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4 results for “pedestrian dynamics”

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zenodo44/100

Data-driven physics-based modeling of pedestrian dynamics - dataset: Pedestrian trajectories at Eindhoven train station

<p>Pedestrian trajectories measured at train station Eindhoven Centraal (the Netherlands) on platform 2 with acces to tracks 3 and 4.</p> <p>The dataset is partitioned in files containing 10 consecutive days each, recording 4 data fields:</p> <ul> <li><strong>time_ms:</strong> Passed time since start of the measurements. Unit: milliseconds.</li> <li><strong>object_identifier:</strong> unique id identifying an object.</li> <li><strong>x_position_mm:&nbsp;</strong>coordinates of the object along the x-axis at the given time. Unit: millimeters.</li> <li><strong>y_position_mm:</strong> coordinates of the object along the y-axis at the given time. Unit: millimeters.</li> </ul> <p>Each object resembles a pedestrian on the train platform recorded with 10 frames per second. We deliberately removed exact date and time information for privacy reasons (see additional note). The data set consists of 60 consecutive days starting at an unkown time between 00:00 AM and 01:00 AM of a random date between April 1st and May 1st 2022. An overhead image of the platform is included showing train track 3 in the bottom and train track 4 in the top of the image.</p> <p>The data set is supplemented to the paper <a title="Data-driven physics-based modeling of pedestrian dynamics" href="https://doi.org/10.48550/arXiv.2407.20794" target="_blank" rel="noopener">Data-driven physics-based modeling of pedestrian dynamics</a> and can be processed by the associated <a title="Software: Data-driven physics-based modeling of pedestrian dynamics" href="https://github.com/c-pouw/physics-based-pedestrian-modeling" target="_blank" rel="noopener">Python implementation</a> to create pedestrian models.&nbsp;</p>

opencc-by-4.0Sep 2024View details →
zenodo40/100

Data-driven physics-based modeling of pedestrian dynamics

<p>Python package to create physics-based pedestrian models from crowd measurements</p> <p>Github: <a href="https://github.com/c-pouw/physics-based-pedestrian-modeling">https://github.com/c-pouw/physics-based-pedestrian-modeling</a></p>

openbsd-3-clauseAug 2024View details →
zenodo40/100

Dense Crowd Dynamics and Pedestrian Trajectories: A Multiscale Field Study at the Fête des Lumières in Lyon

<p>We present one of the first comprehensive field datasets capturing dense pedestrian dynamics across multiple scales, ranging from macroscopic crowd flows over distances of several hundred meters to microscopic individual trajectories, including approximately 7,000 recorded trajectories.</p> <p>The dataset also includes a sample of GPS traces, statistics on contact and push interactions, as well as a catalog of non-standard crowd phenomena observed in video recordings. Data were collected during the 2022 Festival of Lights in Lyon, France, within the framework of the French-German <a href="We%20present%20one%20of%20the%20first%20comprehensive%20field%20datasets%20capturing%20dense%20pedestrian%20dynamics%20across%20multiple%20scales,%20ranging%20from%20macroscopic%20crowd%20flows%20over%20distances%20of%20several%20hundred%20meters%20to%20microscopic%20individual%20trajectories,%20including%20approximately%207,000%20recorded%20trajectories.%20%20The%20dataset%20also%20includes%20a%20sample%20of%20GPS%20traces,%20statistics%20on%20contact%20and%20push%20interactions,%20as%20well%20as%20a%20catalog%20of%20non-standard%20crowd%20phenomena%20observed%20in%20video%20recordings.%20Data%20were%20collected%20during%20the%202022%20Festival%20of%20Lights%20in%20Lyon,%20France,%20within%20the%20framework%20of%20the%20French-German%20MADRAS%20project,%20covering%20pedestrian%20densities%20up%20to%204%20individuals%20per%20square%20meter." target="_blank" rel="noopener">MADRAS project</a>, covering pedestrian densities up to 4 individuals per square meter.</p>

opencc-by-4.0Sep 2024View details →
zenodo4/100

Non-reciprocal crowd dynamics: investigating the impact of moving threats on pedestrian speed preferences

<p>The data used to support the results in our paper.</p>

restrictedMay 2023View details →

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