[go: up one dir, main page]

Qi et al., 2016 - Google Patents

Spatial-temporal congestion identification based on time series similarity considering missing data

Qi et al., 2016

View HTML
Document ID
3783974795543732862
Author
Qi H
Liu M
Wang D
Chen M
Publication year
Publication venue
Plos one

External Links

Snippet

Traffic congestion varies spatially and temporally. The observation of the formation, propagation and dispersion of network traffic congestion can lead to insights about the network performance, the bottleneck dynamics etc. While many researchers use the traffic …
Continue reading at journals.plos.org (HTML) (other versions)

Classifications

    • 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
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063—Operations research or analysis
    • G06Q10/0639—Performance analysis
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • 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
    • G06Q30/00—Commerce, e.g. shopping or e-commerce
    • G06Q30/02—Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • G06Q30/0202—Market predictions or demand forecasting
    • G06Q30/0204—Market segmentation
    • G06Q30/0205—Location or geographical consideration
    • 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
    • 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
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • 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
    • G06F17/5009—Computer-aided design using simulation
    • 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
    • G06F17/10—Complex mathematical operations
    • 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"
    • 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
    • G01C21/26—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
    • 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
    • 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
    • G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/01—Social networking

Similar Documents

Publication Publication Date Title
Li et al. Real-time GIS for smart cities
Fan et al. Using big GPS trajectory data analytics for vehicle miles traveled estimation
Pu et al. Evaluating the nonlinear correlation between vertical curve features and crash frequency on highways using random forests
Chen et al. Simulating urban form and energy consumption in the Pearl River Delta under different development strategies
Ai et al. Generation of constrained network Voronoi diagram using linear tessellation and expansion method
Asprone et al. Vehicular crowd-sensing: a parametric routing algorithm to increase spatio-temporal road network coverage
Aguero-Valverde Direct spatial correlation in crash frequency models: estimation of the effective range
Qi et al. Tracing road network bottleneck by data driven approach
Qi et al. Spatial-temporal congestion identification based on time series similarity considering missing data
Guler et al. Bicycle station and lane location selection using open source GIS technology
Furno et al. Graph-based ahead monitoring of vulnerabilities in large dynamic transportation networks
Zhu et al. Green routing fuel saving opportunity assessment: A case study using large-scale real-world travel data
Pei et al. Bvis: urban traffic visual analysis based on bus sparse trajectories
Galgamuwa et al. Bayesian spatial modeling to incorporate unmeasured information at road segment levels with the INLA approach: A methodological advancement of estimating crash modification factors
Xia et al. A parallel SP-DBSCAN algorithm on spark for waiting spot recommendation
Guo et al. Trend surface analysis of geographic flows
Tang et al. Automatic road network selection method considering functional semantic features of roads with graph convolutional networks
Faraji et al. Point2hex: Higher-order mobility flow data and resources
Lv et al. Analysis of factors influencing cycling count: A micro-level study using road segment count data in London
Wang et al. From rectangle to parallelogram: an area-weighted method to make time-space diagrams incorporate traffic waves
Iranmanesh et al. Mapping the relationship between traffic accidents, road network configuration, and urban land use
Bencekri et al. A planning support system for boosting bikeability in seoul
Acharya et al. Enriching OpenStreetMap network data for transportation applications: Insights into the impact of urban congestion on accessibility
Pei et al. Full Bayesian method for the development of speed models: Applications of GPS probe data
Yu Regional co-location pattern scoping on a street network considering distance decay effects of spatial interaction