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51 results for “Public Transport”
FESOM model data and particle tracking data used in publication 'Cross-shelf transport of Barents Sea dense water as a sink for CO2 in the Arctic Ocean'
<p>FESOM model data and particle tracking data used in the paper 'Cross-shelf transport of Barents Sea dense water as a sink for CO2 in the Arctic Ocean" by Andreas Rogge at al.</p> <p>1) FESOM velocity fields averaged over the top 200 m water depth, averaged over the time period 2015-2018.</p> <p>2) FESOM transect at 95°E in the Arctic Ocean (temperature, salinity and velocity), averaged over the time period 2015-2018.</p> <p>3a) Particle back-tracking data based on daily FESOM velocity fields in netcdf format. Particles were released at 95°E every 14 days during the year 2018 and tracked until they reached the surface. Three different constant sinking velocities were used, representative for small and large non-ballasted particles and small ballasted particles.</p> <p>3b) Distribution of particles at the surface for the experiments with three different sinking velocities as mat files. </p>
E-scooters: competition with shared bicycles and relationship to public transport (processed datasets)
<p>Processed datasets for article "E-scooters: competition with shared bicycles and relationship to public transport" by Łukasz Nawaro (University of Warsaw, Faculty of Economic Sciences).</p>
Development of the multi-dimensional Mobility Divide Index as a methodology to assess the accessibility level of public transport systems
<p>This paper presents the development of a multi-dimensional Mobility Divide Index (MDI) for assessing the accessibility of public transport developed using a co-design approach, directly involving end-users in the index design process. The index measures the gap that persons with disabilities feel they need to over-come to use public transport in the same way non-disabled citizens do. The MDI covers six accessibility-related dimensions: 1) safety, 2) convenience, 3) comfort, 4) affordability, 5) travel time, 6) autonomy. The method paper describes the step-by-step approach to create the MDI as a set of indicators to be rated by people with different access needs to a) provide evidence of the main criticalities to be addressed through the design and implementation of new inclusive mobility solutions, b) guide the design of new inclusive mobility solutions and measure their impacts and c) inform the transport sector encouraging positive changes in transport by providing recommendations for policy-making, new directions for service innovation, improvements and practical advice or highlighting investment priorities to pave the way for a more inclusive mobility. We present our findings in ways that can inform universal design and provide actionable information to researchers, policymakers, transport and urban planners, operators and stakeholders’ representatives to promote inclusive and equitable mobility solutions for all. Finally, we suggest follow up research and innovation, as well as recommendations for its uptake and utilisation in the pursuit of European accessibility standards and requirements for products and services in the mobility sector. </p>
Data from: The multilayer temporal network of public transport in Great Britain
Despite the widespread availability of information concerning public transport coming from different sources, it is extremely hard to have a complete picture, in particular at a national scale. Here, we integrate timetable data obtained from the United Kingdom open-data program together with timetables of domestic flights, and obtain a comprehensive snapshot of the temporal characteristics of the whole UK public transport system for a week in October 2010. In order to focus on multi-modal aspects of the system, we use a coarse graining procedure and define explicitly the coupling between different transport modes such as connections at airports, ferry docks, rail, metro, coach and bus stations. The resulting weighted, directed, temporal and multilayer network is provided in simple, commonly used formats, ensuring easy access and the possibility of a straightforward use of old or specifically developed methods on this new and extensive dataset.
public-transport-structure-analysis-data
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Constellation analysis of automated local public transport shuttles in the north western development area of Berlin
<h2><strong><span>Konstellationsanalyse: Autonome ÖPNV-Shuttles im Entwicklungsband Nordwest </span></strong></h2> <p>Das Dokument beinhalten stellt eine deutschsprachige Ergebnisdokumentation einer Konstellationsanalyse aus dem Projekt „NOWEL4 – Berliner NordWestraum Level 4" dar . Die Konstellationsanalyse ist ein Brückenkonzept für inter- und transdisziplinäre Zusammenarbeit, das u.a. in der Nachhaltigkeits- und Technikforschung eingesetzt werden kann. Die Ergebnisse zeigen eine gegenwärtige Konstellation und eine Zielkonstellation der Einbindung autonomer Shuttles in den ÖPNV im Berliner Entwicklungsband NordWest. </p> <h2> </h2> <p> </p>
Supplementary materials for the article "Encouraging a sustainable adoption of autonomous vehicles for public transport in Belgium: citizen acceptance, business mod-els and policy aspects"
<p>Supplementary materials for the article "Encouraging a sustainable adoption of autonomous vehicles for public transport in Belgium: citizen acceptance, business mod-els and policy aspects". It includes:</p> <p>- The dataset excluding data subject to confidentiality due to privacy reasons</p> <p>- The consent form distributed to survey participants</p> <p>- The survey used in the study</p>
Risk perception and travel satisfaction associated with the use of public transport in the time of COVID-19. The case of Turin, Italy
<p>Dataset created from the data of a survey about risk perception and travel satisfaction associated with the use of PT in the time of Covid-19 in Turin metropolitan area (Italy).</p>
A collection of public transport network data sets for 25 cities
<p>This dataset describes the public transport networks of 25 cities across the world in multiple easy-to-use data formats. These data formats include network edge lists, temporal network event lists, SQLite databases, GeoJSON files, and General Transit Feed Specification (GTFS) compatible ZIP-files.<br> <br> The source data for creating these networks has been published by public transport agencies according to the GTFS data format. To produce the network data extracts for each city, the original data have been curated for errors, filtered spatially and temporally and augmented with walking distances between public transport stops using data from OpenStreetMap. <br> <br> Cities included in this dataset version: Adelaide, Belfast, Berlin, Bordeaux, Brisbane, Canberra, Detroit, Dublin, Grenoble, Helsinki, Kuopio, Lisbon, Luxembourg, Melbourne, Nantes, Palermo, Paris, Prague, Rennes, Rome, Sydney, Toulouse, Turku, Venice, and Winnipeg.</p> <p>Contrary to the version 1.0 of this data set, this version (1.2) does not include the cities of Antofagasta and Athens, for which non-commercial usage of the data is not allowed.<br> <br> Contrary to previous versions of the data set (1.0 and 1.2), in this version (1.2) the temporal filtering of the data has been slightly adapted, so that the daily and weekly data extracts cover all trips departing between from 03 AM on Monday to 03 AM on Tuesday (daily extract) or 03 AM of the Monday next week (weekly extract). Additionally, a temporal network extract covering a full week of operations has been added for each city.<br> <br> Documentation of the data can be found in the Data Descriptor article published in Scientific Data: http://doi.org/10.1038/sdata.2018.89 <br> When using this dataset, please cite also the above-mentioned paper.</p>
Supplementary material for publication "The combined second-echelon vehicle routing problem - Integrating last-mile deliveries into public transport"
<p>Supplementary material for publication "The combined second-echelon vehicle routing problem - Integrating last-mile deliveries into public transport".</p> <p> </p> <p>Includes bus times (provided by the data set goettingen from the scientific software toolbox LinTim (https://lintim.net/)), customer nodes, and distances and results for the algorithms for different vehicle speeds, capacities, number of second-echelon vehicles and number of customers.</p>
Data related to the publication "structure and transport properties of LiTFSI-based deep eutectic electrolytes from machine-learned interatomic potential simulations"
<p>Reference training and test datasets, trained ML potential models, and input scripts for the training (Allegro) and MD simulations (LAMMPS).</p>
Safe traveling in public transport amid COVID-19
<p>Several intense policies, such as mandatorily wearing masks and practicing social distancing, have been implemented in South Korea to prevent the spread of the novel coronavirus disease (COVID-19). In this study, we analyzed and measured the impact of the aforementioned policies by calculating the degree of infection exposure in public transportation. Specifically, we simulated how passengers encounter and infect each other during their journeys in public transportation by tracking movements of passengers. The probabilities of exposure to infections in public transportation were compared via a combination of the aforementioned policies by using the SEIR model, a respiratory infectious disease diffusion model. We determined that the mandatorily wearing of masks exhibits similar effects to maintaining social distancing 2 m in preventing COVID-19. During peak hours, in the cases of mandatorily wearing and practicing social distancing with masks, the reduction in infection rates corresponded to 93.5% and 98.1%, respectively.</p>
Code & Data for Moving the 15-minute city beyond the urban core: the role of accessibility and public transport in The Netherlands
<p>Code & Data for Moving the 15-minute city beyond the urban core: the role of accessibility and public transport in The Netherlands</p>
Raw data related to "Tröndle et al (2023): Public preferences for phasing-out fossil fuels in the German building and transport sectors"
<p>Raw survey data related to "Tröndle et al (2023): Public preferences for phasing-out fossil fuels in the German building and transport sectors".</p>
Safe traveling in public transport amid COVID-19
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Data from: The multilayer temporal network of public transport in Great Britain
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DISTINCTIVE FEATURES OF THE INSTRUCTIONS FOR USING PUBLIC TRANSPORT IN ENGLISH AND UZBEK AND THEIR TRANSLATION
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Usage Requirements on Recommender Systems for a Web Platform for Continuing Education in Public Transport in Germany
<p>The dataset contains usage requirements on recommender systems for a web platform for continuing education in public transport in Germany. The underlying data was collected in 28 stakeholder interviews. </p>
urbisphere_gb-london_UR-3: Transport database derived from UK road networks, OSM, and TfL public transport timetables, for modelling in London, UK
<h2><strong>Files included in this archive</strong></h2> <p><strong>Documentation</strong></p> <ul> <li> urbisphere_London_transport_database.pdf</li> </ul> <p><strong>JSON Database files</strong> (JSON: <a href="https://www.loc.gov/preservation/digital/formats/fdd/fdd000381.shtml">https://www.loc.gov/preservation/digital/formats/fdd/fdd000381.shtml</a> (<em>last accessed: 30/3/2024</em>))</p> <ul> <li>driving_transport.json <ul> <li>Database of driving routes</li> </ul> </li> <li>cycling_transport.json <ul> <li>Database of cycling routes</li> </ul> </li> <li>walking_transport.json <ul> <li>Database of walking routes</li> </ul> </li> <li>public_transport.json <ul> <li>Database of public transport routes</li> </ul> </li> </ul> <p><strong>Python 3.9 Code</strong></p> <ul> <li>London_travel_dictionaries.py <ul> <li>to create databases</li> </ul> </li> <li>assign_speed_limits.py <ul> <li> to assign OSM speed limits to each road in GLA</li> </ul> </li> <li>reduce_sub_services.py <ul> <li> to group transport routes within the TfL timetables</li> </ul> </li> </ul> <h2>Data purpose</h2> <p>This dataset contains transport routes for walking, driving, cycling, and public transport (train, tube, and bus) within the Greater London (GLA), which can be used for simulations of human behaviour and movement, for example using agent-based models (e.g. Capel-Timms et al. 2021, McGrory et al. 2024b).</p> <h3><em>Associated publications</em></h3> <ul> <li>Hertwig et al. 2024b: urbisphere_presentations_UR-1: Modelling anthropogenic heat emissions from residential buildings-comparison between Berlin and London. EMS Annual Meeting 2023 [Poster]. Zenodo. https://doi.org/10.5281/zenodo.10889863</li> <li>Hertwig et al. 2024c: urbisphere_gb-london_UR-7: Gridded total road lengths by type for London, UK. urbisphere–London Data Release and Technical Documentation [Dataset].. Zenodo. https://doi.org/10.5281/zenodo.10889841</li> <li>McGrory et al. 2024a: urbisphere_presentations_UR-3: Dynamic Anthropogenic actiVities and feedback to Emissions (DAVE): An agent-based model for heat and exposure to other anthropogenic emissions. EMS Annual Meeting 2023 [Presentation].. Zenodo. https://doi.org/10.5281/zenodo.10889900</li> </ul> <div> <div> </div> <div> <p> </p> </div> </div>
Public transport travel time matrices for Great Britain (TTM 2023)
<h2><strong>Overview</strong></h2> <p>This dataset provides ready-to-use door-to-door public transport travel time estimates for each of the 2011 Census at the lower super output area (LSOA) and data zone (DZ) units (42,000 LSOA/DZ units in total) in Great Britain (GB) to every other reachable within 150 minutes during the morning peak for the year 2023 using. This information comprises an all-to-all travel time matrix (TTM) at the national level. The TTM are estimated for public transport, bicycle, and walking. Public transport estimates are estimated for two times of departure, specifically during the morning peak and at night. Altogether, these TTMs present a range of opportunities for researchers and practitioners, such as the development of accessibility measures, spatial connectivity, and the evaluation of public transport service changes throughout the day.</p> <p>A full data descriptor is available in 'technical_note.html' file as part of the records of this repository.</p> <h2>Data records</h2> <p>The TTM structure follows a row matrix format, where each row represents a unique origin-destination pair. The TTMs are offered in a set of sequentially named <code>.parquet</code> files (more information about Parquet format at: <a href="https://parquet.apache.org/">https://parquet.apache.org/</a>). The structure contains one directory for each mode, where ‘bike’, ‘pt’, and ‘walk’, correspond to bicycle, public transport, and walking, respectively.</p> <h3>Walking</h3> <p>The walking TTM contains 13.3 million rows and three columns. The table below offers a description of the columns.</p> <div> <span>Table 1: </span>Walking travel time matrix codebook. <table><tbody><tr> <th>Variable</th> <th>Type</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td>from_id</td> <td>nominal</td> <td>2011 LSOA/DZ geo-code of origin</td> </tr> <tr> <td>to_id</td> <td>nominal</td> <td>2011 LSOA/DZ geo-code of destination</td> </tr> <tr> <td>travel_time_p050</td> <td>numeric</td> <td>Travel time walking in minutes</td> </tr> </tbody> </table> </div> <h3>Bicycle</h3> <p>The bicycle TTM includes 40 million rows and four columns which are described in the table below.</p> <div> <span>Table 2: </span>Bicycle travel time matrix codebook. <table><tbody><tr> <th>Variable</th> <th>Type</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td>from_id</td> <td>nominal</td> <td>2011 LSOA/DZ geo-code of origin</td> </tr> <tr> <td>to_id</td> <td>nominal</td> <td>2011 LSOA/DZ geo-code of destination</td> </tr> <tr> <td>travel_time_p050</td> <td>numeric</td> <td>Travel time by bicycle in minutes</td> </tr> <tr> <td>travel_time_adj</td> <td>numeric</td> <td>Adjusted travel time by bicycle in minutes. This adds 5 minutes for locking to the unadjusted estimate.</td> </tr> </tbody> </table> </div> <h3>Public transport</h3> <p>The LSOA/DZ TTM consists of six columns and 265 million rows. The internal structure of the records is displayed in the table below:</p> <div> <span>Table 3: </span>Public transport LSOA/DZ travel time matrix codebook. <table><tbody><tr> <th>Variable</th> <th>Type</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td>from_id</td> <td>nominal</td> <td>2011 LSOA/DZ geo-code of origin</td> </tr> <tr> <td>to_id</td> <td>nominal</td> <td>2011 LSOA/DZ geo-code of destination</td> </tr> <tr> <td>travel_time_p025</td> <td>numeric</td> <td>25 travel time percentile by public transport in minutes</td> </tr> <tr> <td>travel_time_p050</td> <td>numeric</td> <td>50 travel time percentile by public transport in minutes</td> </tr> <tr> <td>travel_time_p075</td> <td>numeric</td> <td>75 travel time percentile by public transport in minutes</td> </tr> <tr> <td>time_of_day</td> <td>nominal</td> <td>A discrete value indicating the time of departure used. Levels: ‘am’ = 7 a.m.; ‘pm’ = 9 p.m.</td> </tr> </tbody> </table> </div> <p> </p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.