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

Mendula et al., 2022 - Google Patents

A data-driven digital twin for urban activity monitoring

Mendula et al., 2022

View PDF
Document ID
6553575317769257857
Author
Mendula M
Bujari A
Foschini L
Bellavista P
Publication year
Publication venue
2022 IEEE Symposium on Computers and Communications (ISCC)

External Links

Snippet

The increasing pace of sensing and communication technology rollout is paving the way for concrete deployments of smart city applications, enabling a data-driven modeling of processes and the environment. In particular, the Urban Facility Management (UFM) process …
Continue reading at www.academia.edu (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • G06Q10/0639Performance analysis
    • G06Q10/06393Score-carding, benchmarking or key performance indicator [KPI] analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • G06Q10/0631Resource planning, allocation or scheduling for a business operation
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled
    • G08G1/0104Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125Traffic data processing
    • G08G1/0129Traffic data processing for creating historical data or processing based on historical data
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30386Retrieval requests
    • G06F17/30424Query processing
    • G06F17/30533Other types of queries
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q10/00Administration; Management
    • G06Q10/04Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition

Similar Documents

Publication Publication Date Title
Yu et al. Prediction of bus travel time using random forests based on near neighbors
Nocera et al. Assessing carbon emissions from road transport through traffic flow estimators
Duan et al. FDSA-STG: Fully dynamic self-attention spatio-temporal graph networks for intelligent traffic flow prediction
JP2015125775A (en) System and method for multi-task learning system for prediction of demand on system
Nesmachnow et al. A distributed platform for big data analysis in smart cities: combining intelligent transportation systems and socioeconomic data for Montevideo, Uruguay
Alshawish et al. Big data applications in smart cities
Mendula et al. A data-driven digital twin for urban activity monitoring
Wang et al. Discovering urban mobility patterns with PageRank based traffic modeling and prediction
Gilardi et al. A nonseparable first-order spatiotemporal intensity for events on linear networks: An application to ambulance interventions
Magalhaes et al. Speed prediction in large and dynamic traffic sensor networks
Cesario et al. Towards a cloud-based framework for urban computing, the trajectory analysis case
Zhu et al. Early identification of recurrent congestion in heterogeneous urban traffic
Bellini et al. Vehicular traffic flow reconstruction analysis to mitigate scenarios with large city changes
Haworth Spatio-temporal forecasting of network data
Pang et al. Dynamic train dwell time forecasting: a hybrid approach to address the influence of passenger flow fluctuations
Sathiyaraj et al. A genetic predictive model approach for smart traffic prediction and congestion avoidance for urban transportation
Darapaneni et al. Demand and revenue forecasting through machine learning
Yu et al. Combining travel behavior in metro passenger flow prediction: A smart explainable Stacking-Catboost algorithm
Shao et al. Estimation of urban travel time with sparse traffic surveillance data
Rasaizadi et al. The ensemble learning process for short-term prediction of traffic state on rural roads
Mashhadi et al. Evaluating Mobility Impacts Of Construction Workzones On Utah Transportation System Using Machine Learning Techniques
Maktoubian et al. Analyzing large-scale smart card data to investigate public transport travel behaviour using big data analytics
Li et al. Comparison of short-term traffic demand prediction methods for transport services
Dong Mining heterogeneous spatial-temporal data with graph neural network to support smart city management
Roy et al. Predicting Taxi Travel Time Using Machine Learning Techniques Considering Weekend and Holidays