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79 results for “pedestrians”
The effect of wheelchair users on the egress time of pedestrian crowds: a systematic literature review and meta-analysis
<p>This dataset provides supplementary input data for a systematic literature review and meta-analysis, examining the effects of mobility-impaired individuals, specifically wheelchair users, on pedestrian egress times. It includes the following variables:</p> <ul> <li><strong>short_trial_name</strong>: A unique identifier for each trial.</li> <li><strong>independent variables</strong>: Factors such as the number of attendees, bottleneck width, and mobility profiles (e.g., individuals with or without wheelchair usage).</li> <li><strong>left_shifted_time</strong>: Standardized start time for egress, adjusted for comparability across trials.</li> <li><strong>lower_left_no_of_people</strong>: The number of individuals who passed through the bottleneck at the standardized start time.</li> <li><strong>right_shifted_time</strong>: Standardized end time for egress.</li> <li><strong>right_left_no_of_people</strong>: The number of individuals who passed through the bottleneck by the standardized end time.</li> </ul>
Urban Transportation Infrastructure and Cyclist and Pedestrian Safety
<p>The goal of this project was to perform a comprehensive evaluation of crash causes and risk factors to identify the root causes of crashes involving bicyclists and pedestrians in San Antonio, TX. The research included the development of a database of bicycle and pedestrian crash reports in the target area, calculation of crash counts and rates, identifying road segments and intersections with highly concentrated bicycle and pedestrian crashes, and the development of effective safety countermeasures. Several variables and factors were analyzed, including driver characteristics such as age and gender, road-related factors, and environmental factors such as weather conditions and time of the day. Bivariate analysis and logistic regression were used to identify the most significant predictors of severe pedestrian/bicyclist crashes. Geospatial analysis was used to investigate crash frequency and severity. High-risk locations were identified through heat maps and hotspot analysis. The downtown area had the highest crash density, but crash severity hotspots were identified outside of the downtown area. The strongest predictors of severe injury include lighting condition, road class, road speed limit, traffic control, collision type, and the age and gender of the pedestrian/bicyclist. Fatal and incapacitating injury risk increased substantially when the pedestrian/bicyclist was at fault. Resource allocation to high-risk locations, a reduction in the speed limit, an upgrade of the lighting facilities in high pedestrian activity areas, educational campaigns for targeted audiences, the implementation of more crosswalks, pedestrian refuge islands, and raised medians, and the use of leading pedestrian/bicyclist interval and hybrid beacons are recommended.</p>
Pedestrian street crossing in difficult weather conditions (Pedestrian-DVS)
<p>Pedestrian-DVS is a dataset of video frames showing pedestrians crossing the street obtained from CARLA simulation environment.</p> <p>The dataset consists of two subsets, showing scenes in good and bad weather conditions. The good weather subset consists of 117 videos, while bad weather subset contains 81 videos. Each video consists of 900 frames.</p> <p>Standard RGB frames are paired with corresponding frames extracted from recordings of dynamic vision sensor. Each frame is labeled using the following format: <frame_number-label.format>. A frame is labeled as positive if, in a given frame, the pedestrian is crossing the street.</p> <p>For more information, please visit the repository with code used to analyze this dataset or contact the authors.</p> <p>Contact email:</p> <p>szmazurek@agh.edu.pl</p>
Wind Value: First Conference 2022, LCSA of a Pedestrian Bridge made from Wind Blades, Angie Nagle (Paul Leahy presented), Video
<p>Video of 12 mins 46 seconds, on Life Cycle Sustainability Assessment (LCSA) of a Pedestrian Bridge made from Discarded Wind Blades, written by Angie Nagle and presented by Paul Leahy, her supervisor.</p>
Sustainable and Equitable Financing for Pedestrian Infrastructure Maintenance
<p>Corresponding data set for Tran-SET Project No. 17PPUNM01. Abstract of the final report is stated below for reference:</p> <p>"In many communities, pedestrian infrastructure is discontinuous, inaccessible to those with physical disabilities, and poorly maintained. Correcting these problems would be a first step in providing infrastructure to achieve the active travel and related transportation goals of many communities. One nearly universal challenge to maintaining sidewalks in a state of good repair and addressing environmental justice concerns is an adequate, sustainable, and equitable source of funding. Municipal governments across the country maintain and repair their streets and roadways; however, most require residents to maintain and repair public sidewalks adjacent to their property. These policies are difficult to enforce and may be at least partly responsible for the poor condition of many sidewalks. They may also place a relatively high cost on low-income households. We evaluate three alternative options for financing the maintenance of public sidewalks in Albuquerque, New Mexico: increasing the gross receipts tax (GRT), the gasoline excise tax, or the property tax. These are broad-based taxes that many municipalities already levy to pay for public infrastructure, including streets. We conclude that any of the alternatives would perform better than policies that require adjacent property owners to maintain public sidewalks. They are generally less regressive, cost less on average, and would allow municipalities to more effectively manage sidewalk assets. The differences between the alternatives are relatively minor compared to their benefits. Additional considerations should include how the revenue from each tax may change over time."</p>
Pedestrian single file movement on stairway: Investigating the impact of stair construct on pedestrian ascent and descent fundamental diagram
<p>Pedestrian single-file movement on stairs</p>
Data from: Multiobjective optimization algorithm for accurate MADYMO reconstruction of vehicle-pedestrian accidents
<p>Uncertainty in reconstruction accuracy is a critical problem faced in the current traffic accident reconstruction process. The purpose of this study is to explore the use of an improved optimization algorithm combined with MAthematical DYnamic MOdels (MADYMO) multibody simulations and crash data to conduct accurate reconstructions of vehicle–pedestrian accidents. The performance of three commonly employed multiobjective optimization algorithms, including nondominated sorting genetic algorithm-II (NSGA-II), neighbourhood cultivation genetic algorithm (NCGA) and multiobjective particle swarm optimization (MOPSO) were compared and evaluated. The effects of the number of objective functions, the selection of different objective functions and the optimal number of iterations are also investigated. The present study indicated that NSGA-II had better convergence and generated more noninferior solutions and better final solutions than NCGA and MOPSO. And multibody simulations coupled with optimization algorithms can be used to accurately reconstruct vehicle-pedestrian collisions.</p>
Calibration of a pedestrian ingress model based on CCTV surveillance data using machine learning methods: data and code
<p>The package includes a dataset of trajectories obtained from a real-time pedestrian traffic detector at the vaccination centre and the code needed to validate the analyses described in the paper entitled Calibration of a pedestrian ingress model based on CCTV surveillance data using machine learning methods. Version 1.0.1 is a patch that corrects path to data.</p>
Using Virtual Reality to Train Children in Pedestrian Safety
ClinicalTrials.gov study NCT00850759. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Data from: Multiobjective optimization algorithm for accurate MADYMO reconstruction of vehicle-pedestrian accidents
Open the record for dataset details and reuse information.
Supplementary evaluation files for the paper: Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones
<p>This package contains evaluation supplementary files for the paper: <em>Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones</em> by Miroslav Opiela and František Galčík.</p> <p><strong>Contents: </strong></p> <ul> <li>ground_truth - real positions of checkpoints for given input files</li> <li>input - sensor measurements recordings with initial positions (also after floor transitions) and checkpoint labels </li> <li>maps - processed map models containing positions of points and connections (e.g., walls) in custom coordinate system. Reference to GNSS and map rotation is inducted in maps-meta.xml</li> <li>output - data processed by the localization system. JSON containing the applied method, its configuration, and all estimated positions. Errors for every folder are summarized in the csv file</li> <li>visualization - trajectories visualized for selected output files</li> <li>readme.txt - describes data formats used for particular files in this dataset and summarizes output files</li> </ul> <p><strong>Venues</strong></p> <p>Data are recorded in three buildings:</p> <ul> <li>codename: SA1, SA1_rotated - recorded by the author in the faculty building (Park Angelinum 9, 04001, Košice, Slovakia) using Lenovo tablet</li> <li>codename: AtlantisR0, AtlantisR-1, AtlantisR+1, AtlantisR+2 - the shopping mall Atlantis Le Centre (Boulevard Salvador Allende, 44800 Saint-Herblain, France). Dataset is from IPIN 2018 competition and loc_20180922_160206 is recorded by the author using Xiaomi Mi 5.</li> <li>codename: CNR_0, CNR_1, CNR_2 - the research institute building CNR (Via Giuseppe Moruzzi, 56127 Pisa, Italy). Dataset is from IPIN 2019 competition. </li> </ul> <p><strong>Used datasets</strong></p> <p>A subset of input data is derivated from available logfiles provided by organizers of IPIN 2018 and IPIN 2019 competitions:</p> <ul> <li>Jimenez, A.R.; Mendoza-Silva, G.M.; Ortiz, M.; Perez-Navarro, A.; Perul, J.; Seco, F.; Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2018 Competition Track 3 (Smartphone-based, off-site). <a href="http://dx.doi.org/10.5281/zenodo.2823964">http://dx.doi.org/10.5281/zenodo.2823964</a></li> <li>Jiménez, A. R.; Perez-Navarro, A.; Crivello, A.; Mendoza-Silva, G.; Ortiz, M.; Perul, J.; Seco, F. and Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2019 Competition Track 3 (Smartphone-based, off-site), Zenodo 2019. <a href="http://dx.doi.org/10.5281/zenodo.3606765">http://dx.doi.org/10.5281/zenodo.3606765</a> </li> </ul> <p><strong>Funding</strong></p> <p>The work was partially supported by the Slovak Grant Agency of the Ministry of Education and Academy of Science of the Slovak Republic under grant no. 1/0056/18 and by the Slovak Research and Development Agency under the contract no. APVV-15-0091.</p> <p><strong>Contact</strong></p> <p>For any further questions, please contact:</p> <p>Miroslav Opiela, miroslav.opiela@upjs.sk Institute of Computer Science, Faculty of Science, P. J. Šafárik University (UPJS), Košice, Slovakia</p>
Data from: Walking crowds on a shaky surface: stable walkers discover Millennium Bridge oscillations with and without pedestrian synchrony
Why did the London Millennium Bridge shake when there was a big enough crowd walking on it? What features of human walking dynamics when coupled to a shaky surface produce such shaking? Here, we use a simple biped model capable of walking stably in 3D to examine these questions. We simulate multiple such stable bipeds walking simultaneously on a bridge, showing that they naturally synchronize under certain conditions, but that synchronization is not required to shake the bridge. Under such shaking conditions, the simulated walkers increase their step-widths and expend more metabolic energy than when the bridge does not shake. We also find that such bipeds can walk stably on externally shaken treadmills, synchronizing with the treadmill motion for a range of oscillation amplitudes and frequencies, sometimes performing net positive work on the treadmill. Our simulations illustrate how interactions between (idealized) bipeds through the walking surface can produce emergent collective behavior that may not be exhibited by just a single biped.
Data from: Active and reactive behaviour in human mobility: the influence of attraction points on pedestrians
Human mobility is becoming an accessible field of study thanks to the progress and availability of tracking technologies as a common feature of smart phones. We describe an example of a scalable experiment exploiting these circumstances at a public, outdoor fair in Barcelona (Spain). Participants were tracked while wandering through an open space with activity stands attracting their attention. We develop a general modeling framework based on Langevin Dynamics, which allows us to test the influence of two distinct types of ingredients on mobility: reactive or context-dependent factors, modelled by means of a force field generated by attraction points in a given spatial configuration, and active or inherent factors, modelled from intrinsic movement patterns of the subjects. The additive and constructive framework model accounts for some observed features. Starting with the simplest model (purely random walkers) as a reference, we progressively introduce different ingredients such as persistence, memory, and perceptual landscape, aiming to untangle active and reactive contributions and quantify their respective relevance. The proposed approach may help in anticipating the spatial distribution of citizens in alternative scenarios and in improving the design of public events based on a facts-based approach.
Event-based vision datasets for pedestrian detection in urban scenarios
<p>Datasets supporting the Spiking Perception and processing for Intelligent Detection of pEdestrians on urban Roads (SPIDER) Project. The codebase is available on Gihub at <a href="https://github.com/th-nuernberg/spider">https://github.com/th-nuernberg/spider</a>.<br><br>In Project SPIDER, we propose a novel solution for urban road surveillance using event-based neuromorphic cameras (i.e. Dynamic Vision Sensors – DVS), neural algorithms, and embedded neuromorphic computing platforms (i.e. Brainchip Akida). The solution is described by rapid detection and identification of abnormal activities, typically describing roadside pedestrian and bicyclist dynamics.<br><br>In order to train the detection system, we decided to use DVS camera events. To also obtain ground truth information we added a traditional CMOS camera on the mount with a preset offset capturing the same field of view. The CMOS frames were only used as support in the labeling process and were not included in the training of the system, the testing, or the evaluation, respectively. We have chosen two locations with different properties of the road, the number of pedestrians and bicyclists, and overall different traffic properties. </p><p><strong>The SPIDER project is an award-winning edge solution for the </strong><a href="https://www.tinyml.org/event/tinyml-hackathon-2023-pedestrian-detection/"><strong>TinyML Vision Zero San Jose Hackathon</strong></a><strong>. The project placed 2nd among 29 teams in the world in the final. The final pitch is available at </strong><a href="https://www.youtube.com/watch?v=ZhBCtfalcOk&t=2872s">https://www.youtube.com/watch?v=ZhBCtfalcOk&t=2872s</a></p><p> </p><p><i>Dataset archive content:</i></p><p> </p><p><strong>Dataset location 1</strong></p><p>• 4 lanes (4 per direction) wide street</p><p>• Location: https://goo.gl/maps/JaYGwaTaBHj5H6SL9</p><p>• 50 kmh (urban) speed limit</p><p>• Near the university campus with Pedestrians (people walking) and bicyclists (people biking, scooting, rolling, etc.)</p><p>• Ideal Operating Environment<br> </p><p><br><strong>Dataset location 2</strong></p><p>• 8 lanes (4 per direction) wide street</p><p>• Location: https://goo.gl/maps/jar6AjysZiM2LP5S7</p><p>• 50 kmh (urban) speed limit</p><p>• Near the main train stations of the city and a location with Pedestrians (people walking, running, or jogging), and cyclists (people biking, scooting, rolling, etc.)<br><br> </p><p><strong>Dataset location 3</strong></p><p>• 6 lanes (3 per direction) wide street on the bridge</p><p>• Location: https://goo.gl/maps/SEEsmpgmLPcD8fG7A</p><p>• 50 kmh (urban) speed limit</p><p>• Near ring street of Munich and a location with Pedestrians (people walking, running, or jogging) and bicyclists (people biking, scooting, rolling, etc.)</p><p>• Night-time data acquisition <br><br> </p>
Parthenon pedestrian street
Section of parthenon pedestrian street. Athens Source: Objaverse 1.0 / Sketchfab
Uncertainty in Pedestrian Decision-Making in Urgent Scenarios Modulates Multi-Level Neural Hierarchies from Perception to Execution
<p><span>In urgent traffic scenarios, pedestrians exhibit decision-making uncertainty, significantly influencing safe interaction dynamics with automated vehicles. However, the inherent mechanisms of such decision behavior remain inadequately understood. To address this gap, we designed dynamic interactive stimulus experiments to replicate pedestrian-vehicle interactions in urgent scenarios, incorporating spatiotemporal pressure and introducing substantial penalties for decision failures. We employed multimodal data analysis, including behavioral data, electroencephalography (EEG) and eye-tracking data, to investigate the influence of urgency on uncertainty in decision-making and the underlying multi-level neural processes. Our findings demonstrate that as the urgency of the stimulus increases, humans adjust their decision objectives, resulting in an initial decrease followed by an increase in decision uncertainty when dealing with more urgent stimuli. Specifically, urgency augments top-down perceptual processes during the early perception stage. <span>Such a mechanism implies an enhanced dependence on prior experiences for perceptual </span></span><span><span><span>decision<span>-making in high-urgency situations. </span></span></span></span><span>While urgency accelerated motion preparation time during the decision-execution stage, it is noteworthy that the culmination of evidence accumulation (represented by the CPP peak) manifested later than the actual response. These results suggest that insufficient perceptual information and evidence accumulation may increase decision-making uncertainty. Our experimental study unveils a correlation between human decision-making uncertainty and scenario urgency, particularly within a defined urgency range. </span></p>
Data, codes, metadata and supplementary materials used for the research of crossing flows of pedestrians
<p>This is a collection of all the data (trajectory of participants in the experimental trials) that has been used for the research to study the formation of self-organising patterns in crossing flows of pedestrians. The experimental trials conformed to the declaration of Helsinki regarding the ethical approval to use human beings as subjects Here all the codes that has been used and the metadata derived using the codes are also presented in .csv format. The supplementary files are added in the latest version.</p>
Dataset for Eye image effect in the context of pedestrian safety: a French questionnaire study
<p>Dataset for the paper Eye image effect in the context of pedestrian safety: a French questionnaire study</p> <p>Introduction: Human behavior is therefore influenced by the presence of others, which scientists also call ‘the audience effect’. The use of social control to produce more cooperative behaviors may positively influence road use and safety. This study uses an online questionnaire to test how eyes images affect the behavior of pedestrians when crossing a road.</p> <p>Material and methods: Different eyes images of men, women and a child with different facial expressions -neutral, friendly and angry- were presented to participants who were asked what they would feel by looking at these images before crossing a signalized road. Participants completed a questionnaire of 20 questions about pedestrian behaviors (PBQ). The questionnaire was received by 1,447 French participants, 610 of whom answered the entire questionnaire. 71% of participants were women, and the mean age was 35±14 years.</p> <p>Results: Eye images give individuals the feeling they are being observed at 33%, feared at 5% and surprised at 26%, and thus seem to indicate mixed results about avoiding crossing at the red light. The expressions shown in the eyes are also an important factor: feelings of being observed increased by about 10-15% whilst feelings of being scared or inhibited increased by about 5% as the expression changed from neutral to friendly to angry. No link was found between the results of our questionnaire and those of the Pedestrian Behavior Questionnaire (PBQ).</p> <p>Conclusion: This study shows that the use of eye images could reduce illegal crossings by pedestrians, and is thus of key interest as a practical road safety tool. However, the effect is limited and how to increase this nudge effect needs further consideration.</p>
FollowMe - A Pedestrian Following Algorithm for Agricultural Logistic Robots (video results)
<p>Video result of submitted paper "FollowMe - A Pedestrian Following Algorithm for Agricultural Logistic Robots" in ICARSC confrence</p>
Pedestrian movement trajectories and collision avoidance strategies in interweaving pedestrian flow experiment
<p>The mechanisms of Collision Avoidance(CA) behaviors in interweaving pedestrian flow movements are important for pedestrian space planning and emergency management but not well understood yet. A series of controlled interweaving pedestrian flow experiments with different pedestrian flow densities are carried out to investigate the CA behaviors, especially CA strategy choices. This dataset consists the movement trajectory and the CA strategy choices information of the participants in the experiment and was provided as a supplementary material of a journal paper submitted to "Royal Society Open Science".</p> <p>All these experiments were conducted in an outdoor public square in the campus of Wuhan University of Technology. A total of 40 students aged 18-23 years participated these experiments. The experiment includes three "groups", representing low, medium and high density levels of interweaving pedestrian flow. Each group of experiment was repeated three times to increase the reliability of the observations. Therefore, there are totally 9 data files in this dataset, each file contains the data of one experiment.</p> <p>After the experiment, the PeTrack software was used to extract pedestrians' trajectories by identifying and tracking the coordinates of participants' heads in the video records. This dataset provides the extracted trajectories of all the pedestrians in each experiment. Trajectory of each RP is marked as Collision Avoidance Segment (CAS) and Normal Walking Segment (NWS). During the CAS, pedestrians have shown collision avoidance strategies. Four types of CA strategies, including "deceleration", "acceleration", "detour", and "stop" are manually identified in these experiments and marked in the dataset. More details of the data and the results of the study could be found in the associated journal paper. This dataset could be useful for researchers in this field.</p>
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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.