Mo et al., 2022 - Google Patents

Predicting Abnormal Events in Urban Rail Transit Systems with Multivariate Point Process

Mo et al., 2022

Document ID
18345217470113808014
Author
Mo X
Li M
Li M
Publication year
Publication venue
Pacific-Asia Conference on Knowledge Discovery and Data Mining

External Links

Snippet

Abnormal events in rail systems, including train service delays and disruptions, are pains of the public transit system that have plagued urban cities for many years. The prediction of when and where an abnormal event may occur, can benefit train service providers for taking …
Continue reading at link.springer.com (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
    • 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
    • G06F17/30864—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems
    • G06F17/30867—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems with filtering and personalisation
    • 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
    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • 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
    • 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
    • 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
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled

Similar Documents

Publication Publication Date Title
Neelakandan et al. RETRACTED ARTICLE: An automated exploring and learning model for data prediction using balanced CA-SVM
Deng et al. Dynamic knowledge graph based multi-event forecasting
Li et al. A survey on explainable anomaly detection
Amiri et al. Peeking inside the black-box: Explainable machine learning applied to household transportation energy consumption
Sun et al. Short‐term traffic forecasting using self‐adjusting k‐nearest neighbours
Fanaee-T et al. Event labeling combining ensemble detectors and background knowledge
Yu et al. Probabilistic prediction of bus headway using relevance vector machine regression
US20180307986A1 (en) Two-phase distributed neural network training system
Ke et al. PCA-based missing information imputation for real-time crash likelihood prediction under imbalanced data
Nguyen et al. Automatic classification of traffic incident's severity using machine learning approaches
Tuke et al. Pachinko Prediction: A Bayesian method for event prediction from social media data
Asgari et al. Spatiotemporal data partitioning for distributed random forest algorithm: Air quality prediction using imbalanced big spatiotemporal data on spark distributed framework
Yang et al. A hybrid method for short-term freeway travel time prediction based on wavelet neural network and Markov chain
US11055631B2 (en) Automated meta parameter search for invariant based anomaly detectors in log analytics
Meoni et al. Dataset popularity prediction for caching of CMS big data
Prathap et al. Crime analysis and forecasting on spatio temporal news feed data—an indian context
Ye et al. Hydrologic time series anomaly detection based on flink
Javaid et al. Coal mining accident causes classification using voting-based hybrid classifier (VHC)
Wali et al. Anomaly Detection in Fog Computing: State-of-the-Art Techniques, applications, Challenges, and Future Directions
Mo et al. Predicting Abnormal Events in Urban Rail Transit Systems with Multivariate Point Process
Liu et al. Air Pollutant Concentration Forecasting Using Long Short‐Term Memory Based on Wavelet Transform and Information Gain: A Case Study of Beijing
Sarkar et al. Classification and pattern extraction of incidents: A deep learning-based approach
Giri et al. Automated and Interpretable Fake News Detection With Explainable Artificial Intelligence
Giusti et al. Analyzing flight delay prediction under concept drift
Shatnawi et al. Big data analytics tools and applications: survey