[go: up one dir, main page]

Bouchelaghem et al., 2022 - Google Patents

User mobility dataset for 5G networks based on GPS geolocation

Bouchelaghem et al., 2022

Document ID
17534721440658830167
Author
Bouchelaghem S
Boudjelaba H
Omar M
Amad M
Publication year
Publication venue
2022 IEEE 27th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)

External Links

Snippet

Geolocation technology is the most exciting area of advancement in 5G, leveraging massive sources of accurate location data to provide users with effective location-positioning services and applications. As research on user mobility prediction is steadily growing in the context of …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in preceding groups
    • G01C21/26—Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network
    • G01C21/28—Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network with correlation of data from several navigational instruments
    • G01C21/30—Map- or contour-matching
    • G01C21/32—Structuring or formatting of map data
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in preceding groups
    • G01C21/26—Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
    • G01C21/3492—Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in preceding groups
    • G01C21/20—Instruments for performing navigational calculations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30241—Information retrieval; Database structures therefor; File system structures therefor in geographical information databases
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/20—Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30861—Retrieval from the Internet, e.g. browsers
    • G06F17/30864—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems
    • G06F17/3087—Spatially dependent indexing and retrieval, e.g. location dependent results to queries

Similar Documents

Publication Publication Date Title
Ghahramani et al. Urban sensing based on mobile phone data: Approaches, applications, and challenges
Zhao et al. Urban human mobility data mining: An overview
Hasnat et al. Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data
Wang et al. Estimating dynamic origin-destination data and travel demand using cell phone network data
Becker et al. Human mobility characterization from cellular network data
US20110208425A1 (en) Mining Correlation Between Locations Using Location History
Qian et al. Using mobile phone data to determine spatial correlations between tourism facilities
Peterson et al. Spatiotemporal variables to understand visitor travel patterns: A management-centric approach
John et al. Deriving incline values for street networks from voluntarily collected GPS traces
Liao et al. Big data‐enabled social sensing in spatial analysis: Potentials and pitfalls
Cao et al. Understanding metropolitan crowd mobility via mobile cellular accessing data
Cui et al. Social media and mobility landscape: Uncovering spatial patterns of urban human mobility with multi source data
Rahimi-Golkhandan et al. Assessing the impact of transportation diversity on postdisaster intraurban mobility
Alhazzani et al. Urban Attractors: Discovering patterns in regions of attraction in cities
Herder et al. Daily and weekly patterns in human mobility.
Bouchelaghem et al. User mobility dataset for 5G networks based on GPS geolocation
Gao et al. Distinguishing different types of city tourists through clustering and recursive logit models applied to Wi-Fi data
Bakkal et al. Modeling and querying trajectories using Neo4j spatial and TimeTree for carpool matching
Hadachi et al. A survey on the advancement of travel time estimation using mobile phone network data
Mucceli et al. On the regularity of human mobility
St Reconstructing trajectories from sparse call detail records
Butron-Revilla et al. Discovering urban mobility patterns and demand for uses of urban spaces from mobile phone data
Dash et al. CDR-To-MoVis: Developing a mobility visualization system from CDR data
Jerônimo et al. Using open data to analyze urban mobility from social networks
Verma et al. Applicability of Big Data for transportation planning and management