[go: up one dir, main page]

Panahandeh, 2017 - Google Patents

Driver route and destination prediction

Panahandeh, 2017

Document ID
1412901730440283608
Author
Panahandeh G
Publication year
Publication venue
2017 IEEE intelligent vehicles symposium (IV)

External Links

Snippet

A method is proposed for estimating driver's intended route and destination. Probabilistic Bayesian models are employed to analyze the history of driving for individuals, where data attributes are GPS traces captured during trips from fleet of cars. The proposed probabilistic …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • 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
    • 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
    • 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
    • G06Q10/00—Administration; Management
    • 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
    • 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
    • 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

Similar Documents

Publication Publication Date Title
US11378411B2 (en) Method and system for utilizing a trip history to predict a destination
Yang et al. Travel cost inference from sparse, spatio temporally correlated time series using markov models
Chen et al. Mining moving patterns for predicting next location
Wu et al. A fast trajectory outlier detection approach via driving behavior modeling
Xue et al. Destination prediction by sub-trajectory synthesis and privacy protection against such prediction
Al-Molegi et al. STF-RNN: Space time features-based recurrent neural network for predicting people next location
Rinzivillo et al. The purpose of motion: Learning activities from individual mobility networks
Wang et al. Moving destination prediction using sparse dataset: A mobility gradient descent approach
He et al. A joint context-aware embedding for trip recommendations
Hu et al. Crowdsourcing-based real-time urban traffic speed estimation: From trends to speeds
Neto et al. Combining Markov model and prediction by partial matching compression technique for route and destination prediction
Chen et al. Efficient traffic speed forecasting based on massive heterogenous historical data
Jain et al. Neuromlr: Robust & reliable route recommendation on road networks
CN102087722A (en) Learning apparatus and method, prediction apparatus and method, and program
CN102298608A (en) Information processing apparatus, information processing method and program
Guo et al. Transportation mode recognition with deep forest based on GPS data
Pecher et al. Data-driven vehicle trajectory prediction
Panahandeh Driver route and destination prediction
Dai et al. Cluster-based destination prediction in bike sharing system
Amirat et al. Nextroute: a lossless model for accurate mobility prediction
Servizi et al. Mining user behaviour from smartphone data: a literature review
Amirat et al. MyRoute: A Graph-Dependency Based Model for Real-Time Route Prediction.
Mohamed et al. Kalman and Cauchy clustering for anomaly detection based authentication of IoMTs using extreme learning machine
Thu et al. Multi-source data analysis for bike sharing systems
Huang et al. Hierarchical destination prediction based on GPS history