On the exploitation of GPS-based data for real-time visualisation of pedestrian dynamics in open environments
暂无分享,去创建一个
[1] Pawel Dabrowski,et al. Comparative analysis of positioning accuracy of GNSS receivers of Samsung Galaxy smartphones in marine dynamic measurements , 2019, Advances in Space Research.
[2] Osama Alfarraj,et al. HUAD: Hierarchical Urban Anomaly Detection Based on Spatio-Temporal Data , 2020, IEEE Access.
[3] Jan Brus,et al. Performance Testing on Marker Clustering and Heatmap Visualization Techniques: A Comparative Study on JavaScript Mapping Libraries , 2019, ISPRS Int. J. Geo Inf..
[4] Becca Leopkey,et al. Risk Management Issues in Large-scale Sporting Events: a Stakeholder Perspective , 2009 .
[5] Andrea Bonaccorsi,et al. Why Open Source Software Can Succeed , 2003 .
[6] K. Waga,et al. System for real time storage, retrieval and visualization of GPS tracks , 2012, 2012 16th International Conference on System Theory, Control and Computing (ICSTCC).
[7] Pasi Fränti,et al. Real Time Access to Multiple GPS Tracks , 2013, WEBIST.
[8] Graeme Hirst,et al. Utility of social media and crowd-intelligence data for pharmacovigilance: a scoping review , 2018, BMC Medical Informatics and Decision Making.
[9] Wei Liu,et al. Network-wide Crowd Flow Prediction of Sydney Trains via Customized Online Non-negative Matrix Factorization , 2018, CIKM.
[10] Hermie Hermens,et al. Human Behaviour Analysis through Smartphones , 2018, UCAmI.
[11] Neeta Nain,et al. Crowd Monitoring and Classification: A Survey , 2017 .
[12] Tarun Kulshrestha,et al. Real-Time Crowd Monitoring Using Seamless Indoor-Outdoor Localization , 2020, IEEE Transactions on Mobile Computing.
[13] Xiaoyun Zhao,et al. On processing GPS tracking data of spatio-temporal car movements: a case study , 2015, J. Locat. Based Serv..
[14] Amol P. Bhondekar,et al. A review on technological advancements in crowd management , 2018, J. Ambient Intell. Humaniz. Comput..
[15] Chris North,et al. Towards insight-driven sampling for big data visualisation , 2020, Behav. Inf. Technol..
[16] Honghua Xu,et al. Abnormal Behavior Detection Based on Spatio-Temporal Information Fusion for High Density Crowd , 2019 .
[17] Chabane Djeraba,et al. Real-time crowd motion analysis , 2008, 2008 19th International Conference on Pattern Recognition.
[18] Paul Lukowicz,et al. Inferring Crowd Conditions from Pedestrians' Location Traces for Real-Time Crowd Monitoring during City-Scale Mass Gatherings , 2012, 2012 IEEE 21st International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises.
[19] U. Rajendra Acharya,et al. Practical Automated Video Analytics for Crowd Monitoring and Counting , 2019, IEEE Access.
[20] Paul Lukowicz,et al. Capturing crowd dynamics at large scale events using participatory GPS-localization , 2014, 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP).
[21] Wanggen Wan,et al. Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play , 2018, ISPRS Int. J. Geo Inf..
[22] Kamarul Hawari Ghazali,et al. Crowd Behavior Monitoring using Self-Adaptive Social Force Model , 2019 .
[23] Pratiksha H. Shroff,et al. Critical rendering path optimizations to reduce the web page loading time , 2017, 2017 2nd International Conference for Convergence in Technology (I2CT).
[24] Baek-Young Choi,et al. ICE-MoCha: Intelligent Crowd Engineering using Mobility Characterization and Analytics , 2019, Sensors.
[25] Wanggen Wan,et al. Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis , 2019, ISPRS Int. J. Geo Inf..
[26] Kamiar Aminian,et al. Mobile Health Applications to Promote Active and Healthy Ageing , 2017, Sensors.
[27] Sajda Qureshi,et al. Creating a Better World with Information and Communication Technologies: Health Equity , 2016, Inf. Technol. Dev..
[28] Francisco Alayón Hernández,et al. System Proposal for Mass Transit Service Quality Control Based on GPS Data , 2017, Sensors.
[29] H. Yun,et al. Time–Space Movement of Festival Visitors in Rural Areas Using a Smart Phone Application , 2015 .
[30] Michal Šimeček,et al. Usability of Wi-Fi fingerprint approach for place departure recognition in travel surveys , 2020 .
[31] François Horlin,et al. Crowd Forecasting Based on WiFi Sensors and LSTM Neural Networks , 2020, IEEE Transactions on Instrumentation and Measurement.
[32] Pete Bettinger,et al. Smartphone GPS accuracy study in an urban environment , 2019, PloS one.
[33] Imran N. Junejo,et al. Social network model for crowd anomaly detection and localization , 2017, Pattern Recognit..
[34] Shaohua Wang,et al. Extraction and monitoring approach of dynamic urban commercial area using check-in data from Weibo , 2019, Sustainable Cities and Society.
[35] Licia Capra,et al. Urban Computing: Concepts, Methodologies, and Applications , 2014, TIST.
[36] Y. Li,et al. Effects of salt substitutes on blood pressure: a meta-analysis of randomized controlled trials. , 2014, The American journal of clinical nutrition.
[37] F. Ren,et al. Check-in behaviour and spatio-temporal vibrancy: An exploratory analysis in Shenzhen, China , 2018, Cities.
[38] Serge P. Hoogendoorn,et al. Monitoring the Number of Pedestrians in an Area: The Applicability of Counting Systems for Density State Estimation , 2018 .
[39] Stefan Stieglitz,et al. Social media analytics - Challenges in topic discovery, data collection, and data preparation , 2018, Int. J. Inf. Manag..
[40] Ramachandran Baskaran,et al. Automated human behavior analysis from surveillance videos: a survey , 2014, Artificial Intelligence Review.