Nothing Special   »   [go: up one dir, main page]

Yokota et al., 2019 - Google Patents

Fast and robust map‐matching algorithm based on a global measure and dynamic programming for sparse probe data

Yokota et al., 2019

View PDF @Full View
Document ID
2293129242947197274
Author
Yokota T
Okude M
Sakamoto T
Kitahara R
Publication year
Publication venue
IET Intelligent Transport Systems

External Links

Snippet

The location data from positioning devices such as those utilising global navigation satellite system (GNSS) provides vital information for the probe‐car systems aiming at solving road‐ traffic problems. In the case of the Japanese Electronic Toll Collection System 2.0, a huge …
Continue reading at ietresearch.onlinelibrary.wiley.com (PDF) (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
    • 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
    • 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
    • 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
    • G06F17/50—Computer-aided design
    • 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

Similar Documents

Publication Publication Date Title
US20190279247A1 (en) Crowd-sourced mapping
Zheng et al. Probabilistic range queries for uncertain trajectories on road networks
CN100568293C (en) The method of GPS locating information and electronic map charging section coupling
EP2101148B1 (en) Method for map matching with guaranteed integrity
Ganti et al. Inferring human mobility patterns from taxicab location traces
Schweizer et al. Map‐matching algorithm applied to bicycle global positioning system traces in Bologna
Yokota et al. Fast and robust map‐matching algorithm based on a global measure and dynamic programming for sparse probe data
CN104567906A (en) Beidou-based urban road network vehicle path planning method and device
Li et al. A hybrid link‐node approach for finding shortest paths in road networks with turn restrictions
Li et al. Empirical study of travel time estimation and reliability
Yang et al. An enhanced weight-based topological map matching algorithm for intricate urban road network
Kong et al. A scenario-based map-matching algorithm for complex urban road network
Torre et al. Matching GPS traces to (possibly) incomplete map data: bridging map building and map matching
Wang et al. A Network‐Based Model of Passenger Transfer Flow between Bus and Metro: An Application to the Public Transport System of Beijing
Song et al. Hidden Markov model and driver path preference for floating car trajectory map matching
Musleh et al. QARTA: an ML-based system for accurate map services
Cao et al. Map matching for sparse automatic vehicle identification data
Huang et al. Mining crowdsourced trajectory and geo‐tagged data for spatial‐semantic road map construction
Lyu et al. Geometric quality assessment of trajectory‐generated VGI road networks based on the symmetric arc similarity
Berjisian et al. Evaluation of map‐matching algorithms for smartphone‐based active travel data
Wu et al. Online map-matching assisted by object-based classification of driving scenario
Xie et al. High-accuracy off-line map-matching of trajectory network division based on weight adaptation HMM
Kirono et al. Graph-based modeling and dijkstra algorithm for searching vehicle routes on highways
Szwoch Combining road network data from openstreetmap with an authoritative database
Dalla Torre et al. A map-matching algorithm dealing with sparse cellular fingerprint observations