[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Xiong et al., 2013 - Google Patents

A descriptive bayesian approach to modeling and calibrating drivers' en route diversion behavior

Xiong et al., 2013

View PDF
Document ID
15047668337997081376
Author
Xiong C
Zhang L
Publication year
Publication venue
IEEE Transactions on Intelligent Transportation Systems

External Links

Snippet

This paper presents a Bayesian approach for modeling and calibrating drivers' en route route changing decision with behavior data collected from laboratory driving simulators and field Bluetooth detectors. The behavior models are not based on assumptions of perfect …
Continue reading at www.researchgate.net (PDF) (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
    • 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
    • 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
    • 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
    • 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
    • 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
    • G08G1/0125—Traffic data processing
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
    • 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
    • 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
    • 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
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks

Similar Documents

Publication Publication Date Title
Guo et al. GPS-based citywide traffic congestion forecasting using CNN-RNN and C3D hybrid model
Xing et al. Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights
Xiong et al. A descriptive bayesian approach to modeling and calibrating drivers' en route diversion behavior
Gao et al. Predicting the spatiotemporal legality of on-street parking using open data and machine learning
Huang et al. Physics-informed deep learning for traffic state estimation: Illustrations with LWR and CTM models
Tang et al. Travel time estimation using freeway point detector data based on evolving fuzzy neural inference system
Lu et al. Lane-level traffic speed forecasting: A novel mixed deep learning model
Wang et al. Efficient traffic estimation with multi-sourced data by parallel coupled hidden markov model
CN114897444A (en) Method and system for identifying service facility requirements in urban subarea
Liu et al. A Gaussian-process-based data-driven traffic flow model and its application in road capacity analysis
Yuan et al. Traffic state classification and prediction based on trajectory data
Guo et al. A novel methodology for prediction of spatial-temporal activities using latent features
Yang et al. Short-term prediction of airway congestion index using machine learning methods
Kabir et al. Time-Series fuel consumption prediction assessing delay impacts on energy using vehicular trajectory
Seo et al. LoRa based architecture for smart town traffic management system
Nguyen et al. DFROUTER—Estimation of vehicle routes from cross-section measurements
Kamel et al. Transportation big data simulation platform for the greater toronto area (GTA)
Heshami et al. A stochastic microscopic based freeway traffic state and spatial-temporal pattern prediction in a connected vehicle environment
Rong et al. Origin-Destination Network Generation via Gravity-Guided GAN
Zhao et al. Exploring the impact of trip patterns on spatially aggregated crashes using floating vehicle trajectory data and graph Convolutional Networks
Xia et al. Deeprailway: a deep learning system for forecasting railway traffic
Sun et al. Alleviating data sparsity problems in estimated time of arrival via auxiliary metric learning
Mohammadi et al. Probabilistic prediction of trip travel time and its variability using hierarchical Bayesian learning
Kong et al. Using an Interpretable Machine Learning Framework to Understand the Relationship of Mobility and Reliability Indices on Truck Drivers’ Route Choices
Afandizadeh et al. Prediction of traffic flow based on calendar data on suburban roads (case study: chalus road)