Kang et al., 2021 - Google Patents
Bayesian path inference using sparse GPS samples with spatio-temporal constraintsKang et al., 2021
- Document ID
- 14871051047197476996
- Author
- Kang J
- Yan K
- Li Y
- Duan Z
- Duan P
- Huang B
- Publication year
- Publication venue
- IEEE Transactions on Intelligent Transportation Systems
External Links
Snippet
Path inference aims to reveal missing paths given a few number of GPS samples associated with a moving object by exploiting the topology of road network and statistical information of historical GPS trajectories, and plays a vital role in data preprocessing of location based …
- 238000005070 sampling 0 abstract description 78
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
- G01C21/34—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
- G01C21/26—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
- G01C21/3469—Fuel consumption; Energy use; Emission aspects
-
- 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
- G01C21/36—Input/output arrangements of navigation systems
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
- G08G1/096833—Systems involving transmission of navigation instructions to the vehicle where different aspects are considered when computing the route
- G08G1/096844—Systems involving transmission of navigation instructions to the vehicle where different aspects are considered when computing the route where the complete route is dynamically recomputed based on new data
-
- 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
- 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
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
- G06Q10/047—Optimisation of routes, e.g. "travelling salesman problem"
-
- 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
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Goh et al. | Online map-matching based on hidden markov model for real-time traffic sensing applications | |
Yuan et al. | Driving with knowledge from the physical world | |
Chen et al. | Reliable shortest path finding in stochastic networks with spatial correlated link travel times | |
Rahmani et al. | Path inference from sparse floating car data for urban networks | |
CN110942211A (en) | Prediction arrival time prediction method and device based on deep neural network | |
US11435202B2 (en) | Trajectory sampling using spatial familiarity | |
Ozdemir et al. | A hybrid HMM model for travel path inference with sparse GPS samples | |
Saki et al. | A practical guide to an open-source map-matching approach for big GPS data | |
US20230137263A1 (en) | Method and apparatus for generating structured trajectories from geospatial observations | |
Deng et al. | Estimating traffic delays and network speeds from lowfrequency GPS taxis traces for urban transport modelling | |
Bahuleyan et al. | Arterial path-level travel-time estimation using machine-learning techniques | |
Sun et al. | Road network metric learning for estimated time of arrival | |
Jiang et al. | From driving trajectories to driving paths: a survey on map-matching algorithms | |
Lee et al. | Development of reinforcement learning-based traffic predictive route guidance algorithm under uncertain traffic environment | |
Gupta et al. | Study of fuzzy logic and particle swarm methods in map matching algorithm | |
Yang et al. | Feature selection in conditional random fields for map matching of GPS trajectories | |
Jagadeesh et al. | Robust real-time route inference from sparse vehicle position data | |
Liang et al. | Online learning for accurate real-time map matching | |
Karagulian et al. | A simplified map-matching algorithm for floating car data | |
Kang et al. | Bayesian path inference using sparse GPS samples with spatio-temporal constraints | |
Laarabi et al. | Real-timefastest path algorithm using bidirectional point-to-point search on a fuzzy time-dependent transportation network | |
EP3617651B1 (en) | Use of a geographic database comprising lane level information for traffic parameter prediction | |
Azad | Smart travel time prediction model for urban traffic using long short-term memory network | |
Agrawal et al. | Favour prediction of Taxi services using real-time visualization | |
US20230135578A1 (en) | Method and apparatus for generating structured trajectories from geospatial observations |