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44 results for “Mobility as a Service”
Mobile Service Robots Crash Testing with Pedestrians: Safety Assessment with Child and Adult Dummies
<p>Data published with the manuscript: “<em>Estimating risks posed by personal mobility devices and service robots to pedestrians: comparative crash testing of adult versus child dummies</em>”. 2021 (Paez-Granados & Billard, 2021)<br> <strong>Summary:</strong></p> <p>This dataset contains injury measures during collisions between a mobile service robot - Qolo - (Paez-Granados, et al, 2018) and pedestrian dummies: male adult Hybrid-III (H3) and child model 3-years-old (Q3). We present multiple collision scenarios for the assessment of pedestrian safety, considering possible impacts at the legs for adult pedestrians, and legs, chest and head for children. In these tests, we followed known methods of safety analysis used in car crash testing and used a standing wheelchair robot "Qolo" as a representative system of mobile service robots, such as delivery bots (robot without occupant), person carrier robots, autonomous wheelchairs, standing mobility vehicles, and other transport robots expected to operate in pedestrian and public areas.</p> <p>The robot was equipped with an experimental front structure allowing different bumper heights and measurement of reaction forces. On the other hand, the human dummies were equipped with standard instrumentation calibrated in accordance with SAE J211-1 for impact tests, thus, the child dummy, Q3 provided head accelerations, neck forces and moments, chest deflections, and accelerations; and pelvis accelerations. The dummy H3 provided forces and moments at the tibia and femur, and accelerations at the pelvis, chest, and head. You will find scripts to read and plot the data, as well as, analysis of the injury risk based on standard crash testing metrics: Head Injury Criteria (HIC-15), head acceleration (a_3ms), Neck Injury (Nij), Chest deflection (CD), and tibia injury (TI).</p> <p><strong>Instructions: </strong></p> <p><em>This dataset contains the following main files:</em></p> <ol> <li><strong><em>Data Description.pdf</em>: </strong>Highly recommended to read through this file for understanding the setup of the collected dataset, as well as, the submitted manuscript.</li> <li><em><strong>collision_test_rawdata.zip</strong>: </em>This file contains all the raw data for each sensor as mentioned in table 3, organized in independent subfolders as described in table 2.<em> ‘test_name’/01_values/’testName’_CFC1000.xlsx</em></li> <li><em><strong>collision_test_analysis.zip</strong>: </em>This file contains all the processed data for each sensor in order to apply known injury metrics (Nij, HIC15, acc_3ms, TI, CC, VCI), organized in independent subfolders as described in table 2.<em>‘test_name’/01_values/’testName’_Analysis_v2.xlsx --> </em>Dataset with filtered sensor data accordingly to SAEJ21.</li> <li><em><strong>collision_data_matlab_structure.zip</strong>:</em><em> Matlab containers with all data - also available as .mat files for easy reading from Code Ocean capsule.</em></li> <li><em><em><strong>scripts-crash-test-service-robots.zip</strong>:</em> processing of the dataset is provided in this file with structure of data in Matlab containers and scripts for visualizing the data (see section III), further analysis scripts in the linked GitHub: <a href="https://github.com/epfl-lasa/crash-tests-service-robots">https://github.com/epfl-lasa/crash-tests-service-robots</a></em></li> </ol>
Synthetic mobile service traffic time series
<p>This dataset contains synthetic mobile service traffic time series used in the paper titled "kaNSaaS: Combining Deep Learning and Optimization for Practical Overbooking of Network Slices", presented at ACM MobiHoc 2023 in Washington, USA. It is composed of 20 time series representing the fluctuations of demands for diverse services categorized under 5G types, including enhanced Mobile Broadband (eMBB), ultra-Reliable Low Latency Communication (uRLLC), and massive Machine Type Communication (mMTC). The time series cover a period of XXX days, and were shown to yield similar properties as those observed in real-world traffic collected in a production mobile network.</p>
Online Repository of the Study "I want to RIDE my e-bicycle!": Supporting Developers Categorizing User Issues of a Mobility-as-a-Service Platform
<p><strong>Online Repository of the Study </strong><em>“I want to RIDE my e-bicycle!": Supporting Developers Categorizing User Issues of a Mobility-as-a-Service Platform</em></p> <p><strong>Introduction</strong></p> <p>In the Mobility-as-a-Service (MaaS) context, e-bikes are important and environmental-friendly transportation resources providing flexibility, time and cost savings, and reducing traffic congestion. Additional to user satisfaction and marketing advantages, the resolution of user-reported issues is regulated in many cities. In order to efficiently solve the issues, it is essential to quickly identify their types (e.g., software- or hardware-related?) to assign them to the responsible team. But for popular e-mobility services, the manual analysis of the reports is inefficient because of its tediousness, high time requirements, and error-proneness. </p> <p>Our empirical study, carried out in the context of a <em>Mobility as a Service </em>start-up company, proposes an approach for the automated identification of relevant concerns reported by users of e-bike services. The company has more than 20,000 private customers across seven different countries and dedicates considerable effort in analyzing user behavior. However, the current manual process of analyzing and triaging user-reported issues hinders MaaS-company’s ability to grow and expand its services. </p> <p>To help MaaS providers identify relevant user-reported issues, In the study, we (i) manually inspect about 3,000 user-reported issues received by the MaaS company; (ii) design a taxonomy modeling the types of relevant issues reported by users; and (iii) propose MaaS-RIDE, an approach to automatically classify the user-reported issues according to the categories of the devised taxonomy. </p> <p>Our results demonstrate that MaaS-RIDE is able to accurately (F-measure ≥ 93%) identify software and hardware user-reported issues. This result is critical for e-bike sharing companies to address such issues in an agile way and achieve the required user satisfaction.</p> <p><strong>Dataset Overview</strong></p> <p>The dataset is composed of the following different sorts of data: </p> <ul> <li> “<em>Data_and_preprocessing</em>” folder <ul> <li>o the user-reported issues data</li> <li>o the user-reported issues data processed as Bag of Words for Machine Learning training. <ul> <li>For this look at the sub-folder “<em>input_data_for_ML</em>” and the following matrices: <ul> <li><em>tf-idf-matrix-of-comment_finals_with_oracle_info_low_level.csv</em></li> <li><em>tf-idf-matrix-of-comment_finals_with_oracle_info.csv</em></li> </ul> </li> <li>Moreover, a sample of selected issues was reported in the replication package: <ul> <li>see file “<em>randomSamples.csv</em>” (due to a non-disclosure agreement with our industrial partner, we are unauthorized to share the whole raw user reports used in our experiments)</li> <li> “RQ1” folder: Types of E-bikes User-reported Issues</li> </ul> </li> </ul> </li> <li> the resulting taxonomy after the analysis of the issues</li> <li> “RQ2” folder: Classifying E-bikes Issue types</li> <li> the trained models </li> <li> the results of the models</li> </ul> </li> </ul> <p>The following sections describe more in detail what each of those folders and files contain.</p> <p><strong>“Data_and_preprocessing” folder</strong></p> <ul> <li><strong>User-reported issues subset.</strong></li> </ul> <p>In an industrial setting, due to privacy reasons, we disclose only an example subset of the user-reported issues, this information is in the file <em>randomSamples.csv</em>.</p> <p>The <em>randomSamples.csv </em>a subset that was generated randomly adding 20 examples using a stratified sampling from the High-level categories and 20 from the Low-level categories. This subset is not exhaustive but serves the purpose of showing the reviewers the kind of issues that this particular industrial set is confronted with. The file contains:</p> <ul> <li> <ul> <li> the Id of the user report; </li> <li> the column "comment_final"<strong> </strong>contains the issue text after the replacement of information that needed anonymization (e.g., vehicle-plates, personal names, addresses and timestamps); </li> <li> the column "High_level_category" contains the selected category from the 5 first level categories of the presented <em>Three-level taxonomy of e-bike user reported issues</em>; </li> <li>• the columns ‘Low_level_category" and "Fine_grained_topic" contain the assigned, if existing, respective category. </li> </ul> </li> <li><strong>Bag of Words Term by Document matrix.</strong></li> </ul> <p>An important input for training the ML models is the Bag of Words representation generated after processing the 2,989 manually-labeled user issues. The result of this process is a Term-by-Document matrix. We share this matrix in the files in the sub-folder <em>input_data_for_ML </em>where they are labeled for High- and Low-level categories. </p> <p>In the <em>tf-idf-matrix-of-comment_finals_with_oracle_info.csv</em> and <em>tf-idf-matrix-of-comment_finals_with_oracle_info_low_level.csv</em> files, the first column refers to the issue “Id”, the last column “oracle” is the labeled category, the rest of the columns represent the terms contained in the 2,989 user-reported issues and in each row the weight of the i−𝑡ℎ term contained in the j−𝑡ℎ user issue by using the tf-idf score.</p> <p><strong>“RQ1” folder</strong></p> <ul> <li><strong>“Three-level taxonomy of e-bike user-reported issues.pdf<em>” file</em></strong></li> </ul> <p>The taxonomy derives from the manual analysis of the 2,989 user issues. We found that a three-level taxonomy provides significant granularity to the MaaS-company. The taxonomy encompasses 5 High-level categories, 16 Low-level categories, and 15 Low-level subcategories of e-bike user-reported issues. The file <em>Three-level taxonomy of e-bike user-reported issues.pdf</em> presents the taxonomy categories and in the columns “Nr.” and “%” it shows the number of occurrences within the analyzed dataset, and the corresponding percentages.</p> <p><strong>“RQ2” folder</strong></p> <ul> <li><strong>“Trained Models” folder</strong></li> </ul> <p>We provide the trained machine and deep learning models in the sub-folder <em>ML_DL_models</em>. Our approach experimented with classic machine learning models based on the Bag-of-Words approach using SVM, on Word Embeddings using FastText, and Language models leveraging BERT. The SVM and BERT models were trained using the open source low-code data analytics platform KNIME and were used to classify issues corresponding to the first and second levels of the taxonomy from the “RQ1” folder. A 10-fold cross validation strategy was used to assess the classification performance. </p> <p>The fastText model was trained by using default values of parameters (https://fasttext.cc/docs/en/options.html) and a 10-fold cross-validation strategy. With fastText, we classified issues corresponding only to the first level of the taxonomy from “RQ1” folder, since fastText is more effective when more data points are available in the training set (i.e., lower levels in the taxonomy have fewer well-represented issue types).</p> <ul> <li><strong>“Model results” folder</strong></li> </ul> <p>In the sub-folder model_results we provide the tables summarizing the results of using the proposed MaaS-RIDE approach, with which we automatically identify and categorize user-reported issues according to the High-level and Low-level categories of the taxonomy devised in RQ1, which are relevant for the MaaS-company. </p>
The Cloud that Runs the Mobile Internet: A Measurement Study of Mobile Cloud Services
<p>In order to assess the performance of domains hosted in Cloud Service Providers (CSP), we run a dedicated measurements campaign on the MONROE measurements platform.</p> <p>For this study we use nodes in 4 countries and the Mobile Network Operators (MNO) over which we measure are:</p> <ul> <li>Norway: Telenor, Telia, <strong>Telia (SE)</strong></li> <li>Italy: Vodafone, Wind, TIM</li> <li>Spain: Yoigo, Orange, <strong>Vodafone (IT)</strong></li> <li>Sweden: Telia, Telenor, 3</li> </ul> <p>* MNOs written in bold letters are SIM cards that are roaming internationally (home country code in brackets).</p> <p>We benefit from MONROE’s openness and capabilities to run long-lived active measurements in realistic but controlled scenarios in commercial MNOs.</p> <p>Namely:</p> <ul> <li><strong>DNS test:</strong> We run DNS lookups for each target mobile domain towards: 1) the default MNO DNS resolver, 2) Google's public resolver and 3) OpenDNS's public resolver. This will allow us to compare the quality of the responses and identify possible DNS-level inefficiencies during the replica selection. All our DNS queries include the EDNS edns-client-subnet flag as it may be used by CSPs like EdgeCast and Amazon services to locate the end user and improve the quality of replica selection. If we do not receive a reply, as it occurs for Vodafone (IT) possibly due to the presence of an in-path DNS proxy filtering those requests, we repeat the request without the flag.</li> <li><strong>Traffic performance test:</strong> We measure the TCP connection and TLS session establishment time, if applicable, towards the resolved IP addresses. We repeat the same experiment over TCP ports 80 and 443, in case the MNO performs traffic shaping or deploys TCP-splitting proxies. The TCP connection, as well as the TLS session establishment are triggered by an HTTP GET request for the favicon.ico object. The presence of the object in the server is not relevant as the handshakes are triggered regardless of its existence.</li> <li><strong>Network topology test: </strong>For each resolved IP address, we run UDP traceroutes to study CSP-MNO peering and topological relationships. We also run pings towards the same IP address.</li> </ul> <p>We also log all the related metadata that are provided by the platform.</p> <p>The schema of the database can be found at:</p> <p>https://github.com/FoivosMichelinakis/cloudmap/blob/master/supportingFiles/parsers/cloudmap.sql</p> <p>The container code used on the MONROE nodes to generate this dataset can be found at:</p> <p>https://github.com/FoivosMichelinakis/cloudmap/</p> <p> </p> <p>We run the aforementioned experiments in isolation from other experiments continuously over 4 weeks, from April 5, 2017 until May 6, 2017.</p> <p>The combined results of the three tests for a given FQDN produce a "sample". The measurements are continuously run and the periodicity of the same sample varies between 4 and 24 hours. Thus, each sample has several instances across time of day and various radio conditions.</p>
Analysing the intra and interregional components of spatial accessibility gravity model to capture the level of equity in the distribution of hospital services: does they influence patient mobility?
<p>aggregated_data_age55+.csv and distance_matrix_age55+.csv have been included in the second version of the dataset as the reference population is limited to resident with 55 years old or more.</p>
Barriers & Facilitators of People With Disabilities in Accepting & Adopting Autonomous Shared Mobility Services (Project A5)
<p>Enclosed you will find the data collected during our STRIDE Phase II Extension research project (A5) and a data dictionary.</p>
Project F2 - Discovering Potential Market for the Integration of Public Transportation and Emerging Shared-Mobility Services
<p>This dataset compiles the results of ridesharing trajectory data aggregation, the analysis result of the transit and ridesharing trip data, and raw data of transit station coordinates and schedules in Chengdu, China. This is part of STRIDE project F2 titled "Discovering Potential Market for the Integration of Public Transportation and Emerging Shared-Mobility Services."</p>
Black American women's attitudes toward seeking mental health services and use of mobile technology to support the management of anxiety
<p><strong>Objective</strong>: This study aimed to understand Black American women's attitudes toward seeking mental health services and using mobile technology to receive support for managing anxiety.</p> <p><strong>Methods</strong>: A self-administered web-based questionnaire was launched in October 2019 and closed in January 2020. Women who identify as Black/African American were eligible to participate. The survey consisted of approximately 70 questions and covered topics such as attitudes toward seeking professional psychological help, acceptability of using a mobile phone to receive mental health care, and screening for anxiety.</p> <p><strong>Results</strong>: The findings of the study (N=395) showed that younger Black women were more likely to have greater severity of anxiety than their older counterparts. Respondents were most comfortable with the use of a voice call or video call to communicate with a professional to receive support to manage anxiety in comparison to text messaging or mobile app. Younger age, higher income, and greater scores for psychological openness and help-seeking propensity increased the odds of indicating agreement with using mobile technology to communicate with a professional. Black women in the South region of the U.S. had twice the odds of agreeing to the use of mobile apps than women in the Midwest and Northeast regions.</p> <p><strong>Discussion</strong>: Black American women, in general, have favorable views toward the use of mobile technology to receive support to manage anxiety.</p> <p><strong>Conclusion</strong>: Preferences and cultural appropriateness of resources should be assessed on an individual basis to increase the likelihood of adoption of and engagement with digital mental health interventions for management of anxiety. </p>
INTEGRA: A Vanguard Study of Health Service Delivery in a Mobile Health Delivery Unit
ClinicalTrials.gov study NCT04804072. IPD Sharing: NO. Countries: 1. Publications: 38.
Attitudes toward seeking mental health services and use of mobile technology survey
Open the record for dataset details and reuse information.
Data from: Modelling mobile agent-based ecosystem services using kernel weighted predictors
1. Agriculture benefits from ecosystem services provided by mobile agents, such as biological pest control by natural enemies and pollination by bees. However, methods that can generate spatially explicit predictions and maps of these ecosystem services based on empirical data are still scarce. 2. Here we propose a generic statistical model to derive kernel functions to characterize the spatial distribution of ecosystem services provided by mobile agents. The model is similar in spirit to a generalized linear model, and uses data of landscape composition and ecosystem services assessed at target sites to estimate parameters of the kernel. The approach is tested in a simulation study and illustrated by an empirical case study on parasitism rates of the diamondback moth Plutella xylostella. 3. The simulation study shows that the scale parameter of the exponential power kernel can be estimated with limited bias, whereas estimation of the shape parameter is difficult. For the case study the model provides biologically relevant estimates for the kernel associated with parasitism of Plutella xylostella. These estimates can be used to generate ecosystem service maps for existing or planned landscapes. The case study reveals that predictions can be sensitive to the parameter values for the width and shape of the kernel, and to the link function used in the statistical model. 4. In the last two decades numerous empirical studies assessed ecosystem services at target sites and related these to the surrounding landscape. Our method can take advantage of these data by estimating underlying kernels that can be used to map the spatial distribution of ecosystem services. However, empirical data that can discriminate between alternative kernel shapes remain critical.
Dataset The Importance of Interface Design and Security Awareness in Improving Service Quality and Its Impact on Digital Customer Loyalty of Smart Mobile Banking
Open the record for dataset details and reuse information.
Mobile Outreach Screening of High-Risk Human Papillomavirus Infection in Women with Limited Access to Health Services in Seine-Saint-Denis (greater Paris Area, France)
ClinicalTrials.gov study NCT06656611. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Impact of Mobile Phone Texting Service to Support Weight Loss
ClinicalTrials.gov study NCT02985307. IPD Sharing: YES. Countries: 1. Publications: 1.
Assessing the Impact of Mobile Phone Technology to Improve Health Nutrition and Population (HNP) Service Utilization in Rural Bangladesh Through Pilot Intervention
ClinicalTrials.gov study NCT03189004. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Feasibility of Mobile and Technology Assisted Aftercare Services for Crisis Stabilization Units
ClinicalTrials.gov study NCT04899934. IPD Sharing: NO. Countries: 1. Publications: 1.
30 Day Mobile App Programs for Stress Management in Customer Service Representatives
ClinicalTrials.gov study NCT03803865. IPD Sharing: YES. Countries: 1. Publications: 10.
Mobile-based Social Services Screening and Referral Tool in an Pediatric Emergency Department
ClinicalTrials.gov study NCT03562910. IPD Sharing: NO. Countries: 1. Publications: 3.
Pain Management in Primary Intervention of the Mobile Emergency and Resuscitation Service
ClinicalTrials.gov study NCT05791253. IPD Sharing: NO. Countries: 1. Publications: 4.
Evaluate Efficacy of Respiratory Rehabilitation Personalized Mobile Services for Respiratory Diseases
ClinicalTrials.gov study NCT03432117. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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DANDI Archive for NWB datasets
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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.