M., Komornicki, T., Kowalski, M., Plesiński, C., & Wiśniewski, S. (2026). Changes in car-bus mobility in the context of the pandemic and the war in Ukraine, in Rzeszów, Poland (2018–2024). Case Studies on Transport Policy, 23, 101644. https://doi.org/10.1016/j.cstp.2025.101644 Access to files has been restricted, but metadata remains open under the Creative Commons Zero license.pl_PL
dc.description.abstractThe dataset deposited in the repository contains processed and harmonized empirical data used in the article examining changes in the hourly profiles of car traffic and demand for bus transport in Rzeszów (Poland) over the full study period 2018–2024. The input data come from two complementary sources: (1) the municipal ITS system, providing hourly traffic counts from monitoring locations (e.g., inductive loops and measurements on junctions and key corridors), and (2) automatic passenger counting (APC) in public transport, providing stop-based records of boardings and alightings that were originally collected at high temporal resolution and were subsequently aggregated to hourly intervals to ensure full comparability with the ITS data. The repository includes analysis-ready, quality-controlled and aggregated tables prepared consistently for the entire investigated period, covering three time profiles aligned with the study design—pre-pandemic (2018–2019), pandemic (2020–2021), and post-restriction (2022–2024)—with a clear distinction between weekdays and weekends and with complete 24-hour diurnal profiles. As part of the preparation workflow, standard quality-control procedures were applied throughout the study period, including completeness checks for hourly observations, removal of erroneous series and atypical/holiday days, and harmonization of temporal resolution and spatial referencing across sources. The deposited files therefore provide the key inputs required to reproduce the empirical steps of the study at the temporal and spatial-structure level, including hourly diurnal profiles, period-mean summaries, spatial aggregations for the monitoring network and public-transport stops included in the analysis, and change indicators used in comparative analyses (including inputs for statistical testing and spatial classification). In terms of metadata, the ITS component consists of processed hourly traffic-count tables organised by monitoring location (junction/corridor), approach/entry leg, movement relation and hourly timestamp, with traffic volume expressed as vehicles per hour, while the APC component consists of processed passenger-count tables derived from stop-level operational records organised by public-transport line/trip identifiers, stop identifiers and timestamps, with boardings and alightings aggregated to hourly intervals. Due to data-sharing conditions imposed by the system owners, the repository provides only processed and aggregated data limited to what is necessary to replicate the reported findings, while the original raw ITS and APC records remain with the respective source institutions; however, the same type of data is available and was prepared in a consistent manner for the full study period covered by the analyses (2018–2024).pl_PL
dc.description.sponsorshipThe data were collected within the framework of the National Science Centre (NCN) research projects 2023/49/B/HS4/01406 and 2024/53/B/HS4/00389.pl_PL
dc.language.isoplpl_PL
dc.rightsCC0 1.0 uniwersalna*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectcar transportpl_PL
dc.subjectpublic transportpl_PL
dc.subjectCovid-19pl_PL
dc.subjectRzeszówpl_PL
dc.titleSource data for the publication Changes in car-bus mobility in the context of the pandemic and the war in Ukraine in Rzeszów, Poland (2018–2024) (dataset)pl_PL
dc.typeDatasetpl_PL
dc.contributor.authorAffiliationUniversity of Lodz, Faculty of Geographical Sciences, Institute of the Built Environment and Spatial Policypl_PL
dc.contributor.authorEmailszymon.wisniewski@geo.uni.lodz.plpl_PL
dc.disciplinegeografia społeczno-ekonomiczna i gospodarka przestrzennapl_PL


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