Mendula et al., 2022 - Google Patents
A data-driven digital twin for urban activity monitoringMendula 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 …
- 230000000694 effects 0 title abstract description 21
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
- 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
- G06Q10/0639—Performance analysis
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] 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
- 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
- G06Q10/0631—Resource planning, allocation or scheduling for a business operation
-
- 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
- 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
- G06Q10/04—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
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- 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
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 |