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

Li et al., 2006 - Google Patents

Assignment of seasonal factor categories to urban coverage count stations using a fuzzy decision tree

Li et al., 2006

Document ID
2401881139048456942
Author
Li M
Zhao F
Chow L
Publication year
Publication venue
Journal of Transportation Engineering

External Links

Snippet

Seasonal factor is an important parameter for converting coverage counts to annual average daily traffic (AADT). There have been many studies on establishing seasonal factor (SF) categories, but there is a limited understanding of how to assign SF groups to short-term …
Continue reading at ascelibrary.org (other versions)

Classifications

    • 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
    • G06F17/30386—Retrieval requests
    • G06F17/30424—Query processing
    • G06F17/30533—Other types of queries
    • 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
    • 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
    • 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
    • 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
    • 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
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • 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
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles

Similar Documents

Publication Publication Date Title
Jiang et al. Mining point-of-interest data from social networks for urban land use classification and disaggregation
Sabouri et al. Exploring the relationship between ride-sourcing services and vehicle ownership, using both inferential and machine learning approaches
CN113204718A (en) Vehicle track destination prediction method considering space-time semantics and driving state
Duddu et al. Principle of demographic gravitation to estimate annual average daily traffic: Comparison of statistical and neural network models
CN109214863B (en) Method for predicting urban house demand based on express delivery data
Hamad et al. Predicting freeway incident duration using machine learning
Dia 1 et al. Evaluation of discrete choice and neural network approaches for modelling driver compliance with traffic information
Huang et al. Bi-level GA and GIS for multi-objective TSP route planning
Tay et al. Bayesian optimization techniques for high-dimensional simulation-based transportation problems
Tariq et al. Combining machine learning and fuzzy rule-based system in automating signal timing experts’ decisions during non-recurrent congestion
Diana et al. A multimodal perspective in the study of car sharing switching intentions
Li et al. Assignment of seasonal factor categories to urban coverage count stations using a fuzzy decision tree
Li et al. Weighted dynamic time warping for traffic flow clustering
Islam Estimation of annual average daily traffic (AADT) and missing hourly volume using artificial intelligence
Chiou et al. A novel method to predict traffic features based on rolling self-structured traffic patterns
Das Traffic volume prediction on low-volume roadways: a Cubist approach
CN112883133B (en) Flow prediction method based on time sequence data and functional evolution data
Lin et al. Insights into Travel Pattern Analysis and Demand Prediction: A Data-Driven Approach in Bike-Sharing Systems
Zagow et al. Identifying urban, transportation, and socioeconomic characteristics across US zip codes affecting CO2 emissions: A decision tree analysis
Zhong et al. Logic-Driven Traffic Big Data Analytics
Niu et al. Highway Temporal‐Spatial Traffic Flow Performance Estimation by Using Gantry Toll Collection Samples: A Deep Learning Method
Rouky et al. A spatiotemporal analysis of traffic congestion patterns using clustering algorithms: A case study of Casablanca
Assi et al. Framework of Big Data and Deep Learning for Simultaneously Solving Space Allocation and Signal Timing Problem
Morton et al. Need a boost? a comparison of traditional commuting models with the xgboost model for predicting commuting flows (short paper)
Ivanchev et al. Determining the most harmful roads in search for system optimal routing